Wheat Yield Prediction with Machine Learning based on MODIS and Landsat NDVI Data at Field Scale

Authors : Murat Güven TUĞAÇ, A. Murat ÖZBAYOĞLU, Harun TORUNLAR, Erol KARAKURT

Pages : 172-184

Doi:10.30897/ijegeo.1128985

View : 19 | Download : 7

Publication Date : 2022-12-25

Article Type : Research Paper

Abstract :Accurate estimation of wheat yield using Remote Sensing-based models is critical in determining the effects of agricultural drought and sustainable food planning. In this study, Winter wheat yield was estimated for large fields and producer fields by applying Normalized Difference Vegetation Index insert ignore into journalissuearticles values(NDVI); based linear models insert ignore into journalissuearticles values(simple linear regression and multiple linear regression); and Machine Learning insert ignore into journalissuearticles values(ML); techniques insert ignore into journalissuearticles values(support vector machine_svm, multilayer perceptron_mlp, random forest_rf);. In this study, depending on the ecological zone, crop sampling was carried out from 380 rainfed parcels where wheat was planted. On the basis of crop development periods insert ignore into journalissuearticles values(CDP);, the highest correlation between NDVI and yield occurred during the flowering period. In this period, coefficient of determination insert ignore into journalissuearticles values(R2); was 63% in TIGEM fields and 50% in producer fields for MODIS data, and 61% and 65% for Landsat data, respectively. In TIGEM fields, the best prediction performance was obtained with the MLP model for MODIS insert ignore into journalissuearticles values(RMSE:0.23-0.65 t/ha); and Landsat insert ignore into journalissuearticles values(RMSE: 0.28-0.64 t/ha);. On the other hand, the highest forecasting accuracy was acquired with the SVM model in producer fields. The RMSE values ranged from 0.74 to 0.80 t/ha for MODIS and 0.51 to 0.60 t/ha for Landsat 8. The error value obtained with MODIS was approximately 1.4 times higher than the Landsat 8 data in producer fields. For yield estimation, the best estimation can be made 4-6 weeks before the harvest. In regional yield estimations, satellite-based ML techniques outperformed linear models. ML models have shown that it can play an important role in crop yield prediction. In crop yield estimation, it is a priority to consider the impact of climate change and ecological differences on crop development.
Keywords : Crop yield prediction, Remote Sensing, Machine learning, NDVI, Wheat