Evaluation of Machine Learning Techniques for Crop Yield Prediction

Abstract

The agricultural segment is a major supporter of the Indian economy as it represents 18% of India's GDP, and it gives work to half of the nation's work power. The farming segment is required to satisfy the expanding need for food because of the increasing populace. Therefore, to cater to the ever-increasing needs of people of the nation, yield prediction is done prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crops prior to cultivating. There are multiple parameters that affect the yield of crops like rainfall, temperature, fertilizers, pH level, and other atmospheric circumstances. Thus, considering these factors, the yield of a crop is thus hard to predict and becomes a challenging task. In this chapter, the dataset of different states producing different crops in different seasons is considered; further, after preprocessing the data, the authors applied machine learning algorithms, and their results are compared.