Innovative Deep Learning Methods for Soil Classification and Crop Yield Prediction

The significance of soil in sustaining life on Earth cannot be overstated, particularly its role in crop production and environmental sustainability. This paper presents a analysis of various machine-learning models employed in soil classification and their implications for crop productivity. Soil texture and nutrient composition are pivotal factors influencing crop growth and sustainability. Leveraging machine learning techniques, such as classification algorithms and computer vision, this study evaluates the effectiveness of Decision Trees (DT), k nearest Neighbors (k-NN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) in automating the soil classification process. These models utilize detectable features including Soil characteristics like moisture, nutrients, structure, quality, pH, and texture to classify soil types accurately. Additionally, techniques like interpolation of soil particle size fractions using transformed data are explored. By examining the performance and efficiency of these models, this research aims to contribute insights into optimizing soil management practices for enhanced crop production and environmental sustainability.