Soil Classification and Crop Prediction Using Machine Learning Techniques

Population growth at a faster pace has resulted in the loss of agricultural land due to urbanization, generating a strong demand for food. Conventional crop production techniques used by farmers are not sufficient to meet the required food security. Therefore, to increase the crop production with limited resources, they must be acquainted with advanced and efficient agricultural management practices – including accurate information on the environment and especially the soil type for a particular crop to be cultivated. Crop cultivation practices and crop yields depend on the nature and nutrients present in the soil, thus proving that soil classification has a huge significance in sustainable food production. A large variety of soils occur worldwide. The Indian subcontinent includes a large variety of soil types that are derived from a wide variety of rocks and minerals. According to different characteristics, including climate, vegetation, and terrain to name a few, soil resources vary throughout agroecological subregions. Crop forecasting can assist farmers in increasing agricultural output, which in turn helps to support the global food production industry. In order to give researchers, specialists, and scientists full knowledge about the different types of soil found in different locations, soil categorization systems are created. There have been many classification schemes created, and they are all used globally. Based on the physical conditions, these systems categorize soils according to their general behavior. Though the traditional method of soil survey continues as the most popular form of soil mapping and inventory, it is subjective, time-consuming, and laborious. In order to provide new latest insights and a better understanding of soil science in crop cultivation and crop prediction, this chapter provides an elaborate survey on various machine learning classifications and regression techniques explored in this area. Machine learning models can help classify various soil series and suggest suitable crops based on their geographical attributes. Various supervised machine learning algorithms and ensembles are used on standard datasets.