Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand

Akkarapon Chaiyana, Ratchawatch Hanchoowong, Neti Srihanu, Haris Prasanchum, Anongrit Kangrang, Rattana Hormwichian, Siwa Kaewplang (), Werapong Koedsin and Alfredo Huete
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Akkarapon Chaiyana: Department of Civil Engineering, Faculty of Engineering, Maha Sarakham University, Kantharawichai District, Maha Sarakham 44150, Thailand
Ratchawatch Hanchoowong: Department of Civil Engineering, School of Engineering and Industrial Technology, Mahanakorn University of Technology, Bangkok 10530, Thailand
Neti Srihanu: Faculty of Engineering, Northeastern University, Muang District, Khon Kaen 40000, Thailand
Haris Prasanchum: Faculty of Engineering, Rajamangala University of Technology Isan, Khon Kaen Campus, Khon Kaen 40000, Thailand
Anongrit Kangrang: Department of Civil Engineering, Faculty of Engineering, Maha Sarakham University, Kantharawichai District, Maha Sarakham 44150, Thailand
Rattana Hormwichian: Department of Civil Engineering, Faculty of Engineering, Maha Sarakham University, Kantharawichai District, Maha Sarakham 44150, Thailand
Siwa Kaewplang: Department of Civil Engineering, Faculty of Engineering, Maha Sarakham University, Kantharawichai District, Maha Sarakham 44150, Thailand
Werapong Koedsin: Faculty of Technology and Environment, Phuket Campus, Prince of Songkla University, Phuket 83120, Thailand
Alfredo Huete: School of Life Sciences, University of Technology Sydney, Sydney, NSW 2007, Australia

Sustainability, 2024, vol. 16, issue 6, 1-16

Abstract: Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior to harvest at the province scale for fourteen provinces in Thailand’s Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and the variance inflation factor (VIF). The temperature condition index (TCI), the normalized difference vegetation index (NDVI), land surface temperature (LST nighttime ), and mean temperature (Tmean) were the resulting variables in the prediction model with a p -value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root-mean-square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region will be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation.

Keywords: decision support tools; machine learning; remote sensing; climatic data; predictive model; province scale (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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