Empirical Survey Analysis For Crop Yield Prediction & Identification Of Factors Affecting Yield Gaps

Abstract

About 70% of India’s economy is involved in the agriculture sector to live their lives and contributed to the GDP of the country. The
Crop yield information along with the environmental change estimate will be useful for the agriculturalist to decide on price policies
prior to harvesting the food source. It establishes a requirement for the prediction model, which precisely determines the harvest
conditions, crop varieties, and agricultural yield. In literature, numerous crop prediction methods were devised to estimate crop
production in the agricultural field & each technique has its potential in terms of yield forecasting. This review article provides a detailed
analysis of the utilized approaches in the literature for the prediction of crop production as well as a discussion on the identification of
concerns related to the yield gaps of crops. The discussed approaches were classified based on the application of different strategies,
such as Machine learning methods, Deep learning methods, Data mining techniques, vegetative indices, fuzzy logic, and hybrid
methods. The study was analyzed based on performance metrics, year of publication, datasets employed, software used for
experimentation, and performance attained using various methods and highlights the research gaps of the respective method along with
the future direction.