Soil Biological Health Monitoring Final Design Report
Sponsor: The Ohio State University Department of Food, Agricultural, and Biological Engineering
FABE 4910 – Spring 2022
Team Members: Gabe Aggrey, Kaylee Sabo, Alyssa Bowles, and Madeline Prenger
Technical Advisor: Dr. Darren Drewry
Course Advisor: Ambria Small
April 25th, 2022
Department of Food, Agricultural, and Biological Engineering
590 Woody Hayes Drive
Columbus, OH 43210
Dear Dr. Darren Drewry,
Attached is the Soil Biological Health Monitoring Device Project Final Report Project, which details design options, design analysis, testing and results, cost analysis, and recommendations for future project work. After months of research, prototyping, and testing, the team has made the following major conclusions regarding design:
- Concept 2, which includes the Arduino MKR GSM 1400, SCD30, and NGM2611-E13, should be selected for optimal function of the monitoring device.
- A 5-gallon housing size should be used in the final design and for in-field device placement.
- Overall, this device is a cheap and viable option for farmers to evaluate their soil biological health.
We appreciate your guidance and support throughout the duration of this project and would like to thank you for your involvement. We would also like to acknowledge our course advisors Ambria Small and Dr. Jane Fife for their time and assistance over the past few months. If you have any further questions about the information in this report, please feel free to contact any member of our team.
Best,
Gabe Aggrey aggrey.5@osu.edu
Alyssa Bowles bowles.112@osu.edu
Madeline Prenger prenger.59@osu.edu
Kaylee Sabo sabo.168@osu.edu
Table of Contents
Microcontrollers and Microprocessors
Explanation of design variables
Methods used for Data Analysis
Calibration and Reliability Tests
Conclusions and Recommendations
Appendix A: Qualifications of Personnel
Appendix B: Results from Sensor Calibration & Reliability Testing
Appendix B.1: Thursday – 3/31 – 8 am Sensor Data
Appendix B.2: Thursday – 3/31 – 1 pm Sensor Data
Appendix B.3: Friday – 4/1 – 8 am Sensor Data
Appendix B.4: Friday – 4/1 – 1 pm Sensor Data
Appendix B.5: Gas Chromatograph Data
Appendix B.6: Gas Chromatograph Data Averaged
Appendix B.7: Gas Chromatograph and Sensor Carbon Dioxide Graphs
Appendix B.8: Carbon Dioxide ANOVA Tables
Appendix B.9: Untreated Methane Graphs
Appendix B.10: Methane Untreated Comparison Graphs
Appendix B.11: Sensor Methane Output Versus Gas Chromatograph Trend
Appendix B.12: Calculated Methane Concentration
Appendix B.13: Calibrated Methane Graphs
Appendix B.14: Calibrated Methane Comparison Graphs
Appendix B.15: Methane ANOVA Table
Appendix C: Results from Housing Testing
Appendix C.1: Concept 1 1-Gallon Housing Data
Appendix C.2: Concept 2 1-Gallon Housing Data
Appendix C.3: Concept 1 2-Gallon Housing Data
Appendix C.4: Concept 2 2-Gallon Housing Data
Appendix C.5: Concept 1 5-Gallon Housing Data
Appendix C.6: Concept 2 5-Gallon Housing Data
Appendix C.7: Housing Test Carbon Dioxide Reading Comparison Graphs
Appendix C.8: Housing Test Temperature Reading Comparison Graphs
Appendix C.9: Housing Test Relative Humidity Reading Comparison Graphs
Appendix C.8: Housing Test Methane Comparison Graphs
Appendix C.9: 1-Gallon ANOVA Table
Appendix C.10: 2-Gallon ANOVA Table
Appendix C.11: 5-Gallon ANOVA Table
Appendix D: Specification Sheets
Appendix D.1: SCD30 Documentation
Appendix D.2: K30 Documentation
Appendix D.3: DHT22 Documentation
Appendix D.4: NGM2611 – E13 Documentation
Appendix D.5: Arduino MKR GSM 1400 Documentation
Appendix E: Desktop Sensor Code
Appendix G.1: Concept 1 Wiring Diagram
Appendix G.2: Concept 2 Wiring Diagram
Appendix G.3: SCD30 Individual Wiring Diagram
Appendix G.4: K30 Individual Wiring Diagram
Appendix G.5: DHT22 Individual Wiring Diagram
Appendix G.6: NGM2611 – E13 Individual Wiring Diagram
Appendix H: Sensor Library Directions
Appendix H.2: Needed Libraries for SCD30
Appendix H.3 Needed Libraries for DHT22
List of Figures
Figure 1: Diagram of carbon cycle in the soil (Cotrufo, Francesca, and Jocelyn Lavallee)
Figure 2: Colorimetric results of Solvita test strips (Shedekar, Vinayak)
Figure 3: Diagram of gas Chromatograph (Turner, Diane)
Figure 4: Internal Process of NDIR Sensor (CO2 Meter, 2021).
Figure 5: Electrochemical Sensor in Carbon Monoxide (Figaro, 2021).
Figure 6: Semiconducting Metal Oxide Sensors (Sureshkumar and Dutta, 2020).
Figure 7: Calorimetric Sensor Diagram (Transducer Sensors, 2018).
Figure 8: Diagram of How the IoT Cloud Works.
Figure 9: Arduino MKR-GSM 1400 (Arduino, 2021).
Figure 10: Left: NGM2611-E13 Sensor (Figaro, 2014), Right: MQ4 Sensor (Winsen, 2018)
Figure 11: Left:SCD30 Sensor (Adafruit learning system, 2021), Right: K30 Sensor (CO2 Meter, 2015)
Figure 12: DHT22 Temperature & Humidity Sensor (Adafruit learning system, 2012).
Figure 13: 1-Gallon, 2-Gallon, & 5-Gallon Housing Containers
Figure 14: Isometric sketch of initial design, not to scale.
Figure 15: Conceptual design for scope of project
Figure 16: Concept 1 Wiring Diagram
Figure 17: Concept 2 Wiring Diagram
Figure 18: Sensor Inside Chamber (left), Chamber Setup (right)
Figure 19: CO2 Concentration Measured by Device for Reliability & Calibration Tests
Figure 20: Comparison Graph of Device and Gas Chromatograph CO2 Readings
Figure 21: Methane in PPM Detected by Device and Gas Chromatograph
Figure 22: CO2 Concentrations Detected by Concepts in Different Types of Housings
Figure 23: Relative Humidity Detected by Concepts in Different Types of Housings
Figure 24: Temperature Detected by Concepts in Different Types of Housings
Figure 25: Methane Reading from Sensors During Housing Tests
List of Tables
Table 1: Initial Sensors Combinations Concepts
Table 4: CO2 Measured by Device & Gas Chromatograph for Calibration & Reliability Tests in PPM
Table 5: CH4 Measured by Device & Gas Chromatograph for Calibration & Reliability Tests in PPM
Executive Summary
Soil biological health is an important consideration for farmers due to the impact of soil health on plant growth. To check soil biological health, soil samples can be sent to laboratories where the flux of carbon dioxide (CO2) can be measured to determine the health of the soil. This process takes time and may not give a farmer enough time to adjust their soil plan before planting, fertilizing, or harvesting crops. The need for quick and efficient greenhouse gas emission monitoring is growing due to farmers’ desire to understand their soil’s biological health and participation in carbon credit programs. Carbon credit programs are incentive programs where farmers, growers, and other people can earn credits by using better land management methods, such as no till, to reduce their fields’ carbon output.
The team set out to design a device that measures greenhouse gas emissions to address these issues. The development of this project design also addressed future user needs such as the affordability of the device, and the transmission of data directly to the user through Bluetooth communication.
Through research, the team decided to test an Arduino Microcontroller with Global System for Mobiles or GSM capabilities and two different combinations of sensors for methane, CO2, temperature, and humidity along with three different size housing containers. The Arduino Microcontroller was chosen due to the GSM capabilities, the code being open source, and wiring diagrams and tutorials being readily available. The two CO2 sensors tested were the SCD30 and K30 sensors. For concept 1, the DHT22 temperature and humidity sensor was used, which has a digital output. Since the SCD30 sensor has a built-in temperature and humidity sensor, no additional sensor was necessary for concept 2. Both concepts used the same methane sensor, the NGM26-11E13, which has an analog output. These sensors were all chosen due to being readily available, affordable, and able to measure within the range of what ambient soil produces.
The team’s testing and evaluation of the concepts has shown that the sensors will work for this project. The data has been able to be retrieved through the Arduino IoT cloud and sent to a phone with a SIM card. The accuracy and precision of the sensors was evaluated by testing gas samples via a gas chromatograph in a greenhouse setting and comparing it to the sensor data. Using the gas chromatograph data, an equation was derived to convert the analog output of millivolts to ppm for the methane sensor. And ANOVA tests of the results showed that there was not a significant difference in the measurements from the device compared to the gas chromatograph.
Recommendations for the project in the future include the addition of an external power supply and testing of different communication techniques and software. More in-depth housing tests are also recommended alongside soldering the wires to the device for stability. Additionally, the device should be tested in the field, in different environments, and long term. This device is feasible for monitoring greenhouse gas fluxes to help the client participate in carbon credit programs and monitor their soil’s biological health quickly, accurately, and affordably.
Introduction
According to the United States Environmental Protection Agency, agricultural sources are estimated to emit 10% of United States greenhouse gas emissions (EPA). Farmers and growers have an increasing need to monitor greenhouse gas emissions to help them understand soil biological health and participate in carbon emissions reduction incentives.
To monitor the greenhouse gas emissions of the soil, soil samples can be sent to a lab for an accurate assessment. The specific lab can be selected by the farmer based on proximity, cost, and time considerations. According to Rutgers lab, a Soil CO2 Burst test, which uses microbial respiration as an indicator of soil biological health, costs $23 per test (“Services and Fees.”). The turnaround time for samples sent to labs varies based on the number of tests and staffing in the lab. For the soil testing lab at Rutgers, the results will take 1-2 weeks if sent electronically via email, or potentially longer if sent by mail (“Frequently Asked Questions.”). Due to the long turnaround time for results, the field conditions likely will have changed since the sample was taken and the results will then be outdated.
Greenhouse gas reduction incentives are either government or independently funded incentives for reducing CO2 emissions through implementation of better agricultural management practices. Since there is an increasing need for CO2 reduction, scientists have been analyzing ways to remove CO2 from the atmosphere as well as how to reduce emissions entering the atmosphere. Programs like Agoro Carbon Alliance incentivize specific practices such as tillage management, cover crops, and nitrogen efficient practices (Agoro Carbon Alliance). By allowing farmers to monitor their carbon emissions, the specific effects of different management practices can be evaluated and tracked for proof of CO2 reduction.
The sponsor for this project, The Ohio State Food, Agricultural, and Biological Engineering Department, wants to develop a device that would be able to monitor the soil on the field and give the farmer or grower the data more quickly. The device needs to be reliable and accurate at monitoring the greenhouse gases that are being emitted so farmers can react to the data from their soil and change management practices immediately in the field if needed. With this data, the farmer will be able to prove greenhouse gas emission reduction after implementation of a new practice for greenhouse gas emission reduction incentives.
This project will be focused on a smaller scope of this overall goal. This scope will address the transmission of accurate and reliable greenhouse gas emission data, without finalizing the infield components of the design. The team will be investigating the different sensors for gas emission collections, the size of the housing to contain the device, and the method of transmitting this data back to the user. Recommendations for future testing and development of this device beyond the project scope were included. It will be necessary to address these future recommendations to implement this design in the field.
Background
Soil biological health plays a role in how crops and other plants grow but components like pH, nitrogen, and phosphorus are not easily monitored by farmers and growers. Soil biological health can be monitored by proxy by monitoring the greenhouse gas flux from the soil. These greenhouse gases include CO2, nitrous oxide, and methane. Greenhouse gases cause the greenhouse effect, where the gases trap the Sun’s heat and stop it from being released into space, which causes the Earth’s atmosphere to heat up resulting in global warming (European Commission, 2021).
It is important to monitor greenhouse gas emissions to understand the biological health of the soil and how this impacts the atmosphere. CO2 from the atmosphere can be absorbed by plants in the process of photosynthesis and converted to organic carbon within the soil. However, when microorganisms break down plant residue and other organic matter within the soil, they can convert the organic carbon to CO2 released back into the atmosphere. If a greater amount of carbon can be stored within soil carbon pools, a greater amount of CO2 can be removed from the atmosphere. The soil carbon pools can be increased with an increase in organic matter. Soil organic matter is made up of living organic matter such as soil fauna and microbes like bacteria and ‘dead’ organic matter that is decaying (Magdoff, 2021). Humus is ‘dead’ organic matter that is resistant to decomposition and is where most of soil carbon is stored (TERC, 2021). Figure 1 shows the carbon soil cycle.
Figure 1: Diagram of carbon cycle in the soil (Cotrufo, Francesca, and Jocelyn Lavallee)
Agricultural management practices can help to increase the amount of soil organic matter. Cover crops are chosen by the farmer to plant during off season to prevent the field from remaining bare and restore nutrients, such as carbon, phosphorus, potassium, sulfur, and nitrogen to the soil. Other agricultural practices such as tilling can also influence the amount of soil organic matter, release of gas emissions, and health of the soil. Practicing no tillage and utilizing diverse cover crops helps to enhance carbon pools, increase crop residue, and therefore microbial activity which is essential to nutrient cycling within the soil (Alhameid, 2018).
Methane is not directly correlated to soil biological health like CO2 but is a large contributor to global warming and atmospheric chemistry (Koschorreck, and Ralk, 1993). Monitoring methane in soil can help to better understand the process of methane emissions and help to lower the amount of greenhouse gases released to the atmosphere from the soil.
If a farmer can monitor greenhouse gas emissions quickly, they can apply the data to the way they manage their fields during and after the growing season. Current methods are time consuming and can only give the farmers an idea of soil biological health at the time of data collection. This data may no longer apply to their field if they have applied fertilizers or if the crops are in a different stage of growing.
The amount of CO2 and methane fluctuates based on day, season, and region which correlate back to different temperatures and different biological organisms. Since biological organisms are less active in colder temperatures, if a gas sample is only taken during a cold morning it may not give accurate estimation of how active the biological health of the soil is. This means there is a need for continuous monitoring even if it just to average the data over the day.
In Field Soil Sampling
When monitoring soil health, farmers can perform tests on their own. These tests can vary in method, timing of results, price, and accuracy. In field soil sampling can measure emissions via both quantitative and qualitative indicators.
Quantitative measurements can be obtained by installing sensor devices within the field and yield more accurate results than qualitative sampling. Many soil sensors that are available commercially monitor soil moisture, temperature, salinity, and type (“Sensors”). Typically, devices that monitor gaseous emissions are more expensive and used for research purposes. The price, timing of results, and method of obtaining results for qualitative measurements will vary based on the device.
Qualitative measurements can include soil health cards/indicator assessments and colorimetric tests such as Solvita®. Soil health cards, and indicator assessments are used to assess the current soil quality by having the user score each category based on what is seen. But it requires farmers to have a detailed knowledge and experience with local natural resources. It allows farmers and other land managers to monitor the health of their soil at their leisure and compare results following implementation of different management practices (“Natural Resources Conservation Service”). These tests are low cost and provide results quickly, however the accuracy is dependent on the test user and area of land they are monitoring.
Solvita®
The Solvita® Basic Field CO2 Test and a drone were used as a part of a 2020-2022 FABE capstone team’s proof-of-concept design. The Solvita® Basic Field CO2 Test consists of a colorimetric indicator strip that tells the range of CO2 in the soil sample, shown in Figure 2. This strip is placed in a sealed container with the soil sample for 24 hours and then analyzed based on the color on the strip.
Figure 2: Colorimetric results of Solvita test strips (Shedekar, Vinayak)
Their design involved placing the Solvita® Basic Field CO2 Test strip into the field with a cap over it, waiting 24 hours, and the sending a drone out to take an image of the Solvita® Basic Field CO2 Test. When using the Solvita® Basic Field CO2 Test this way, the team did not see correlating results to how the Solvita® Basic Field CO2 Test is supposed to be used. This means that the results may not be accurate. This design also takes time to collect the data and requires a grower to be able to fly a drone and take an image with it.
Outsourcing Soil Sampling
Many farmers rely on sending their soil samples to a lab for analysis due to the cost and potential labor involved with in-field sampling. The results of lab tests will be dependent on how representative the sample sent is of the whole field. It is recommended to send a soil test for every 20 acres of land. These tests should be conducted annually or every 2 to 3 years (“How to Take a Soil Sample”). Costs for sending samples to a lab will vary depending on the location and accessibility of soil labs. According to Rutgers, a soil CO2 burst test where a soil sample is sent to a lab to measure the microbial respiration as a biological indicator of soil health costs $23 per test (“Services and Fees”). These tests can take up to 12 days to obtain results, not including the time to send samples to the lab or to send results to the farmer. Within this range of time, soil biological health conditions could change substantially depending on time of year, crops being grown, and land management practices.
Gas Chromatography
A Gas Chromatograph, shown in Figure 3, is a device that is utilized to separate the components of a mixture and determine the amount of each component that is present in the sample. A gas sample from a sealed environment is collected in a syringe that is then transferred to a vacuum sealed tube. The sample is injected into the gas chromatograph along with an inert carrier gas that does not react with the mixture. The sample and carrier gas are sent through a long, coiled tube after heating where the components are separated. Then a detector at the end of the tube records the amount of sample that reaches it. A chromatogram is then generated to show the signals from the detector and the time it took the sample to reach it. The peaks displayed on the chromatogram are representative of the amount of each component from the sample. From this graph, the concentrations of gas at the time of measuring can be obtained (Helmenstine, Anne Marie).
Figure 3: Diagram of gas Chromatograph (Turner, Diane)
Sensors Technologies
Over the past 20 years, there have been major advancements in sensor technology. There are four main types of sensors used for taking gas measurements: nondispersive infrared (NDIR) sensors, electrochemical sensors, semiconducting metal oxide sensors, and calorimetric sensors (Smith, 2019). Each of these sensor types can be connected to microcontrollers, such as an Arduino or Raspberry Pi (Blackstock, Covington, Perne, and Myre, 2019). These sensors can be used for continuous monitoring if the sensor is kept free from film and other debris.
Non-dispersive infrared (NDIR) sensors are a type of infrared gas analyzer (IRGA). In NDIR sensors, there is an infrared lamp that shines through a tube with an infrared light detector at the other end. The gas molecules then block light, with a certain wavelength, from reaching the detector. The detector can determine how much of the gas is in the air from the amount of infrared in a certain wavelength not passing through it. This process has been done since the 1870s, but the devices are now much smaller and more reliable (CO2 Meter, 2021). Figure 4 shows a schematic of the internal process of an NDIR sensor. The K30 CO2 sensor and the SCD30 CO2 sensor are both NDIR sensors.
Figure 4: Internal Process of NDIR Sensor (CO2 Meter, 2021).
In electrochemical sensors, the gas being detected runs through an external circuit (Analytical technology, 2019). In this circuit, an oxidation or reduction reaction happens and creates a positive or negative flow that then goes through the other circuit and can be measured. Figure 5 shows an example of how an electrochemical sensor works with carbon monoxide. For CO2 the reaction is:
(Sadaoka,1993)
According to Smith (2019), these sensors tend to have a cross sensitivity bias where other compounds interfere with the reaction and long-term accuracy but work well in a broad range of temperatures and humidity. Examples include the MG811 CO2 sensor and TGS4160 CO2 sensor.
Figure 5: Electrochemical Sensor in Carbon Monoxide (Figaro, 2021).
Semiconducting metal oxide sensors absorb gas molecules which causes a change in electrical resistance, so concentration of the gas can be determined. Metal oxide sensors have a low power consumption and cost. They are typically used to detect large concentrations of dangerous gases and are less accurate for measuring low concentrations (Pickering, Tewari, and Twanow, 2018). Figure 6 shows how a metal oxide sensor works. The NGM2611-E13 and the MQ4 methane sensors are both semiconducting tin dioxide sensors.
Figure 6: Semiconducting Metal Oxide Sensors (Sureshkumar and Dutta, 2020).
Calorimetric sensors (Figure 7) are known as thermal conductivity gas sensors and use heat produced by a reaction to determine how much of a gas is in the air. They correlate the heat produced to a reactant concentration. Gas Chromatographs use this type of sensing on a larger scale, but smaller, portable calorimetric sensors have been made in recent years (Transducer Sensors, 2018).
Figure 7: Calorimetric Sensor Diagram (Transducer Sensors, 2018).
The team researched these types of sensors to determine what types of sensors were available, reliable, inexpensive, and otherwise suitable for the project. The sensor types the team determined would work best for this project were the semiconductor metal oxide sensor type for methane and the NDIR type sensor for CO2. Both methane and CO2 have electrochemical sensors and calorimetric sensors available, but they are expensive, have a short lifetime, and do not measure in ppm, so they would not be suitable for this project.
Microcontrollers and Microprocessors
There are many types of microcontrollers and microprocessors that can be used to communicate with sensors and collect and read the data the sensors output. The two main popular brands are Raspberry Pi and Arduino.
Raspberry Pi is a microprocessor and can act as a computer. It can run an operating system and is Linux based. It is sometimes referred to as a Single Board Computer (SBC) due to sitting on one printed circuit board. Code for a Raspberry Pi microprocessor can be written in C, C++, Python, Java, and others since it is a computer by itself. Raspberry Pi is not open source and design files can cost money (Teja, Ravi).
Arduinos are microcontrollers and need to be hooked to a battery source or computer. Arduinos use a single software, the Arduino IDE, where code can be written, complied, and uploaded to the microcontroller. The code is written in C++ which is a programming language that is readable by humans. The code is available in open-sources and files, so source code for all software and libraries is free and easy to find.
Due to the accessibility and connectivity of Arduino, further research was conducted on the different Arduino Boards. Arduino has a range of options for microcontrollers that all meet distinctive design requirements. For this project, the team focused research on the Arduino Uno, Arduino Leonardo, Arduino Ethernet Rev-3, and Arduino MKR GSM 1400.
The Arduino Uno is a cheaper microcontroller, $12-$25, that is known for its user-friendly capabilities. It also has good flexibility and integration capabilities that allow the projects to be modified using LEDs, servo motors, LCD screens, sensors, valves, etc (Long, Moe).
The Arduino Leonardo is also low-cost, $20, and good for projects that are utilizing USB communication due to the built in USB communication aspect so that the device does not require a secondary processor (“ArduinoBoardLeonardo”). However, this project will require a Bluetooth or cellular communication aspect to send data directly to the user. Both the Arduino Ethernet Rev-3 and Arduino MKR GSM 1400 will allow for data to be sent to an IoT cloud that can be accessed on a computer, tablet, or phone from a distance from the device.
The Arduino Ethernet Rev-3 is higher in price, $92, and has an ethernet connection rather than an onboard USB to serial driver chip and an ability to read micro-SD cards that can be stored and accessed from an SD library (“Arduino Ethernet REV3 with Poe: Arduino Documentation”).
The Arduino MKR GSM 1400 utilizes cellular connection from an external antenna to communicate data, so it requires purchasing a SIM card in addition to the microcontroller. Any SIM card can be purchased and will impact the IoT cloud that is used for the project. To ensure the best compatibility, the device should be paired with the Arduino SIM card. Combined, the microcontroller and SIM card cellular kit costs $71 (Arduino).
Communication Technologies
The Meter Group is a leading brand in remote environmental sensing for the agriculture industry. They make products to monitor temperature, humidity, moisture, weather conditions and much more. They also have the unique capability of transmitting real time data and information to an online access point. This is desirable for researchers and growers, so they do not have to go out to the field to take a sample or a reading but instead can access the information from anywhere.
Network capabilities have greatly expanded over the past 100 years and farmers are just beginning to tap into the unlimited potential. Some of the possible network connections include Global System for Mobile (GSM), Wi-Fi, Bluetooth, and many more. Each of these networks connects have advantages and disadvantages. GSM has very widespread capabilities using cellular antennas, it can be accessed from almost anywhere, and is relativity low cost. Some disadvantages include that network speeds can be slow, and it is required to have a data plan (Advantages of GSM | Disadvantages of GSM, 2012).
Wi-Fi is desirable since after deployment, it is convenient, easily expandable, and has a relatively low cost. Some disadvantages include the range of connection, it is necessary to be within 100 feet of the access point, and routers only allow maximum of 30 devices to be connected at once (Roomi, 2020). Finally, Bluetooth has low power consumption, avoids interference from other wireless devices, and no cost once the Bluetooth software is set up. A few disadvantages include low bandwidth compared to Wi-fi and the short range, less than 100 feet, for optimal use (Advantages and Disadvantages of Bluetooth – Polytechnic Hub, 2017).
To access the information remotely, there are a few platforms where the data is transmitted, using cellular networks, that be accessed if you have a network connection (cellular or Wi-Fi). These can be described as the Internet of Things or IoT (Figure 8). Some of these programs that utilize IoT include Google Cloud IoT, ThinkSpeak Cloud, and Arduino Cloud IoT. The Google Cloud IoT service uses C++ incorporated system with the capabilities of computing and analyzing data in real time between innovative devices. ThinkSpeak IoT analytics platform is incorporated with MathWorks’ MATLAB and Simulink software and specializes in environmental monitoring of data. The Arduino IoT Cloud features data monitoring, variable synchronization, and wireless uploads which gives the design the ability to upload data without the use of a directly connected computer. It is also most compatible with other Arduino products.
Figure 8: Diagram of How the IoT Cloud Works.
Detailed Design Description
Proposed designs
The team decided to utilize a microcontroller, housing, and sensor combination to measure the CO2 and methane released from the soil.
The base design consists of an Arduino MKR GSM 1400 microcontroller, which has Global System for Mobiles (GSM) communication capabilities, shown in Figure 9. This microcontroller has free open-source code available and is compatible with all the sensor types. The team also used an Arduino Leonardo to test code and run both concepts concurrently during testing. The Arduino Leonardo does not have GSM communication capabilities so it must be plugged into the USB port of a computer to retrieve data.
For the proposed design, the device’s communication aspect used the network capabilities of the Arduino MKR GSM 1400. The proper network was selected to provide data remotely to devices such as cellular phones and laptops. The team researched the best software to implement data transmission and investigated programs such as Google Cloud IoT, ThinkSpeak Cloud, and Arduino Cloud IoT. However, due to the Arduino device compatibility with the Arduino Cloud IoT, this service was selected over the other options.
Figure 9: Arduino MKR-GSM 1400 (Arduino, 2021).
The team considered two methane sensors, the NGM2611-E13 from Figaro and the MQ4 sensor (Figure 10). These sensors are flammable gas sensors, but their major component is methane detection. The MQ4 can detect methane within a range of 300 – 10,000 parts per million (ppm) and the NGM2611-E13 can detect methane within the range of 1 to 10,000 ppm. Both are semiconductor metal oxide sensors that output an analog reading and need to be calibrated for temperature, humidity, and to output data in ppm rather than millivolts.
Figure 10: Left: NGM2611-E13 Sensor (Figaro, 2014), Right: MQ4 Sensor (Winsen, 2018)
The two CO2 sensors that the team considered were the SC30 and the K30 sensors (Figure 11). Both sensors were NDIR sensors that communicated data via the I2C bus and measure CO2 concentration in ppm. While the SCD30 and K30 are similar, the SCD30 can monitor more aspects of the sample, since it contains a temperature and relative humidity sensor. Both sensors can measure 0 to 10,000 ppm of CO2. They also have open-sourced code and wiring diagrams available online. These resources allowed for easier connection to an Arduino microcontroller.
Figure 11: Left:SCD30 Sensor (Adafruit learning system, 2021), Right: K30 Sensor (CO2 Meter, 2015).
Each of these CO2 and methane sensors were paired with each other to be evaluated before testing. Table 1 shows the sensor combinations that comprise each concept.
Table 1: Initial Sensors Combinations Concepts
| Concept 1 | Concept 2 | Concept 3 | Concept 4 | |
| Methane Sensors | NGM-2611E | NGM-2611E | MQ4 | MQ4 |
| Carbon Dioxide Sensor | K30 | SCD30 | K30 | SCD30 |
Concept 1 and Concept 3 need an external temperature and relative humidity sensor since the K30 CO2 sensor does not have one. The DHT22 sensor was chosen since it was low cost and pairs well with Arduino products (Figure 12).
Figure 12: DHT22 Temperature & Humidity Sensor (Adafruit learning system, 2012).
The device must be housed in a container for use in the field to avoid debris and other environmental interferences. So, the team decided to test three different housing sizes to determine what volume of air space is necessary to get reliable readings. These housing components were tested with design concepts 1 and 2, for a total of six concepts tested. Figure 13 shows the housing components utilized which included a gallon milk jug, a 2-gallon bucket, and a 5-gallon bucket.
Figure 13: 1-Gallon, 2-Gallon, & 5-Gallon Housing Containers
The team sketched a design to encompass a broader scope of the project to gain insight on the end goal for the device (Figure 14). The device design would include the sensors combined on a breadboard and inserted into a waterproof container. These would be placed inside of a housing container that is placed underground, leaving the bottom open for gas emissions to enter the system from the soil.
An aspect of the design for further consideration would be a pole to make the device identifiable within the field and to hold a solar panel to supply power to the device. Once a solar panel or external power supply is incorporated the device will be able to operate independently on a field.
Figure 14: Isometric sketch of initial design, not to scale.
With the scope in mind, the team focused on a conceptual design that would include a containment system, a shelf for the sensors to sit on, and the sensors themselves (Figure 15).
Figure 15: Conceptual design for scope of project
Selected design and rationale
A decision matrix, shown in Table 2, was executed to determine which sensor components would be tested. The analyzed criteria were selected based on the needs of the sponsor. Each of the criterion in the decision matrix were evaluated based on assigned weights. These weights were determined based on the importance of the criteria for the final project design. A scale from 1 to 5 was utilized with 1 being least important and 5 being most important.
| Criteria | Weight | Concept 1 | Concept 2 | Concept 3 | Concept 4 |
| Ability to monitor soil CO2 levels (ppm) | 5 | 0 | 0 | 0 | 0 |
| Ability to monitor soil methane levels (ppm) | 5 | 0 | 0 | -1 | -1 |
| Ability to monitor temp/humidity | 1 | 0 | 1 | 0 | 1 |
| Amount of time to take a measurement (s) | 2 | 0 | 0 | 0 | 0 |
| Amount of data storage space needed (gB) | 2 | 0 | 0 | 0 | 0 |
| Amount of current needed (A) | 3 | 0 | 1 | -1 | -1 |
| Amount of voltage needed (V) | 3 | 0 | -1 | 0 | -1 |
| Water resistance of the sensor | 1 | 0 | 1 | -1 | 0 |
| Number of ports necessary in the Arduino | 5 | 0 | 0 | 0 | 0 |
| Ability to operate in a range of temperatures | 4 | 0 | 0 | -1 | -1 |
| Total | 0 | 2 | -13 | -14 |
Concept 1 was used as a base to compare the other concepts for scoring. So, it was given scores of zero for each criterion. Then assessing the ability of each concept to fulfill the given criteria compared to concept 1, a score of 1 (better), 0 (the same), or -1 (does not fulfill) was applied.
The most important criterion was the ability to monitor the CO2 and methane concentrations within ambient soil levels. Ambient soil levels for CO2 emissions are expected to be in a range of 600 to 8,000 ppm. Both selected CO2 sensors are capable of monitoring emissions in this range. The range for ambient methane emissions is expected to be 0 to 2 ppm. According to the MQ4’s specification sheet, the lowest value for methane monitoring is 300 ppm. So, it could not measure methane at the concentration that would be released from the soil. This meant that Concept 3 and Concept 4 would not work for this project. The remaining concepts both scored similarly within the matrix. So, Concept 1 and Concept 2 were both chosen to move forward into the testing phase.
Wiring diagrams were developed and adjusted with assistance from Christopher Gecik before the actual device was wired to avoid damaging the components. The wires used to wire the concepts were cut and stripped to fit the length needed to go from each breadboard. Then the code for the concepts were developed using open-source code and libraries available for the K30, SCD30 and DHT22 sensors. These were adjusted along with code developed by David Bastviken and his team for the NGM2611-E13 sensor (Bastviken, D., Nygren, J., Schenk, J., Parellada Massana, R., and Duc, N. T.). The finalized codes are shown in Appendix E and F.
The Arduino MKR GSM 1400 microcontroller was placed on a breadboard that was attached to a waterproof box with Velcro strips. Then a hole was drilled along the side of the box to run wires to the breadboards with the sensors. After each concept was wired, the wires were zip-tied together to reduce tangling and unplugging during handling. Once finalized, the hole for the waterproof box will be sealed around the wires to protect the Arduino from debris and other environmental factors.
Concept 1 consisted of the Arduino MKR GSM 1400 microcontroller, the NGM2611-E13 methane sensor, the DHT22 temperature and humidity sensor, a 10K resistor, and the K30 carbon dioxide sensor. Figure 16 shows concept 1 wired to the Arduino.
Figure 16: Concept 1 Wiring Diagram
Concept 2 consisted of the Arduino MKR GSM 1400 microcontroller, the NGM2611-E13 methane sensor, and the SCD30 carbon dioxide sensor temperature and relative humidity. Figure 17 shows concept 2 wired to the Arduino.
Figure 17: Concept 2 Wiring Diagram
For testing, the breadboard with the sensors for each concept and the waterproof box containing the Arduino were placed next to each other inside of the metal ring, shown in Figure 18. The cap was placed on top of each ring to seal the gas sample being tested. Each of these caps had a hole for a thermometer and a ring filled with silicone to insert the syringe for collection. The setup for the sealed rings is shown in Figure 18.
Figure 18: Sensor Inside Chamber (left), Chamber Setup (right)
Explanation of design variables
During the construction of the system, the team came across problems that resulted in changed design variables. The first variable that was improved for this design was the use of both CO2 sensors K30 and SCD30 as testing variables during greenhouse experiments. This was done to understand the accuracy of measurements that these sensors are detecting and compare them to each other in ppm. Following a comparison of each sensor’s measurements with the gas chromatograph’s readings, the CO2 sensor with the greatest precision would be selected for the final design.
The second variable tested was the housing component that is used for protecting the Arduino and Sensors from physical and environmental matters. The main factor tested for this component was the reliability of measurements within the space. Within each housing system, a shelf was assembled for the breadboards with the concepts and Velcro strips were added to attach the box containing the Arduino. A 5-gallon bucket with the device sealed inside was tested. After that, similar tests were conducted with a 1-gallon milk jug and 2-gallon bucket. Both concepts 1 and 2 were placed inside each of these containment systems and ran simultaneously.
The design performance was evaluated using two sets of success metrics, one for evaluating the validity of the sensors and one for the different housing sizes. The first step to evaluating the performance of the device was to have the sensors reading ambient levels of CO2 and methane gas. This was achieved early on in device development. The next step was to use Concept 2 to compare the data collected from the device to gas samples analyzed using gas chromatography. The samples for the gas chromatograph were collected from a sealed, in-soil chamber which also housed the device.
Using this process, the first success metric evaluated was the amount of CO2 gas measured by Concept 2. As seen in Table 3: Success Metrics, the objective was to have an ANOVA test p-value greater than 0.05 when comparing the data. This would mean that Concept 2 was within 5% accuracy of the results from the gas chromatograph analysis. The next success metric evaluated was the amount of methane gas measured by Concept 2. The objective and method of analysis was the same as the CO2 test.
Another objective was to assess the amount of time it takes to take a measurement. It would be successful if Concept 2 was outputting readings within one minute. Next, two additional success metrics to be evaluated are temperature and humidity. The specific value for these would vary with time. The target ranges were found based on the average low to high temperatures and humidity in Ohio, which the device will be exposed to. If the device can monitor in that range, then it is considered acceptable. If all success metrics are met, this would demonstrate that the device sensors are working within specification for this application.
The second set of success metrics was used to evaluate the best housing design. Since there was only one methane sensor for testing, the CO2 results were used to evaluate three housing designs. This data was then compared to the in-soil chamber used for the gas chromatography. Any housing design with an ANOVA test p-value greater than 0.05 when comparing the data will be considered acceptable for this application. Finally, two additional success metrics to be evaluated were temperature and humidity, which will vary with time. If the device can monitor within the target range, then it is considered acceptable.
| Need: | Metric: | Target Value: | Acceptable Value: |
| Sensor Testing | |||
| Amount of CO2 measured | Parts Per Million (ppm) | Varies with time
(600– 8000 ppm) |
ANOVA test p-value >0.05 with Confidence Interval of 95% |
| Amount of methane measured | Parts Per Million (ppm) | Varies with time
(0 – 2.0 ppm) |
ANOVA test p-value >0.05 with Confidence Interval of 95% |
| Temperature measured | Degree Celsius (oC) | Varies with time
(-3 oC – 24 oC) |
Measuring within the range of target value |
| Relative humidity measured | Percent (%) | Varies with time
(50% – 80%) |
Measuring within the range of the target value |
| Amount of time to take a measurement | Time | Less than one minute | 0 to 5 minutes |
| Housing Testing | |||
| Amount of CO2 measured | Parts Per Million (ppm) | Varies with time
(600 ppm – 8000 ppm) |
ANOVA test p-value >0.05 with Confidence Interval of 95% |
| Temperature measured | Degree Celsius (oC) | Varies with time
(-3oC – 24 oC) |
Measures within the range of the target value |
| Relative humidity measured | Percent (%) | Varies with time
(50% – 80%) |
Measures within the range of the target value |
Design Evaluation
Methods
The team set up a tub with organic store-bought soil in a greenhouse space in Kottman Hall. This soil sat for a few days undisturbed before testing began. This environment was chosen due to the warmer temperatures than outside and constant monitoring of the temperature and humidity.
To evaluate Concept 1 and Concept 2, described above, the validity of the CO2 and methane sensors were verified using a calibrated gas chromatograph. The relative humidity and temperature were verified by using the data collected in the greenhouse. The team was able to place the sensors inside the gas chromatograph collection chamber which consisted of a metal ring and top with rubber gasket around it. The team followed the procedure given to them by Dr. Chiavegato and her team to obtain gas samples that would be analyzed by the gas chromatograph. The team inserted 2 metal rings into the soil in the tub and let the tub sit undisturbed for over 24 hours.
After 24 hours, the samples of gas were collected. This was done by putting the tops on the metal rings to seal the container. Then a needle and syringe were used to pull gas samples out of the housing at 0 minutes, 5 minutes, 10 minutes, and 20 minutes after sealing. The sample in the syringe was then transferred to a vacuum sealed vile. Three samples were collected from each of the housings at each time point. This was done in case a vile lost its vacuum seal or broke. If all three samples were viable, the data was averaged for each time point. Samples were collected over two days, at 8 am and 1 pm on both days.
To communicate data independently, data was transmitted from the device using the Arduino IoT cloud. A profile was created on Arduino IoT website, a new ‘Thing’ was created, and the desired variables were named and linked. A new sketch was created, and the code was developed to fit the needs of the sensors. A dashboard was created to view the output of the sensor data. However, this was unsuccessful since the cellular connection was not reliable enough and would lose connection frequently. Instead, data was collected using the serial monitor on the Arduino IDE and then exported to excel to be analyzed.
For the housing tests, the same tote of soil was used. The three housing sizes were tested separately but sequentially. The team installed Velcro in each housing container to attach the device, so it was kept free of debris. Both concept 1 and concept 2 were then put into the container together and the container was set on top of the soil. Then data was collected for 20 minutes. The CO2, temperature, and relative humidity data from concepts 1 and 2 were compared to each other within each housing subsample in excel.
Methods used for Data Analysis
To analyze the data collected from the samples and devices, the data was put into tables and graphed in scatter plots. These data and graphs are in Appendix B for the calibration and reliability tests and Appendix C for the housing tests.
For methane, the device measurements were converted from millivolts to volts. Then the equation for calibration of the device was derived by finding the trend between the gas chromatograph data (ppm) and device measurements (V) (Appendix B.11). The trend yielded the following equation:
To calibrate the device to output ppm rather than mV, the equation was used to amend the code. The device still outputs spikes consistent with analog readings due to the background noise, this can be addressed by further adjusting the code to smooth the data by averaging the inputs.
To analyze and compare the CO2 and methane output from device with data from the gas chromatograph in both chambers, an Analysis of Variance (ANOVA) test was performed using Excel. With a confidence interval of 95%, the alpha was 0.05. The ANOVA test has a null hypothesis that there is no significant difference in the data from the samples of all groups (Sullivan, 2021). If the test produces a p-value less than the alpha value, the null hypothesis is rejected. If the null hypothesis is rejected, there is a statistically significant difference between the groups. If the p-value is greater than alpha value, then the null hypothesis is not rejected and the data from both groups is considered to not have a significant difference.
The CO2 data collected from the housing tests was graphed together and compared. This visualization would show how closely correlated the concepts were for each housing size. An ANOVA test to compare the CO2 outputs of each concept was performed to determine which housing size would produce the least variation in results.
Results
Calibration and Reliability Tests
During the calibration and reliability tests, there was a range of temperatures in the greenhouse due to a large drop in temperature from 8 am on Thursday, March 31, 2022, to 8 am on Friday, April 1, 2022. Temperature has an influence on the activity of biological organisms in soil. This effect was seen in the concentration of CO2 above the soil when comparing the results for each test. Figure 19 shows the designed device’s CO2 concentration reading during each calibration and reliability test and helps illustrate the effect of temperature on CO2concentrations from soil.
Figure 19: CO2 Concentration Measured by Device for Reliability & Calibration Tests
Using concept 2, CO2 and methane concentration data from the three samples from each chamber was averaged to obtain one data point for each time. Then the device’s readings at 0-minutes, 5-minutes, 10-minutes, and 20-minutes were extracted from the data collected for comparison to the sample data at these times. Table 4 and Table 5 show this data for CO2 and methane, respectively.
Table 4: CO2 Measured by Device & Gas Chromatograph for Calibration & Reliability Tests in PPM
| Thurs 8am | Thurs 1pm | Fri 8am | Fri 1pm | |
| Device – 0 min | 688.256 | 701.875 | 627.467 | 491.730 |
| Device – 5 min | 2922.998 | 2596.571 | 1810.473 | 2156.265 |
| Device – 10 min | 4923.258 | 3893.011 | 2705.427 | 3467.495 |
| Device – 20 min | 7932.407 | 6813.009 | 4390.396 | 5718.683 |
| Left Chamber – 0 min | 1497.860 | 1084.260 | 770.327 | 814.704 |
| Left Chamber – 5 min | 3506.990 | 2352.820 | 1834.900 | 2637.050 |
| Left Chamber – 10 min | 5513.800 | 4167.200 | 2934.560 | 4252.350 |
| Left Chamber – 20 min | 7324.190 | 6913.690 | 4157.370 | 6405.370 |
| Right Chamber – 0 min | 1239.700 | 1732.000 | 779.300 | 935.783 |
| Right Chamber – 5 min | 2862.920 | 2732.290 | 1907.910 | 1797.280 |
| Right Chamber – 10 min | 2526.550 | 4911.300 | 2756.700 | 2853.460 |
| Right Chamber – 20 min | 5455.970 | 6937.750 | 4446.580 | 4707.440 |
Table 5: CH4 Measured by Device & Gas Chromatograph for Calibration & Reliability Tests in PPM
| Thurs 8am | Thurs 1pm | Fri 8am | Fri 1pm | |
| Device – 0 min | 1.13555859 | 1.19038768 | 1.21121283 | 1.21318422 |
| Device – 5 min | 1.12351981 | 1.19858679 | 1.21713493 | 1.12962599 |
| Device – 10 min | 1.13187267 | 1.1990197 | 1.22795849 | 1.13658506 |
| Device – 20 min | 1.14669242 | 1.19254251 | 1.24178864 | 1.13085 |
| Left Chamber – 0 min | 1.171 | 1.31166667 | 1.196 | 1.20333333 |
| Left Chamber – 5 min | 1.09666667 | 1.42233333 | 1.34466667 | 1.20866667 |
| Left Chamber – 10 min | 1.19 | 1.213 | 1.143 | 1.14333333 |
| Left Chamber – 20 min | 0.97 | 1.19833333 | 1.23266667 | 0.98333333 |
| Right Chamber – 0 min | 1.09966667 | 1.24566667 | 1.10666667 | 1.22333333 |
| Right Chamber – 5 min | 1.019 | 1.158 | 1.239 | 1.161 |
| Right Chamber – 10 min | 1.21133333 | 1.22 | 1.254 | 1.21533333 |
| Right Chamber – 20 min | 0.91666667 | 1.2345 | 1.08166667 | 1.31966667 |
ANOVA tests for the calibration and reliability tests were performed using the data above. For the CO2, the p-value obtained was 0.82869 for the comparison among the samples from each test. This means that there is a failure to reject the null and no significant difference was found between the device and gas chromatograph samples. The device and the gas chromatograph can be considered to produce similar data. Figure 20 shows the data collected from the left chamber which was the chamber that the device was in for the calibration and reliability tests and the devices readings. This graph provides visualization of the similarity between the data.
Figure 20: Comparison Graph of Device and Gas Chromatograph CO2 Readings
The ANOVA test for methane produced a p-value of 0.7551. This means that there was a failure to reject the null. Figure 21 is a comparison graph of the left chamber and the devices readings of methane concentrations. A low concentration of methane in the air leads the visualization of data from the graph to show variation. Typically, methane air concentration stays below 2 ppm, so the average data for methane is consistent with what was expected in all tests.
Figure 21: Methane in PPM Detected by Device and Gas Chromatograph
Housing Tests
Figure 22 shows the CO2 concentrations detected by both concepts in different housing sizes. This graph shows both concepts measured similar outputs for all tests. When the concepts were in the 1-gallon and 5-gallon housings, they saw a rise in the concentrations like the gas chromatograph tests. The 2-gallon housing did not see this rise. The team hypothesis as to why this has happened was due to the housing having two spouts that were not pushed into the soil. Since this containment system was not sealed, it allowed air to escape.
An ANOVA test was performed for each the 1-gallon, 2-gallon and 5-gallon housing, and all fail to reject the null hypothesis. The 5-gallon housing had the highest p-value which shows is was the least significantly different when comparing the data from the gas chromatography test on 4/1/2022 at 1pm. (Appendix C.9-C.11).
Figure 22: CO2 Concentrations Detected by Concepts in Different Types of Housings
Figure 23 and Figure 24 show the detection of relative humidity and temperature for both concepts. The relative humidity and the temperature measured by the concepts while in the 1-gallon housing container varied depending on the device. The team suspects this was due to the 1-gallon being smaller than the other housing concepts. Since it was smaller, the heat from the devices was trapped in the housing container which led to a rise in temperature and relative humidity.
Figure 23: Relative Humidity Detected by Concepts in Different Types of Housings
Figure 24: Temperature Detected by Concepts in Different Types of Housings
Figure 25 shows the methane data collected from the housing tests. From these results, the housing does not have a large effect on the methane concentration that the device reads. This could be due to the low concentration in the air. Following analysis of the data collected during the housing tests, the 5-gallon housing performed the best. The team recommends further testing to optimize the housing to fit the client needs.
Figure 25: Methane Reading from Sensors During Housing Tests
Communication Tests
The device was able to establish a connection and transmit data through the Arduino IoT cloud infrequently. Due to recurring issues with the network connection and the Arduino IoT cloud, the cellular communication aspect was deemed unreliable and was not used for data collection. So, the serial monitor had to be used and the data was exported to excel. As a result, other methods of cellular communication need to be further investigated and tested.
Cost Analysis
The cost of this project can be broken down into two separate categories, the first being the cost of the device and the second is the cost of the testing. The breakdown of device cost can be seen in Table 6 and the breakdown of testing costs can be seen in Table 7.
These costs do not include materials that were borrowed or independently procured. These materials include Arduino Leonardo, wires, Velcro, glue dots, hot glue gun, 1-gallon container, 3-gallon container, waterproof Arduino box, zip ties, external Arduino battery, plastic tote for testing, vacuumed vials, syringes, soil gas chamber, gas chromatograph, timers, and thermometers.
| Source | Part | Quantity | Price |
| Adafruit | DHT22 Temperature /Humidity Sensor |
1 | $9.95 |
| Adafruit | SCD 30 CO2 Sensor |
1 | $58.95 |
| Arduino | Arduino MKR GSM 1400 | 1 | $55.12 |
| Arduino | SIM Card | 1 | $3.00 |
| Digikey | Circuit Kit | 1 | $19.95 |
| Digikey | K30 CO2 Sensor |
1 | $65.52 |
| RS | NGM2611-E13 Methane Sensor |
1 | $30.00 |
| Total | $242.49 | ||
| Source | Part | Quantity | Price |
| Lowes | 5-Gallon Bucket | 1 | $4.98 |
| Lowes | Organic Soil | 2 | $12.98 |
| Total | $30.94 | ||
The final cost of the design will decrease since only one CO2 sensor will be needed. Concept 1 includes the Arduino, circuit kit, SIM card, K30 sensor, NGM2611-E13 sensor, and DHT22 sensor and will cost $183.54. Concept 2 includes the Arduino, circuit kit, SIM card, SCD30 sensor, and NGM2611-E13 sensor and will cost $167.02. Since the two concepts have similar costs, it is recommended use the most accurate concept for the final design. The housing cost will vary depending on which size container is desired. This will be a low cost, about $5.00.
There is no specific cost associated with the maintenance of the device but certain components, such as the battery and SIM card, may need to be replaced. Which have an estimated cost of $19.00.
Further Design Considerations
Environmental/sustainability
The design of this device was crafted to match a high level of sustainability. It can be reused through conducted experiments and into the future. The use of solar panels as a source of renewable energy to power the mainframe of the device would eliminate the need for nonrenewable energy sources. Solar power would be an eco-friendly form of energy and would give the device a sustainable way to operate independently in the field. The second factor in sustainability is the cellular components of the Arduino MKR GSM 1400 that allow for remote data communication through cloud interfaces, such as Arduino Cloud. This would give the user a method of receiving data in real time through internet-based devices.
Manufacturability
The Arduino, which serves as the main component of the design is manufactured by the Arduino company. The Arduino MKR GSM 1400 can be found on the Arduino website for purchase along with a SIM card that is accompanied by it. All sensors including the SCD 30, K30, DHT22, & NGM2611-E13 must be manufactured by their respected distribution company. The water-resistant box component is manufactured by a 3rd party company as a waterproof container. Then is modified by creating a hole for the passage of wires between the sensors and Arduino component. It will then be sealed for the prevention of environmental factors that can alter the performance of the design.
Ethical/health and safety
The first consideration for safety in this design is the prevention of using higher voltage than the device is capable of intaking. The maximum voltage that the Arduino can input is 5V. This means that any higher transfer of energy can result in electrical issues that will alter the design temporarily or permanently. This could also place clients in potential harm due to this electrical hazard. To prevent electrical hazards, like electrocution, the wires should be soldered to the breadboard and the sensors. This will also prevent incorrect orientation of wiring and provide a sturdy connection between the device’s components.
Social/political
Growers have used alternative methods to monitor soil health, such as seasonal or yearly soil testing and analyzing parameters from field observations. This device can help clients easily partake in carbon credit programs. But the traditional methods to measure these parameters may remain the preferred method due to experience with those systems. Providing incentives for the purchase of this device through carbon credit programs may increase client interest. Emphasis on the devices ability to provide the information quickly may attract clients that want to react to the information immediately rather than waiting on lab results.
Conclusions and Recommendations
Following the evaluations conducted by the team, the microcontroller and sensor combination are a viable solution to grower’s need to monitor greenhouse gases emissions in the field quickly and reliably. A device such as the one the team designed could give growers a way to detect methane and CO2 concentrations released from the soil and, by proxy, monitor their soil biological health.
Both Concept 1 and Concept 2 performed similarly in the housing tests and could work with any future monitoring device. Concept 1 would need to be investigated further due to the lack of reliable data transmission with the Arduino IoT Cloud and problems with DHT22 library connection in the code. Since Concept 2 did not display as many issues and has a temperature and humidity sensor built into the SCD30 CO2 sensor, the team recommends focusing on this concept moving forward with the design.
The housing tests conducted showed the 5-gallon housing performed the best. The 1-gallon housing saw a spike in the temperature with the sensors, due to the size of the container. The 2-gallon container was not properly sealed so further testing would need to be conducted. Due to this, the team recommends more housing testing to be done to optimize the housing. Potentially varying the area of the ground covered rather than just the volume of the container. This could be accomplished by testing containers with the same volume that all have different heights.
For the device to be ready for in-field deployment, it needs a method for reliable data communication, an SD card for backup storage, and an external power supply. These components will allow the device to operate independently of a computer or electrical outlet. Due to time constraints of the project, the team was unable to explore these problems. Moving forward, the team recommends that these topics are addressed. Before the design is used in the future, some materials that were borrowed or individually procured will need to be obtained. These materials include breadboards, longer wires, external Arduino battery, and plastic tote for testing.
In future iterations of the project, different platforms for IoT or other SIM cards should be investigated due to the lack of reliability with the Arduino IoT Cloud. Incorporating an SD card into the design will allow for data to be backed up in the case of communication failure. The device will also need the code adjusted for the analog output of the methane sensor to be smoothed due to the device picking up “noise”. This will create a smooth line of data by averaging the values being read. Additionally, the team recommends research into solar panels to power the device independently and sustainably. It would also be beneficial to perform longer and more extensive testing to prove the device is suitable and reliable for long term data collection.
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Shedekar, Vinayak. “Solvita ® CO2 Respiration Soil Health Test.” Solvita ® CO2 Respiration Soil Health Test | Agronomic Crops Network, https://agcrops.osu.edu/newsletter/corn-newsletter/2019-08/solvita-%C2%AE-co2-respiration-soil-health-test.
Smith, Dianna. “CO2 Sensors: Which Type Should You Be Looking for?” Kaiterra, 6 Oct. 2019, https://learn.kaiterra.com/en/air-academy/carbon-dioxide-sensors.
Sullivan, L. (2021). Introduction. Hypothesis Testing – Analysis of Variance (ANOVA). Retrieved April 12, 2022, from https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_hypothesistesting-anova/bs704_hypothesistesting-anova_print.html
Sureshkumar, N., & Dutta, A. (2020). Environmental gas sensors based on nanostructured thin films. Multilayer Thin Films – Versatile Applications for Materials Engineering. https://doi.org/10.5772/intechopen.89745
Teja, Ravi. “What Are the Differences between Raspberry Pi and Arduino?” Electronics Hub, 31 Dec. 2021, https://www.electronicshub.org/raspberry-pi-vs-arduino/.
TERC. “5A: Soil, Carbon and Microbes.” Climate and the Carbon Cycle, SERC, 17 May 2021, https://serc.carleton.edu/eslabs/carbon/5a.html.
Transducer Sensors. (2018, May 5). Calorimetric sensors. web hit counter. Retrieved April 14, 2022, from https://transducersensors.com/calorimetric-sensors/
Turner, Diane. “Gas Chromatography.” Analysis & Separations from Technology Networks, 17 Mar. 2021, https://www.technologynetworks.com/analysis/articles/gas-chromatography-how-a-gas-chromatography-machine-works-how-to-read-a-chromatograph-and-gcxgc-335168.
Winsen. “Flammable Gas Sensor Manual.” 2018.
Appendix A: Qualifications of Personnel
Gabe Aggrey
Gabriel will be graduating from The Ohio State University in May 2022 with a bachelor’s degree in Food, Agricultural, and Biological Engineering with a specialization in Biological Engineering. He has been involved in organizations such as Ohio State’s Fresh Image group & Black Student Association. As a graduate of Cristo Rey Columbus, Gabriel has participated in the work study internship program within businesses such as Commerce National Bank and Ometek Incorporated. His academic career at tOSU consists of academics in organic chemistry, system instrumentation & dynamics, as well as MATLAB & AUTOCAD courses. Skills that Gabriel demonstrates include critical thinking, computer literacy, and efficient teamwork.
Alyssa Bowles
Alyssa will be graduating from The Ohio State University December 2022 with a Bachelor’s in Food, Agricultural, and Biological Engineering with a specialization in Ecological Engineering. As well as obtaining minors in Women’s, Gender, Sexuality Studies, and Environmental Science with a focus in ecosystem restoration. Alyssa was a member and mentor for the Environmental and Natural Resources Scholars Program. She completed two internships with the Ohio EPA: Division of Air Pollution Control and worked as a research assistant in OSU’s Ecohydrology Research Lab. As a student, Alyssa was involved in various leadership positions for student organizations. She was the President of Students for a Sustainable Campus, the Founder and President for Painting with Bob Ross at OSU, and Education Committee Chair and Campus Ambassador for Period. All these experiences have helped Alyssa develop problem solving, time management, teamwork, and leadership skills.
Madeline Prenger
Madeline will be graduating from The Ohio State University May 2022 with a Bachelor’s in Food, Agriculture, and Biological Engineering and with a minor in Biomedical Engineering. Throughout her educational experience, Madeline completed an internship with Exact Sciences in Phoenix, Arizona and worked as a Service Engineer Intern. In her academic career, Madeline took system dynamic courses, heat and mass transfer courses, MATLAB courses, AUTOCAD courses and many others. These experiences lead to the continuous development of teamwork and leadership skills. After graduation, Madeline will be participating in the Early Career Program (Engineering) at Mettler Toledo.
Kaylee Sabo
Kaylee will be graduating from The Ohio State University in May 2022 with a bachelor’s degree in Food, Agricultural, and Biological Engineering with a specialization in Agricultural Engineering, a focus in soil and water, and a minor in Soil Science. She has taken a variety of courses such as stream systems, ecological engineering and science, geotechnical engineering, system dynamics, and energy in agriculture. Kaylee was a part of the Green Engineering Scholars program at tOSU, the Christians on Campus Organization, and worked as an Office Assistant for all four years while obtaining her degree. She has also volunteered every summer with the Jefferson County Soil and Water Conservation since high school. After graduation, Kaylee will be starting her career as an Environmental Protection Engineer with the State of Tennessee’s Department of Environment and Conservation in the Water Resource Department in Jackson, Tennessee.
Appendix B: Results from Sensor Calibration & Reliability Testing
Appendix B.1: Thursday – 3/31 – 8 am Sensor Data
Provided in Final Data Excel File – Sheet: Th 8 am – C&R
Appendix B.2: Thursday – 3/31 – 1 pm Sensor Data
Provided in Final Data Excel File – Sheet: Th 1 pm – C&R
Appendix B.3: Friday – 4/1 – 8 am Sensor Data
Provided in Final Data Excel File – Sheet: Fr 8 am – C&R
Appendix B.4: Friday – 4/1 – 1 pm Sensor Data
Provided in Final Data Excel File – Sheet: Fr 1 pm – C&R
Appendix B.5: Gas Chromatograph Data
Provided in Final Data Excel File – Sheet: C&R sheets
Appendix B.6: Gas Chromatograph Data Averaged
| Methane (ppm) | Carbon Dioxide(ppm) | ||||
| Test Time | Time Sample Taken (min.) | Left Chamber | Right Chamber | Left Chamber | Right Chamber |
| Thursday 1pm | 0 | 1.311666667 | 1.245666667 | 1,084.26 | 1,732.00 |
| 5 | 1.422333333 | 1.158 | 2,352.82 | 2,732.29 | |
| 10 | 1.213 | 1.22 | 4,167.20 | 4,911.30 | |
| 20 | 1.198333333 | 1.2345 | 6,913.69 | 6,937.75 | |
| Thursday 8 am | 0 | 1.171 | 1.099666667 | 1,497.86 | 1,239.70 |
| 5 | 1.096666667 | 1.019 | 3,506.99 | 2,862.92 | |
| 10 | 1.19 | 1.211333333 | 5,513.80 | 2,526.55 | |
| 20 | 0.97 | 0.916666667 | 7,324.19 | 5,455.97 | |
| Friday 1pm | 0 | 1.203333333 | 1.223333333 | 814.7043333 | 935.7826667 |
| 5 | 1.208666667 | 1.161 | 2,637.05 | 1,797.28 | |
| 10 | 1.143333333 | 1.215333333 | 4,252.35 | 2,853.46 | |
| 20 | 0.983333333 | 1.319666667 | 6,405.37 | 4,707.44 | |
| Friday
8 am |
0 | 1.196 | 1.106666667 | 770.3266667 | 779.2996667 |
| 5 | 1.344666667 | 1.239 | 1,834.90 | 1,907.91 | |
| 10 | 1.143 | 1.254 | 2,934.56 | 2,756.70 | |
| 20 | 1.232666667 | 1.081666667 | 4,157.37 | 4,446.58 | |
Appendix B.7: Gas Chromatograph and Sensor Carbon Dioxide Graphs
Appendix B.8: Carbon Dioxide ANOVA Tables
| Anova: Single Factor
|
||||||
| SUMMARY | ||||||
| Groups | Count | Sum | Average | Variance | ||
| Device | 16 | 51839.32 | 3239.958 | 5127598 | ||
| Left | 16 | 56167.44 | 3510.465 | 4658345 | ||
| Right | 16 | 48582.93 | 3036.433 | 3140727 | ||
| ANOVA | ||||||
| Source of Variation | SS | df | MS | F | P-value | F crit |
| Between Groups | 1809613.48 | 2 | 904806.7 | 0.20999 | 0.81139 | 3.204317 |
| Within Groups | 193900042 | 45 | 4308890 | |||
| Total | 195709655 | 47 | ||||
| Anova: Two-Factor With Replication
|
||||||
| SUMMARY | Thurs 8am | Thurs1pm | Fri 8 am | Thurs 1 pm | Total | |
| Device | ||||||
| Count | 4 | 4 | 4 | 4 | 16 | |
| Sum | 16466.919 | 14004.47 | 9533.763 | 11834.2 | 51839.3 | |
| Average | 4116.72975 | 3501.117 | 2383.441 | 2958.54 | 3239.96 | |
| Variance | 9463102.81 | 6592056 | 2514427 | 4868740 | 5127598 | |
| Left | ||||||
| Count | 4 | 4 | 4 | 4 | 16 | |
| Sum | 17842.84 | 14517.97 | 9697.157 | 14109.5 | 56167.4 | |
| Average | 4460.71 | 3629.493 | 2424.289 | 3527.37 | 3510.47 | |
| Variance | 6332192.74 | 6394395 | 2115639 | 5653236 | 4658345 | |
| Right | ||||||
| Count | 4 | 4 | 4 | 4 | 16 | |
| Sum | 12085.14 | 16313.34 | 9890.49 | 10294 | 48582.9 | |
| Average | 3021.285 | 4078.335 | 2472.623 | 2573.49 | 3036.43 | |
| Variance | 3123859.45 | 5395737 | 2387817 | 2638904 | 3140727 | |
| Total | ||||||
| Count | 12 | 12 | 12 | 12 | ||
| Sum | 46394.899 | 44835.78 | 29121.41 | 36237.6 | ||
| Average | 3866.24158 | 3736.315 | 2426.784 | 3019.8 | ||
| Variance | 5570710.87 | 5080126 | 1915417 | 3756811 | ||
| ANOVA | ||||||
| Source of Variation | SS | df | MS | F | P-value | F crit |
| Sample | 1809613.48 | 2 | 904806.7 | 0.18889 | 0.82869 | 3.259446 |
| Columns | 16155934.7 | 3 | 5385312 | 1.12428 | 0.35212 | 2.866266 |
| Interaction | 5303793.94 | 6 | 883965.7 | 0.18454 | 0.97928 | 2.363751 |
| Within | 172440313 | 36 | 4790009 | |||
| Total | 195709655 | 47 | ||||
Appendix B.9: Untreated Methane Graphs
Appendix B.10: Methane Untreated Comparison Graphs
Appendix B.11: Sensor Methane Output Versus Gas Chromatograph Trend
Appendix B.12: Calculated Methane Concentration
| Day | Time(min) | CH4 Output (mV) | CH4 Output (V) | CH4 Concentrations (ppm) |
| Thursday 1pm | 0 | 1071.78 | 1.07178 | 1.13533333 |
| 5 | 1119.79 | 1.11979 | 1.05783333 | |
| 10 | 1086.43 | 1.086425 | 1.20066667 | |
| 20 | 1027.84 | 1.027835 | 0.94333333 | |
| Thursday
8 am |
0 | 859.38 | 0.859375 | 1.27866667 |
| 5 | 833.33 | 0.83333333 | 1.29016667 | |
| 10 | 825.20 | 0.8252 | 1.2165 | |
| 20 | 851.23 | 0.85123333 | 1.21641667 | |
| Friday
1pm |
0 | 781.25 | 0.78125 | 1.15133333 |
| 5 | 758.47 | 0.75846667 | 1.29183333 | |
| 10 | 720.22 | 0.720215 | 1.1985 | |
| 20 | 668.95 | 0.66895 | 1.15716667 | |
| Friday
8 am |
0 | 773.925 | 0.773925 | 1.21333333 |
| 5 | 1095.376667 | 1.09537667 | 1.18483333 | |
| 10 | 1066.895 | 1.066895 | 1.17933333 | |
| 29 | 1090.496667 | 1.09049667 | 1.1515 |
Appendix B.13: Calibrated Methane Graphs
Appendix B.14: Calibrated Methane Comparison Graphs
Appendix B.15: Methane ANOVA Table
| Anova: Single Factor | ||||||
| SUMMARY | ||||||
| Groups | Count | Sum | Average | Variance | ||
| Column 1 | 16 | 19.028 | 1.18925 | 0.0133497 | ||
| Column 2 | 16 | 18.7055 | 1.16909375 | 0.01047747 | ||
| Column 3 | 16 | 18.8265203 | 1.17665752 | 0.00171965 | ||
| ANOVA | ||||||
| Source of Variation | SS | df | MS | F | P-value | F crit |
| Between Groups | 0.00331763 | 2 | 0.00165881 | 0.19479701 | 0.823692 | 3.20431729 |
| Within Groups | 0.38320233 | 45 | 0.00851561 | |||
| Total | 0.38651996 | 47 | ||||
| Anova: Two-Factor With Replication | ||||||
| SUMMARY | Thurs 8am | Thurs 1pm | Fri 8am | Fri 1pm | Total | |
| Device | ||||||
| Count | 4 | 4 | 4 | 4 | 16 | |
| Sum | 4.53764349 | 4.78053667 | 4.89809489 | 4.61024527 | 18.8265203 | |
| Average | 1.13441087 | 1.19513417 | 1.22452372 | 1.15256132 | 1.17665752 | |
| Variance | 9.2404E-05 | 1.8755E-05 | 0.00018055 | 0.0016426 | 0.00171965 | |
| Left | ||||||
| Count | 4 | 4 | 4 | 4 | 16 | |
| Sum | 4.42766667 | 5.14533333 | 4.91633334 | 4.53866666 | 19.028 | |
| Average | 1.10691667 | 1.28633333 | 1.22908334 | 1.13466667 | 1.18925 | |
| Variance | 0.00995358 | 0.01075319 | 0.0072924 | 0.011056 | 0.0133497 | |
| Right | ||||||
| Count | 4 | 4 | 4 | 4 | 16 | |
| Sum | 4.24666667 | 4.85816667 | 4.68133333 | 4.91933333 | 18.7055 | |
| Average | 1.06166667 | 1.21454167 | 1.17033333 | 1.22983333 | 1.16909375 | |
| Variance | 0.01556319 | 0.00153128 | 0.00787681 | 0.00435352 | 0.01047747 | |
| Total | ||||||
| Count | 12 | 12 | 12 | 12 | ||
| Sum | 13.2119768 | 14.7840367 | 14.4957616 | 14.0682453 | ||
| Average | 1.10099807 | 1.23200306 | 1.20798013 | 1.17235377 | ||
| Variance | 0.00796556 | 0.00503397 | 0.00496314 | 0.00651093 | ||
| ANOVA | ||||||
| Source of Variation | SS | df | MS | F | P-value | F crit |
| Sample | 0.00331763 | 2 | 0.00165881 | 0.28309726 | 0.75510829 | 3.25944631 |
| Columns | 0.11731044 | 3 | 0.03910348 | 6.67349207 | 0.00106668 | 2.86626555 |
| Interaction | 0.05494907 | 6 | 0.00915818 | 1.56295631 | 0.18633105 | 2.36375096 |
| Within | 0.21094282 | 36 | 0.00585952 | |||
| Total | 0.38651996 | 47 | ||||
Appendix C: Results from Housing Testing
Appendix C.1: Concept 1 1-Gallon Housing Data
Provided in Final Data Excel File – Sheet: 1 Gallon – Housing
Appendix C.2: Concept 2 1-Gallon Housing Data
Provided in Final Data Excel File – Sheet: 1 Gallon – Housing
Appendix C.3: Concept 1 2-Gallon Housing Data
Provided in Final Data Excel File – Sheet: 2 Gallon – Housing
Appendix C.4: Concept 2 2-Gallon Housing Data
Provided in Final Data Excel File – Sheet: 2 Gallon – Housing
Appendix C.5: Concept 1 5-Gallon Housing Data
Provided in Final Data Excel File – Sheet: 5 Gallon – Housing
Appendix C.6: Concept 2 5-Gallon Housing Data
Provided in Final Data Excel File – Sheet: 5 Gallon – Housing
Appendix C.7: Housing Test Carbon Dioxide Reading Comparison Graphs
Appendix C.8: Housing Test Temperature Reading Comparison Graphs
Appendix C.9: Housing Test Relative Humidity Reading Comparison Graphs
Appendix C.8: Housing Test Methane Comparison Graphs
Appendix C.9: 1-Gallon ANOVA Table
Appendix C.10: 2-Gallon ANOVA Table
Appendix C.11: 5-Gallon ANOVA Table
Appendix D: Specification Sheets
Appendix D.1: SCD30 Documentation
https://sensirion.com/media/documents/4EAF6AF8/61652C3C/Sensirion_CO2_Sensors_SCD30_Datasheet.pdf
https://cdn-learn.adafruit.com/downloads/pdf/adafruit-scd30.pdf
Appendix D.2: K30 Documentation
http://co2meters.com/Documentation/Datasheets/DS_SE_0118_CM_0024_Revised9%20(1).pdf
Appendix D.3: DHT22 Documentation
https://cdn-learn.adafruit.com/downloads/pdf/dht.pdf
Appendix D.4: NGM2611 – E13 Documentation
https://www.figarosensor.com/product/docs/NGM-2611E%200714%20Layout.pdf
Appendix D.5: Arduino MKR GSM 1400 Documentation
https://docs.arduino.cc/static/d57147b95d9a6ce882907a4bb72fbc5e/ABX00018-full-pinout.pdf
Appendix E: Desktop Sensor Code
Appendix E.1: Concept 1
// Concept 1
//NGM2611E13(CH4), DHT22(Temp and Humidity) and K3030(CO2) sensors
//By FABE’s Soil Biological Health Capstone 2021-2022 Team
//Libaries Needed
//K30 Libary is not an arduino library and needs downloaded
//Its called the FirstCypress-K30_CO2_I2C_Arduino
#include “DHT.h”
#include <K30_I2C.h>
//Pins for DHT
#define DHTPIN 8
#define DHTTYPE DHT22 // DHT 22 (AM2302), AM2321
// Initialize DHT sensor.
DHT dht(DHTPIN, DHTTYPE);
#define ECHO_TO_SERIAL 1 // echo data to serial port
#define WAIT_TO_START 0 // Wait for serial input in setup()
// The analog pins that connect to the sensors
#define CH4sens A1 //CH4 sensor Vout
#define CH4ref A2 //CH4 sensor Vref
#define Vb A0 //Battery voltage
int CH4s = 0;
int CH4r = 0;
int Vbat = 0;
float CH4smV = 0;
float CH4rmV = 0;
float VbatmV = 0;
float mV = 5000;
float steps = 1024;
void setup(void)
{
// put your setup code here, to run once:
Serial.begin(9600);
Serial.begin(9600);
Serial.println(F(“DHTxx test!”));
dht.begin();
}
K30_I2C k30_i2c = K30_I2C(0x68);
int co2 = 0;
int rc = 1;
void loop(){
Serial.print(“Reading K30 sensor …… \n”);
rc = k30_i2c.readCO2(co2);
if (rc == 0){
Serial.print(“Succesful reading\n”);
Serial.print(co2);
Serial.print(” ppm”);
} else{
Serial.print(“Failure to read sensor\n”);
}
Serial.println(“\n”);
delay(3000); //time between readings
float h = dht.readHumidity();
// Read temperature as Celsius (the default)
float t = dht.readTemperature();
// Read temperature as Fahrenheit (isFahrenheit = true)
float f = dht.readTemperature(true);
// Check if any reads failed and exit early (to try again).
if (isnan(h) || isnan(t) || isnan(f)) {
Serial.println(F(“Failed to read from DHT sensor!”));
return;
}
CH4s = analogRead(CH4sens); //read CH4 Vout
CH4smV = CH4s*(mV/steps); //convert pin reading to mV
delay(10); //delay between reading of different analogue pins adviced.
CH4r = analogRead(CH4ref); //read CH4 Vref
CH4rmV = CH4r*(mV/steps); //convert pin reading to mV
delay(10); //delay between reading of different analogue pins adviced.
Vbat = analogRead(Vb); //read CH4 Vref
VbatmV = Vbat *(mV/steps); //convert pin reading to mV, NOT YET correcting for the voltage divider.
delay(10); //delay between reading of different analogue pins adviced.
#if ECHO_TO_SERIAL
// Compute heat index in Fahrenheit (the default)
float hif = dht.computeHeatIndex(f, h);
// Compute heat index in Celsius (isFahreheit = false)
float hic = dht.computeHeatIndex(t, h, false);
Serial.print(F(“Humidity: “));
Serial.print(h);
Serial.print(F(“% Temperature: “));
Serial.print(t);
Serial.print(F(“°C “));
Serial.print(f);
Serial.print(F(“°F Heat index: “));
Serial.print(hic);
Serial.print(F(“°C “));
Serial.print(hif);
Serial.println(F(“°F”));
Serial.print(“CH4 Output Voltage: “);
Serial.print(CH4smV);
Serial.println(” milivolts “);
Serial.print(“CH4 Reference Voltage: “);
Serial.print(CH4rmV);
Serial.println(” milivolts “);
Serial.print(“CH4 Battery Voltage: “);
Serial.print(VbatmV, 3);
Serial.println(” milivolts “);
Serial.println(“”);
#endif //ECHO_TO_SERIAL
}
Appendix E.2: Concept 2
// Concept 2
//NGM2611E13(CH4) and SCD30(CO2) sensors
//By FABE’s Soil Biological Health Capstone 2021-2022 Team
// Basic demo for readings from Adafruit SCD30
#include <Adafruit_SCD30.h>
Adafruit_SCD30 scd30;
#define SCD30_I2CADDR_DEFAULT 0x61 ///< SCD30 default i2c address
#define SCD30_CHIP_ID 0x62 ///< SCD30 default device id from WHOAMI
#define LOG_INTERVAL 4000 // mills between logging entries (reduce to take more/faster data)
#define SYNC_INTERVAL 5000 // mills between calls to flush() – to write data to the card
uint32_t syncTime = 0; // time of last sync()
#define ECHO_TO_SERIAL 1 // echo data to serial port
#define WAIT_TO_START 0 // Wait for serial input in setup()
// The analog pins that connect to the sensors
#define CH4sens A1 //CH4 sensor Vout
#define CH4ref A2 //CH4 sensor Vref
#define Vb A0 //Battery voltage
int CH4s = 0;
int CH4r = 0;
int Vbat = 0;
float CH4ppm = 0;
float CH4smV = 0;
float CH4rmV = 0;
float VbatmV = 0;
float mV = 5000;
float steps = 1024;
// for the data logging shield, we use digital pin 10 for the SD cs line
const int chipSelect = 10;
/////////////////////////
/* Begin Read Sequence */
/////////////////////////
void setup(void) {
Serial.begin(115200);
while (!Serial) delay(20); // will pause Zero, Leonardo, etc until serial console opens
Serial.println(“Adafruit SCD30 test!”);
// Try to initialize!
if (!scd30.begin()) {
Serial.println(“Failed to find SCD30 chip”);
while (1) { delay(20); }
}
Serial.println(“SCD30 Found!”);
// if (!scd30.setMeasurementInterval(10)){
// Serial.println(“Failed to set measurement interval”);
// while(1){ delay(10);}
// }
Serial.print(“Measurement Interval: “);
Serial.print(scd30.getMeasurementInterval());
Serial.println(” seconds”);
//#if WAIT_TO_START
// Serial.println(“Type any character to start”);
// while (!Serial.available());
//#endif //WAIT_TO_START
}
void loop(){
if (scd30.dataReady()){
// Serial.println(“Data available!”);
if (!scd30.read()){ Serial.println(“Error reading sensor data”); return; }
Serial.print(“CO2 Concentration: “);
Serial.print(scd30.CO2, 3);
Serial.println(” ppm”);
Serial.print(“Temperature: “);
Serial.print(scd30.temperature);
Serial.println(” degrees C”);
Serial.print(“Relative Humidity: “);
Serial.print(scd30.relative_humidity);
Serial.println(” %”);
Serial.println(“”);
} else {
Serial.println(“No data”);
}
delay(400);
// delay for the amount of time we want between readings
delay((LOG_INTERVAL -1) – (millis() % LOG_INTERVAL));
// log milliseconds since starting
uint32_t m = millis();
#if ECHO_TO_SERIAL
//Serial.print(“time since starting: “);
//Serial.print(m);
//Serial.println(” millisecond “);
#endif
CH4s = analogRead(CH4sens); //read CH4 Vout
CH4smV = CH4s*(mV/steps)/(10*10*10); //convert pin reading to mV
CH4ppm = 1.4406*(exp(-(0.222*CH4smV)));
delay(10); //delay between reading of different analogue pins adviced.
CH4r = analogRead(CH4ref); //read CH4 Vref
CH4rmV = CH4r*(mV/steps); //convert pin reading to mV
delay(10); //delay between reading of different analogue pins adviced.
Vbat = analogRead(Vb); //read CH4 Vref
VbatmV = Vbat *(mV/steps); //convert pin reading to mV, NOT YET correcting for the voltage divider.
delay(20); //delay between reading of different analogue pins adviced.
#if ECHO_TO_SERIAL
// Serial.print(“CH4 Output Voltage: “);
// Serial.print(CH4smV);
// Serial.println(” milivolts “);
Serial.print(“CH4 Concentration: “);
Serial.print(CH4ppm);
Serial.println(” ppm “);
#endif //ECHO_TO_SERIAL
}
Appendix F: IoT Sensor Code
/*
Sketch generated by the Arduino IoT Cloud Thing “Concept 2 NEW”
https://create.arduino.cc/cloud/things/bb32d860-c9bd-42aa-9c0b-a30f35ba3982
Arduino IoT Cloud Variables description
The following variables are automatically generated and updated when changes are made to the Thing
float ch4;
float co2;
float humidity;
float temp;
Variables which are marked as READ/WRITE in the Cloud Thing will also have functions
which are called when their values are changed from the Dashboard.
These functions are generated with the Thing and added at the end of this sketch.
#include <SPI.h>
#include <SD.h>
*/
#include <Adafruit_SCD30.h>
Adafruit_SCD30 scd30;
#define SCD30_I2CADDR_DEFAULT 0x61 ///< SCD30 default i2c address
#define SCD30_CHIP_ID 0x62 ///< SCD30 default device id from WHOAMI
#include “thingProperties.h”
#define LOG_INTERVAL 2000 // mills between logging entries (reduce to take more/faster data)
// milliseconds before writing the logged data permanently to disk
// LOG_INTERVAL write each time (safest)
#define SYNC_INTERVAL 5000 // mills between calls to flush() – to write data to the card
uint32_t syncTime = 0; // time of last sync()
#define ECHO_TO_SERIAL 1 // echo data to serial port
#define WAIT_TO_START 0 // Wait for serial input in setup()
// The analog pins that connect to the sensors
#define CH4sens A1 //CH4 sensor Vout
#define CH4ref A2 //CH4 sensor Vref
#define Vb A0 //Battery voltage
int CH4s = 0;
int CH4r = 0;
int Vbat = 0;
float CH4ppm = 0;
float CH4smV = 0;
float CH4rmV = 0;
float VbatmV = 0;
float mV = 5000;
float steps = 1024;
// for the data logging shield, we use digital pin 10 for the SD cs line
const int chipSelect = 10;
// the logging file
/////////////////////////
/* Begin Read Sequence */
/////////////////////////
void setup() {
// Initialize serial and wait for port to open:
Serial.begin(9600);
// This delay gives the chance to wait for a Serial Monitor without blocking if none is found
delay(1500);
// Defined in thingProperties.h
initProperties();
// Connect to Arduino IoT Cloud
ArduinoCloud.begin(ArduinoIoTPreferredConnection);
/*
The following function allows you to obtain more information
related to the state of network and IoT Cloud connection and errors
the higher number the more granular information you’ll get.
The default is 0 (only errors).
Maximum is 4
*/
setDebugMessageLevel(2);
ArduinoCloud.printDebugInfo();
Serial.println(“Adafruit SCD30 test!”);
// Try to initialize!
if (!scd30.begin()) {
Serial.println(“Failed to find SCD30 chip”);
while (1) { delay(10); }
}
Serial.println(“SCD30 Found!”);
// if (!scd30.setMeasurementInterval(10)){
// Serial.println(“Failed to set measurement interval”);
// while(1){ delay(10);}
// }
Serial.print(“Measurement Interval: “);
Serial.print(scd30.getMeasurementInterval());
Serial.println(” seconds”);
}
void loop() {
ArduinoCloud.update();
// Your code here
if (scd30.dataReady()){
// Serial.println(“Data available!”);
if (!scd30.read()){ Serial.println(“Error reading sensor data”); return; }
Serial.print(“Temperature: “);
Serial.print(scd30.temperature);
Serial.println(” degrees C”);
Serial.print(“Relative Humidity: “);
Serial.print(scd30.relative_humidity);
Serial.println(” %”);
Serial.print(“CO2: “);
Serial.print(scd30.CO2, 3);
Serial.println(” ppm”);
Serial.println(“”);
} else {
//Serial.println(“No data”);
}
delay(100);
// delay for the amount of time we want between readings
delay((LOG_INTERVAL -1) – (millis() % LOG_INTERVAL));
CH4s = analogRead(CH4sens); //read CH4 Vout
CH4smV = CH4s*(mV/steps); //convert pin reading to mV
CH4ppm = 1.4406*(exp(-(0.222*CH4smV)));
delay(10); //delay between reading of different analogue pins adviced.
CH4r = analogRead(CH4ref); //read CH4 Vref
CH4rmV = CH4r*(mV/steps); //convert pin reading to mV
delay(10); //delay between reading of different analogue pins adviced.
Vbat = analogRead(Vb); //read CH4 Vref
VbatmV = Vbat *(mV/steps); //convert pin reading to mV, NOT YET correcting for the voltage divider.
delay(10); //delay between reading of different analogue pins adviced.
#if ECHO_TO_SERIAL
//Serial.print(“CH4 Output Voltage: “);
//Serial.print(CH4smV);
//Serial.println(” milivolts “);
Serial.println(“Data available!”);
Serial.print(“CH4 Concentration: “);
Serial.print(CH4ppm);
Serial.println(” ppm “);
#endif //ECHO_TO_SERIAL
co2 = scd30.CO2;
temp = scd30.temperature;
humidity = scd30.relative_humidity;
ch4 = CH4ppm;
}
Appendix G: Wiring Diagrams
Appendix G.1: Concept 1 Wiring Diagram
Appendix G.2: Concept 2 Wiring Diagram
Appendix G.3: SCD30 Individual Wiring Diagram
Appendix G.4: K30 Individual Wiring Diagram
Appendix G.5: DHT22 Individual Wiring Diagram
Appendix G.6: NGM2611 – E13 Individual Wiring Diagram
Appendix H: Sensor Library Directions
Appendix H.1: Directions
To start:
- Open Arduino Application
- Go to Sketch -> Include Library -> Manage Library
- Lookup needed library
- Install the main Library and if it has any dependencies install them as well
You can then use the examples or write your own code to work with the sensors using these Libraries!
For K30:
- Download the First Cypress zip in teams
- go to Sketch -> Include Library -> Add Zip
- Add the First Cypress zip
Links to instructions and libraries:
DHT22: https://www.makerguides.com/dht11-dht22-arduino-tutorial/
SCD 30: https://github.com/adafruit/Adafruit_SCD30
K30: https://create.arduino.cc/projecthub/alfred333/co2-monitoring-with-k30-sensor-86f6d9
Appendix H.2: Needed Libraries for SCD30
- Adafruit SCD30
- Adafruit Unified Sensor
- Adafruit BusIO
- Adafruit SSD1306
Appendix H.3 Needed Libraries for DHT22
- DHT sensor library
- Adafruit Unified Sensor