A Machine Learning Approach to Simulating Farmers’ Crop Choices for Drought Prone Areas

Abstract

The backbone of the Indian economy is based on the agriculture sector, which provides the elementary ingredients to humanity and raw material for industrialization. Climate and other environmental changes have become a major threat in the agriculture field. The selection of the crop due to the environmental changes is the main challenge for the farmers. Therefore, a machine learning approach gives the vital role of selection of the crop which is suitable for the drought-prone areas. We observed that in the present condition in Maharashtra, the suicide rate of the farmers increased over the years. Due to weather conditions, debt and frequent change in Indian government norms. Generally, farmers do not become aware of the soil nutrients and their compositions. Therefore, the farmers’ major issue is the crop selection, in the low fertility land. Machine learning (ML) is an essential approach for practical and effective solutions for this problem. This paper focuses on the selection of the crop based on the existing data by using Decision tree algorithm. Real data of Maharashtra are used for constructing the novel model that will be tested with the samples. This model helps the farmers to forecast a suitable harvest for cultivation by appropriate methods. The prediction will help to the farmers’ crop choice for drought-prone areas before cultivating onto the agriculture field.