CROP PRODUCTION PREDITCTION

Over 50% of India’s population relies on agriculture for their survival, making it the foundation of the Indian economy. Weather, climate, and other environmental changes have evolved into a serious threat to the continued health of agriculture. The decision-support tool for crop production prediction (CPP), which includes supporting decisions about which crops to produce and what to do during the growing season of the crops, is provided by machine learning (ML), which plays a vital part in this process. The current study examines a systematic review that extracts and synthesises the CPP features, and it also explores a number of approaches that have been created to examine crop production prediction using artificial intelligence techniques. Reduction in relative error and lower crop yield prediction accuracy are the Neural Network’s main drawbacks. Similar to this, supervised learning algorithms failed to recognise the nonlinear relationship between input and output variables, which presented a challenge during the selection, grading, or sorting of fruits. To establish an accurate and effective model for crop classification, including crop yield estimation based on weather, crop disease, classification of crops based on the growing phase, etc., numerous investigations were advised.Prior to harvest, crop output predictions would aid farmers and policymakers in deciding on the best course of action for marketing and storage.