Abstract
Accurate and timely information about production estimates of wheat is useful for policymakers and government planners. The traditional methods for yield forecasting are labor insensitive and time-consuming therefore remote sensing is an effective approach for precise yield forecasting. The study was planned to develop a comprehensive framework for yield forecasting and to assess interannual yield variability in semi-arid regions. For wheat area classification, the peak season Landsat-8 satellite images were acquired, and Top of Atmospheric (TOA) correction was performed. The ground-truthing points of 100 farms were collected from the study area for the training of algorithms. The eight machine learning algorithms were used tune and tested using 10-k fold cross-validation and the best model was used for land cover classification of wheat. For yield forecasting, the temporal normalized difference vegetation index (NDVI) and land surface temperature (LST) were derived for the wheatgrowing season from November to April. A Principal Component Analysis (PCA) was used to variable selection and then Least Absolute Shrinkage Selection Operator (LASSO) analysis was performed to develop coefficients of the yield forecasting model. The developed model was further used in yield forecasting of 10 years (2008-2018) in four semi-arid regions. The predicted yield was compared with Crop Reporting Service (CRS), Pakistan department. The results of all machine learning algorithms showed an accuracy of 88% to 96%, however, the Random forest algorithm showed higher accuracy, which was further used for classification. The wheat estimated area of 6.9% was less than reported by CRS. For interannual variability, the relationship of observed (CRS) and predicted yield of 10 years showed a close relation with R2 ranged from 0.69 to 0.75 in the semi-arid region of Punjab, Pakistan. It was concluded that machine learning algorithms can be used as novel tools for yield forecasting and assessment of interannual yield variability.
Hafiza Hamrah Kanwal, Ishfaq Ahmad, Ashfaq Ahmad, Yongfu Li. (2021) YIELD FORECASTING AND ASSESSMENT OF INTERANNUAL WHEAT YIELD VARIABILITY USING MACHINE LEARNING APPROACH IN SEMIARID ENVIRONMENT, Pakistan Journal of Agricultural Sciences, Volume 58, Issue 2.
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