Soil Type Detection and Crop Prediction Using Deep Learning Method CNN

The study presents a novel use of Convolutional Neural Network (CNN) algorithms for crop prediction and soil type classification. Soil type classification is crucial for agricultural planning and management, while accurate crop prediction aids in optimizing crop yields and resource allocation. Traditional methods for soil classification and crop prediction often rely on manual labor and subjective assessments. Our proposed CNN based approach utilizes deep learning to analyze soil properties and predict suitable crops, offering an automated and data-driven solution. Extensive trials on real-world soil and crop datasets show the efficacy of our approach, yielding encouraging accuracy and efficiency outcomes. This research contributes to advancing precision agriculture practices by providing a reliable tool for soil classification and crop prediction.