Chaiyana, A.; Hanchoowong, R.; Srihanu, N.; Prasanchum, H.; Kangrang, A.; Hormwichian, R.; Kaewplang, S.; Koedsin, W.; Huete, A. Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand. Sustainability2024, 16, 2260.
Chaiyana, A.; Hanchoowong, R.; Srihanu, N.; Prasanchum, H.; Kangrang, A.; Hormwichian, R.; Kaewplang, S.; Koedsin, W.; Huete, A. Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand. Sustainability 2024, 16, 2260.
Chaiyana, A.; Hanchoowong, R.; Srihanu, N.; Prasanchum, H.; Kangrang, A.; Hormwichian, R.; Kaewplang, S.; Koedsin, W.; Huete, A. Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand. Sustainability2024, 16, 2260.
Chaiyana, A.; Hanchoowong, R.; Srihanu, N.; Prasanchum, H.; Kangrang, A.; Hormwichian, R.; Kaewplang, S.; Koedsin, W.; Huete, A. Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand. Sustainability 2024, 16, 2260.
Abstract
Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aim 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 of 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 variance inflation factor (VIF). Temperature condition index (TCI), normalized difference vegetation index (NDVI), land surface temperature (LSTnight), 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 would 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
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
