Creeping Bentgrass Yield Prediction With Machine Learning Models

Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (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 pote...

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Autores principales:	Qiyu Zhou, Douglas J. Soldat
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	turfgrass nitrogen management yield prediction machine learning random forest Plant culture SB1-1110
Acceso en línea:	https://doaj.org/article/052d843007ba4426b2a233b9ad949dcd
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id	oai:doaj.org-article:052d843007ba4426b2a233b9ad949dcd
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spelling	oai:doaj.org-article:052d843007ba4426b2a233b9ad949dcd2021-11-04T09:31:45ZCreeping Bentgrass Yield Prediction With Machine Learning Models1664-462X10.3389/fpls.2021.749854https://doaj.org/article/052d843007ba4426b2a233b9ad949dcd2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpls.2021.749854/fullhttps://doaj.org/toc/1664-462XNitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (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 temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, the data were collected from two putting greens where the turfgrass received 0 to 1,800 round/week traffic rates, various irrigation rates to maintain the soil moisture content between 9 and 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. The temperature and relative humidity were the most important weather factors. Including NDRE improved the prediction accuracy of the model. 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 upon the evaluation of the model. This represented a large improvement over the existing growth prediction model (R2 = 0.01). However, the machine-learning models created were not able to accurately predict the 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.Qiyu ZhouDouglas J. SoldatFrontiers Media S.A.articleturfgrassnitrogen managementyield predictionmachine learningrandom forestPlant cultureSB1-1110ENFrontiers in Plant Science, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	turfgrass nitrogen management yield prediction machine learning random forest Plant culture SB1-1110
spellingShingle	turfgrass nitrogen management yield prediction machine learning random forest Plant culture SB1-1110 Qiyu Zhou Douglas J. Soldat Creeping Bentgrass Yield Prediction With Machine Learning Models
description	Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (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 temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, the data were collected from two putting greens where the turfgrass received 0 to 1,800 round/week traffic rates, various irrigation rates to maintain the soil moisture content between 9 and 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. The temperature and relative humidity were the most important weather factors. Including NDRE improved the prediction accuracy of the model. 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 upon the evaluation of the model. This represented a large improvement over the existing growth prediction model (R2 = 0.01). However, the machine-learning models created were not able to accurately predict the 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.
format	article
author	Qiyu Zhou Douglas J. Soldat
author_facet	Qiyu Zhou Douglas J. Soldat
author_sort	Qiyu Zhou
title	Creeping Bentgrass Yield Prediction With Machine Learning Models
title_short	Creeping Bentgrass Yield Prediction With Machine Learning Models
title_full	Creeping Bentgrass Yield Prediction With Machine Learning Models
title_fullStr	Creeping Bentgrass Yield Prediction With Machine Learning Models
title_full_unstemmed	Creeping Bentgrass Yield Prediction With Machine Learning Models
title_sort	creeping bentgrass yield prediction with machine learning models
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/052d843007ba4426b2a233b9ad949dcd
work_keys_str_mv	AT qiyuzhou creepingbentgrassyieldpredictionwithmachinelearningmodels AT douglasjsoldat creepingbentgrassyieldpredictionwithmachinelearningmodels
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Creeping Bentgrass Yield Prediction With Machine Learning Models

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