Author: Danneberger

Potential for New Turfgrass Technology in Management and Teaching

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Data collection and interpretation has played a role in turfgrass management for over 60 years.  During this span of time has evolved toward more complex data sets that have become a greater asset for managers to make informed management decisions and predictions.  Some of the earliest data collection consisted of weather, primarily temperature collection.  Some of the earliest temperature data was recorded and converted into growing degree-days (GDD).

Growing degree-days have been used to predict plant growth and pest development for the purpose for more efficient timing and use of control products and methods.  Gathering and compiling weather data has led to the development of turfgrass disease prediction models.  Over the years, or should I say decades I have been involved in the original development of several disease prediction models for the United States and globally.  Several of these disease prediction models are displayed on Syngenta’s Greencast website (https://greencastonline.com).

Complex data collection has lead to better moisture and irrigation control practices.  This year has seen new monitoring devices for soil moisture both on greens and fairways.  New technologies in fairway moisture monitoring for example has led to massive data generation to predict soil moisture levels, which leads to better irrigation efficiency.

As data collection grows the term big data is used.  Big data refers to large and complex datasets that are too challenging to process using traditional data processing tools. It includes structured, semi-structured, and unstructured data from various sources such as social media, sensors, and a wide range of other sources.

Artificial Intelligence Use

Emerging technology like artificial intelligence uses portions of versions of data to help us aid in solving problems.  This year we have been looking at how artificial intelligence can be used in turf

There are various artificial intelligence systems available, but for this demonstration I am using Chat GPT.   Initially I have been using it for turfgrass weed identification and control.  In the first example, I took a photograph of the weed of interest.  After uploading the picture into Chat GPT I asked what the weed was.  It correctly identified it as black medic.  The program proceeded to provide a description of the weed, conditions favorable for weed invasion and also cultural and chemical management of the weed.  These stages are represented in the screen captures provided.

In the second series of pictures the weed I asked ChatGPT to identify was yellow rocket.  ChatGPt misdiagnosed the weed and continued to mis identify on successive attempts.  Continued evaluation and testing is needed to determine the best ways to enhance the effectiveness of Chat GPT

Iphone apps with the ChatGPT highlighted

Black Medic

Photo taken with phone

Weed pic uploaded into ChatGPT

Asked the question to ChatGPT what is this weed

Description of weed

Description of black medic given

Black medic control

Herbicide control of black medic

Yellow Rocket

Misdiagnosed

In this case chatGPT misdiagnosed yellow rocket as velvetleaf

Interaction with chatGPT over misdiagnosis

Putting Green Grain: Brushing Study

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Introduction

When turfgrass growth becomes horizontal the leaves begin to lie in various directions.  We call this grain.  Often times with grain turfgrass leaves are sporadically coarse in nature contributing to a general roughness to the turf.  Grain that occurs on putting green is considered negatively in that it impacts putting green consistency.  If you are putting with the grain (leaf blades laying away from you) the ball will tend to roll further or be “faster” than if you are putting against the grain (the leaf blades are laying toward you) which will be a much slower putt.  

Similarly, golfers may read a putt by looking at which way the grain is laying between the golf ball and the hole.  If the grain is toward the golfer the turf may appear a little darker green or conversely if it is laying away from the golfer the turf may be a little lighter color.  Grain can impact the amount of break in a putt.

Grain is often associated with the growth habit of the turfgrass species.  For example, creeping bentgrass is prostrate in growth while  annual bluegrass grows more upright.  Bermudagrass greens including the ultradwarf varieties frequently develop grain.  Grainy patches develop which is contrasted here between the green areas and lighter green patches. The patches or ares that develop on a bermudagrass green are often described as grain, but the cause may be due in part to genetic mutations.

Mechanical practices to remove grain and improve texture are primarily through mowing practices (frequency, height, direction), verticutting, groomers, brushing, and topdressing.  The goal is to get the turfgrass plants to grow vertically or “up-right” thus removing grain and improving density and texture of the turf.  

Results

For the last several years we have looked at the impact of brushing, including the evolution in brushing equipment on removing grain and improving putting green turf health.  In our field day presentation we will look at relatively low cost methods of brushing.  We are studying the impact of the brushing units on plant stress, green speed and overall quality, which is primarily the reduction in grain.