Analysis of Five Mathematical Models for Crop Yield Prediction

Abstract

A review of mathematical models used for the prediction of crop yield has been presented. Though there are many other non-mathematical techniques also available for the purpose, but mathematical modeling used for any real world problem opens many perspectives and provides many possible solu-tions of the problem for the betterment of human race. Five mathematical models (remote sensing followed by mathematical modeling, fuzzy logic based model, multiple linear regression mechanistic model, linear algebra based descriptive model and growth model based on TOMGRO mechanistic model) have been extracted and analyzed critically. These models are based on different mathematical concepts and techniques (non-linear optimization, fuzzy logic, linear predictor functions, linear algebra and differential calculus) covering a wide range of mathematical modeling. The general forms of these models have been derived. Average accuracy of presented models was found to be in the range 90% - 99% that strongly favors the optimum usage of mathematical modeling for crop yield forecasting processes. The section giving gaps and future research prospects presents the comparative analysis of the models. Development of new and moderated mathematical models for more precision and better accuracy has also been suggested using new mathematical techniques and hybridization or modifying the existing models.