Data-driven management of greenhouse high-wire fruiting vegetables using 3D scanning

Original paper: Ohashi, Y., Y. Ishigami, and E. Goto. 2020. Monitoring the growth and yield of fruit vegetables in a greenhouse using a three-dimensional scanner. Sensors. 20: 5270. doi:10.3390/s20185270

Controlled environment agriculture (CEA) for food crop production allows high productivity and efficient resource use. Applications of CEA have been expanding rapidly worldwide. However, the current limitation that slows the expansion is shortage of experienced human workers. In addition, increasing costs of labor are serious problem that slows the potential growth of CEA. More automation is needed to lower the labor input in various tasks of crop management. Automated monitoring of plant growth and morphology (structure) can allow growers to make more science-based, data-driven decisions for crop management and minimize labor, energy, water, and other resource usage.

Three-dimensional scanning technologies have developed rapidly to create a digital twin (model) for various measurements and computational analyses. There are affordable technologies and various data processing software.  The authors of this paper are a team of horticultural engineers at Chiba University (Matsudo, Japan) and demonstrated usage of 3D scanning for greenhouse crop management. Their specific objective was to estimate key metrics of plant growth and productivity, such as leaf area, plant height, biomass, and fruit yield for three major fruiting crops grown in greenhouse (tomato, cucumber, and sweet (bell) pepper).

The tests were conducted in a greenhouse located in Chiba University. Different ages (sizes) of tomato, cucumber and pepper plants of selected cultivars are used for the tests. A hand-held 3D scanner (DPI-8X, Opt Technologies, Tokyo) was used to scan individual plants as well as a group (canopy) of six plants grown using a typical soilless cultivation method with a common density applied in commercial greenhouses. The scan generated a set of many data points with xyz coordinates (point cloud) which are then converted to a digital ‘surface model’ for finding leaf area and a digital ‘solid model’ for finding fruit yield. The leaf areas were then used for finding biomass (dry weight) of leaves and whole plants using predetermined correlation between these variables. However, accuracy of estimation of leaf area and plant biomass for tomato was less than that for cucumber and pepper, due to more complex leaf morphology (compound leaves with many leaflets) of tomato than the other crops.

The authors also estimated the leaf areas at different heights of canopy, demonstrating the capacity of quantifying leaf distributions inside the canopy.  This information is especially useful for finding levels of available light at various heights inside the canopy. The information of light distribution in the canopy will help growers use more data-driven crop management practices including leaf pruning, plant density management, so that light use efficiency can be maximized.

Yield prediction based on 3D scans showed reasonable correlation with measured yield (R2 > 0.7) for tomato and pepper. The RGB data obtained by the scanner was used for detecting ripe fruit, whose point cloud data were converted to sold (voxel) models. Fruit volume estimated by the solid model was then converted to fruit mass (grams) by a predetermined fruit density (ratio of mass and volume). Cucumber was not scanned for fruit analysis as fruit shapes were extracted based on colors (red or yellow) and fruit color of cucumbers is green (difficult to distinguish from leaf images).  Similarly, immature (unripe) fruits of tomato and pepper were not extracted either for the same issue of color-based recognition.  Overall, fruit yield estimate was least accurate compared with leaf area and other metrics. The inaccuracy is based on the difficulty to scan the opposite side of fruit.

Although dense canopy and overlapping leaves add difficulty in achieving high accuracy, this preliminary effort to demonstrate the potential use of a 3D scanner for crop management was successful. Coefficient of determination (R2) was almost always high (>0.8) except LAI for tomato.  The tools (hardware and software) that they used for scanning and data conversions were commercially available. Although more work will need to be done to improve the accuracy and perhaps to streamline the logistics of measurement and data processing, this is a valuable demonstration of what we can do with a simple handheld 3D scanner or similar tool in future greenhouse crop production.

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production


Hemming, S., de Zwart, F., Elings, A., Righini, I., & Petropoulou, A. (2019). Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop Production. Sensors, 19(8), 1807. 


As the global population keeps growing, so does the demand for healthy, fresh, and accessible food. Greenhouse environments contribute an important function in providing fresh food at a high production rate while maximizing resource efficiency. The greenhouse industry has stumbled over the obstacle of finding enough skilled staff to manage these crop production systems. Modern high-tech greenhouses are equipped with process computers, and, to add more automated control, greenhouse climate and crop models have been developed. The use of Artificial Intelligence (AI) has reached breakthroughs in many areas and has not been used yet to control climate and irrigation and make crop management decisions for growing a greenhouse crop autonomously. An international challenge was conducted on autonomous greenhouses in 2018 at the high-tech research greenhouses of Wageningen University and Research in cooperation with five multi-disciplinary international teams to combine the use of modern AI algorithms and the greenhouse crop production of cucumbers cultivar “Hi-Power”. This article aims to describe the results of the teams in terms of optimizing crop yields and net profit using state-of-the-art AI algorithms for cucumber production.


The experiments were conducted in six identical greenhouse compartments equipped with standard motor movers. Cucumber seedlings were grown in rockwool substrate cubes and placed on slabs on hanging gutters. The five participating teams were: Sonoma, iGrow, deep_greens, The Croperators, and AiCu. These teams were able to remotely control the motors’ activity in their greenhouse compartment by using their own original AI algorithm, varying in design and techniques. As part of the competition, a sixth team led by Dutch growers controlled a greenhouse compartment and served as a reference. The competing teams used their original AI algorithms to regulate the climate and irrigation setpoints through a central computer and operated in the greenhouse compartments accordingly. Standard sensors set up in the greenhouse compartments continuously measured data and calculations were made based on these and digitally sent back to the teams. Teams were also allowed to install additional sensors in their compartments at the start of the experiment. Three harvest quality parameters were established: A: no defects (>375g), B: defects in shape, color, or others (300-374g), and C: less than 300g per fruit. Harvest data was measured manually by greenhouse staff and digitally sent to the teams.

The teams were judged based on three criteria: Sustainability, based on resource use efficiency (20%), Net profit, based on the number of fruits harvested per price of the fruit and category (50%), and AI algorithm, based on originality and efficacy of the algorithm (30%). Two models were used to analyze and compare the different AI algorithm approaches since the operation resulted in differences in cropping, climate, irrigation strategies, harvest yields, and resource use efficiencies. However, the combined model did not represent the presence and effects of pests and disease. The combined model was carried out to compare the calculated output as the predicted fresh cucumber yield per greenhouse compartment versus the realized cucumber yield in the same greenhouse compartment to verify the models. Additionally, model calculations were carried out applying the cropping strategy, lighting strategy, and climate strategy of other teams for each greenhouse compartment to predict the changes in yield and compare these.

Figure 2 – Scheme of data exchange (Hemming et al., 2019)

Results and Discussion

Out of all the teams, including the Dutch growers, the Sonoma team resulted in the highest production of cucumbers and consistently did this throughout the experiment. They predicted that by having a high daily light integral, they would be able to achieve a greater harvest, as they did, focusing their algorithms on this. Other teams decided to increase daily light integral and maintain a low carbon dioxide concentration (The Croperators) or have lower daily light integrals in the beginning to minimize fruit abortion (Dutch growers) which resulted in lower yields but led to a greater understanding of important production factors. All teams began the experiment with a relatively low amount of carbon dioxide, where most teams increased their concentration throughout the experiment and later diminished their dosage towards the end, the Sonoma team decided to increase their carbon dioxide concentration continuously throughout the total cropping period.

The goal of maximizing crop management is finding the optimum combination of the climate, lighting, and cropping strategies. This can be achieved by attaining the greatest number of fruits per area without hindering photosynthesis. Light, for example, is the foundation for healthy growth. For this particular experiment, having higher light integrals resulted in higher yields for the teams. As predicted with the combined models, the teams iGrow, AiCu, and growers could have possibly achieved a higher yield if they would have used the lighting strategies of either team Sonoma or The Croperators. Overall, most of the teams were able to obtain a decent production using a low amount of resources while reaching a net profit close to or better than manual commercial growers.

The goal of this experiment was to see how AI algorithms would become better at making crop management decisions and their effects on the greenhouse climate and production. At the end of the cucumber crop’s growth period, all the algorithms developed for this experiment resulted in establishing a successful greenhouse environment. As this was the first successful experiment on remotely controlling a greenhouse for cucumber production using artificial intelligence algorithms, the authors demonstrated that AI algorithms can compete, and even outperform, experienced manual growers. Artificially assisted or managed greenhouse systems could be a useful tool to grow crops where there is limited knowledge. In the future, more developments and studies in this area will be needed to make AI an alternative for trained and skilled greenhouse workers and growers, which to this day, greenhouses cannot function without.

Design for an Improved Temperature Integration Concept in Greenhouse Cultivation

Original paper: O. Ko¨rner *, H. Challa Design for an improved temperature integration concept in greenhouse cultivation Farm Technology Group, Department of Agrotechnology and Food Sciences, Wageningen University, Mansholtlaan 10, 6708 PA Wageningen, The Netherlands Received 22 July 2002; received in revised form 15 November 2002; accepted 28 December 2002.

     Heating energy represents more than two-thirds of a typical greenhouse total energy consumption. Is currently well known that the average day and night temperatures are what controls how fast plants develop. As the temperature increases, crops develop much faster, but there is a significant cost associated, which typically leads to an increase in energy consumption. To mitigate the cost and become more efficient in the control environment production, an approach to improve temperature integration concept could play an essential role in energy savings. Temperature Integration Concept is based on the ability of crops to tolerate temperature deviation from their biological set points. The integration concept manipulates temperature, aiming to be compensated within a pre-set period without having adverse effects on plant growth.

     Theoretically, a crop with more dynamic and flexible temperature boundaries could potentially play an important role, so this study aimed to improve the temperature integration concept by introducing dynamic temperature constraints. A modified temperature integration procedure was designed combining the usual long-term temperature average over several days and fixed boundaries for daily average temperature with short-term temperature averages over 24 hours with a very flexible temperature limit. The overall idea is based on a concept called the Freedom for temperature fluctuations. This concept allows the temperature to freely fluctuate due to the environment without being controlled by heating or ventilation. Temperature fluctuation increases with longer averaging period and increasing temperature bandwidth, which allows longer periods of several days, which enables compensation of warm or cold periods resulting in higher energy savings.

     The proposed regimen for temperature integration was performed by modeling and simulation techniques (MATLAB version 6.0) using tomato as a model crop. Variables such as air temperature, outside radiation, relative humidity, and CO2 measurements were input in the model with a fixed time of 5 min over one year. Measurements such as setpoints for heating, ventilation and CO2 concentrations were calculated with a climate control model (CCM) which provide enough information for calculating relative humidity, air temperature, energy consumption, and natural gas consumption. For accuracy, energy loses where also consider into the model. Two reference temperature regimens were used for comparison:  BP= commercial standards, setpoint increase linearly, and a Bpfix= night and daytime heating and ventilation temperature setpoints were fixed (uncommon practice). The heating setpoints were 18, and 19 °C and ventilation set points were 19 and 20 °C for night and day, respectively. The weather prediction was also used for providing data into the model simulation. Validations of the CCM model was performed in four semi-commercial Venlo-type greenhouse compartments.

     Two temperature integration regimens were model by the Autor: RTI (regular temperature integration) and MTI (modified temperature integration) both with a bandwidth of +/- 2, +/- 4 and +/- 6. The modified regime model (MTI) resulted in more energy saved when compared with regular temperature integration model (RTI) and the BP controls. Energy-saving increased with temperature bandwidth in all cases evaluated. Fluctuation during a cold time (winter) was observed. Overall, yearly greenhouse energy saving increased by up to 23% compared with the BP regime (temperature with a bandwidth of +/- 6 C). Compared with regular temperature integration energy-saving increased relatively with 14%. Interestingly, the setpoint for relative humidity profoundly influenced energy-saving suggesting further focus in future evaluations. When evaluating the different temperature dose-response data, they observe than an increase in the duration of maximum and minimum temperature increase energy saving and gross photosynthesis of tomato plants, which can be traduced to more photosynthetic efficiency. In conclusion, the conceptual design for advance temperature integration control seems to be promising for energy reduction. The distinction between short- and long-term processes in temperature integration lead to an increase in energy savings. A more advanced flexible humidity control concept could probably help to decrease energy consumption further since the highest energy saving was achieved when no humidity control was used.