Abstract

This study presents a method for classifying cotton crops using multi-spectral satellite images to study the soil behavior under these crops. The proposed method uses a machine learning approach based on a support vector machine (SVM) to classify the crops in the satellite images. The SVM model is trained on a dataset of multi-spectral satellite images and tested on an independent dataset to evaluate its performance. The study also analyzes the soil behavior under the cotton crops by studying various soil parameters such as moisture content, organic matter, and nutrient levels. The results demonstrate the potential of the proposed method for accurate crop classification and soil behavior analysis.