Soil and crop interaction analysis for yield prediction with satellite imagery and deep learning techniques for the...

Crop yield is a significant factor in world income and poverty alleviation as well as food production through agriculture. Conventional crop yield forecasting approaches that employ subjective estimates including farmers' perceptions are imprecise and contain high variability over large farming areas, particularly in areas where data is limited. The improvement of data capture techniques in the last few years especially from high-resolution sensors and Deep Learning (DL) have enhanced the quality and scope of agricultural data to assist policymakers and administrators. Mostly researchers used various techniques for independently forecasting soil fertility and crop yield. In image processing, Sentinel-2 is one technique that enhances agriculture, especially in analyzing crop health and type of soil prediction. Using the Normalized Difference Vegetation Index (NDVI) for processing the red and near-infrared bands allows computation ranges between -1 and 1. The values are higher than 0.7, the crops are in good health, or the values are less than 0.3 means crops are under stress. Therefore, information about soil types and NDVI data provide the most elaborate recommendations regarding agriculture. This is done through executing superior picture analysis and verification for precise errors below 5 %. It also develops a rainfall-runoff forecast through a Convolutional Neural Network approach. Our proposed methodology attains an average accuracy of about 98.7 % compared with traditional approaches average is about 85 %-90 %. A high-accuracy model of this type facilitates a spatial and temporal resolution of five days and improves farmers' irrigation process since it offers more accurate agronomic decisions. This research may lead in the agriculture and deep learning applications for economic and societal improvement. Application of artificial intelligence in agriculture synchronizes relevancy from satellite imagery making precision smart and boosting food productivity by 20 % with better utilization of resources.