Implementation of AI Based Model for Identification of Different Soil Types Using Tensorflow

Abstract

The agricultural industry is among the most important industry in the world and playing a critical part in human growth. Soil classification is becoming incredibly valuable, and recent study reveals that classification of images is proven as the preferred way of soil information for farmers. The testing of soil is the first step in construction planning to determine whether the plot of land is suitable for constructing any structure in order to prevent future disasters. In this work, the model has been proposed to address the visual classification of types of soil using the deep neural network (DNN) and tensor flow framework. Further, AI based sequential model has been created in python to classify the soil. This has been analyzed that with the use of proposed technique five types of soil viz. black, cinder, laterite, peat, and yellow soil has been classified while keeping the practical simplicity of the images. Far superior accuracy has been achieved using the CNN model on the above dataset. The model is useful for farmers to increase the crop production after identification of the appropriate soil type for respective crops.