Analytical Approach for Soil and Land Classification Using Image Processing with Deep Learning

Abstract

Agriculture highly depends on soil. Soils are available in a number of types. Each type of soil has unique characteristics, and various crops grow in each type of soil. For a number of reasons, researchers have recently developed an interest in land mappings and classifications. Soil health and analysis of soil health, that are important for the healthy crop productions, are receiving more attention from the research community as a result of the rising demanding for the agricultural fields. The soil classification is the process of categorizing soil sets into groups with comparable qualities and behaviors. Soil is a mineral storehouse. Farmers depends on the soil to grow various crops; however, most farmers are aware of which crops grow in particular soil. The classification of soil and land is essential. Soil type identification is necessary to avoid quantitative losses in agricultural productivity. Therefore, an analytical approach for soil and land classification using image processing and deep learning is presented in this methodology. The process of applying different operations to an image in order to either improve it or extract useful information from it is described as image processing. Using a deep learning algorithm based a convolutional neural network, this method categorizes images of soil and land.