Predicting Crop Yield Using Deep Learning and Remote Sensing

Abstract

The art of predicting crop production is done before the crop is harvested. Crop output forecasts will help people make timely judgments concerning food policy, prices in markets, import and export laws, and acceptable warehousing. It is possible to reduce the socioeconomic effects of crop loss brought on by a natural disaster, such as a flood or a drought, and to organize humanitarian food assistance. It has been suggested that deep learning, which lets the model to automatically extricate features and learn from the datasets, could be useful for predicting agricultural yields. This review helps to understand that how vegetation indices and environmental variables affect agricultural output by revealing gaps in our understanding of deep learning methodologies and remote sensing data in a specific area. Literature review of 2011-2022 has been collected from different databases and sites and analyzed to meet the aims of this review. The study mainly focused on the benefits of machine learning, and remote sensing for forecasting crop yield. The most often employed form of remote sensing is satellite technology, namely the usage of the Moderate-Resolution Imaging Spectro radiometer. Vegetation indices referred to as the most often employed attribute for forecasting crop yield, according to the results. This review compares all these techniques and pros and cons related to them.