Wheat Crop Field and Yield Prediction using Remote Sensing and Machine Learning

Agriculture plays an important role in the growth of a country's economy. Crop area and yield predictions using machine learning are important investigation domains in current research fields. Wheat is the most important food crop in Pakistan which is cultivated in the Rabi season. Weather conditions, Remote Sensing (RS) data, and Machine learning (ML) technologies can be used to forecast wheat yield before actual harvesting to assist the management of wheat production, trade, and storage. In this paper, a supervised ML based framework is proposed that extracts features/Vegetation Indices (VIs) including Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Red Edge Normalized Difference Vegetation Index (RENDVI), and Normalized Difference Moisture Index (NDMI) from Sentinel-2 Satellite images and contributes for: estimation of wheat area, and identification of most effective VIs in wheat area estimation, prediction of wheat yield, and identification of most effective VIs and meteorological parameters in wheat yield prediction. In the initial experimental setup, good performance output obtained using the Random Forest (RF) machine learning algorithm therefore in this framework RF machine learning algorithm is focused on wheat area estimation and generation of Land Use Land Cover (LULC) maps which is capable of estimating area with an accuracy of 84%, consumer's accuracy of 81 %, producer's accuracy of 83% and kappa statistics of 0.80. LULC maps are used for wheat yield prediction. Multivariate regression forward stepwise technique is applied for yield prediction and selection of effective VIs and meteorological parameters. The adjusted coefficient of determination (R2) between reported and predicted yield found 0.84 with an error of 46.14 Kg/ha for yield prediction.