Comparative Analysis of Crop Yield Prediction Using Machine Learning

Moreover half of the population of India relies on agriculture for a living, making it the foundation of the nation’s economy. Agriculture’s future viability is now being threatened by weather, temperature, and other environmental variables. One use of machine learning (ML) is the Crop Yield Prediction (CYP) decision support tool, which provides suggestions about which crops to cultivate and what to perform during the crop’s growth season. Multi-source data for soils, climates, and remotely sensed vegetation indices particular to each site are needed for yield prediction. It is difficult to cope with model uncertainty when using complicated data-model fusion algorithms for crop growth monitoring and yield prediction Several aspects must be considered while developing an accurate and effective model for agricultural yield estimation depending on climate, crop illness, crop classification based on development phase, and other considerations, several research proposals for agricultural development have been made. This study explores severalML techniques for estimating agricultural yields and offers a thorough evaluation of the effectiveness of the methods and we found that the accuracy with Random Forest is higher i.e. 99.31% among all.