AUTHOR=Zhou Qiyu , Soldat Douglas J. 

TITLE=Creeping Bentgrass Yield Prediction With Machine Learning Models

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.749854

DOI=10.3389/fpls.2021.749854

ISSN=1664-462X

ABSTRACT=Nitrogen (N) is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use N to maintain a sub-maximal growth rate. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the currently existing turf growth prediction model only takes into account temperature, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm for estimating short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, data were collected on two putting greens where the turfgrass received 0 to 1800 round/week traffic rates, various irrigation rates to maintain soil moisture content between 9 to 29%, and N fertilization rates of 0 to 17.5 kg ha-1 applied biweekly. The RF model agreed with the actual clipping yield collected from the experimental results. Temperature and relative humidity were the most important weather factors. Including NDRE improved the model's prediction accuracy. The highest coefficient of determination (R2) of the RF model was 0.64 for the training dataset and was 0.47 for the testing data set when evaluating the model. This represented a large improvement over an existing growth prediction model (R2=0.01). However, the machine-learning models created were not able to accurately predict clipping production at other locations. Individual golf courses can create customized growth prediction models using clipping volume to eliminate the deviation caused by temporal and spatial variability. Overall, this study demonstrated the feasibility of creating machine-learning-based yield prediction models that may be able to guide N fertilization decisions on golf course putting greens and presumably other turfgrass areas.