Support Vector Machines for Soil Quality Assessment in Sustainable Farming

In order to understand the soil’s health, fertility, and crop appropriateness, soil quality assessments are an important part of sustainable agricultural techniques. Using their accuracy and resilience in dealing with complicated, nonlinear datasets, Support Vector Machines (SVMs) are investigated in this research as a potential tool for soil quality evaluation. Our study presents a comprehensive methodology for using SVM to analyze soil properties, including pH, nutrient content, moisture levels, and organic matter. We employ a dataset encompassing diverse soil samples from different geographical regions and farming conditions. The proposed SVM-based model is trained and validated using this dataset, demonstrating high predictive accuracy and reliability in classifying soil quality. Additionally, we compare the performance of SVM with other machine learning techniques such as Random Forest and k-Nearest Neighbors, highlighting the advantages of SVM in terms of precision and computational efficiency. The results indicate that SVM can serve as a powerful tool for farmers and agronomists, facilitating informed decision-making and promoting sustainable agricultural practices. In conclusion, this research underscores the potential of SVM in soil quality assessment, contributing to the advancement of precision agriculture. Future work will focus on integrating SVM with remote sensing technologies and expanding the dataset to include more diverse soil types and environmental conditions.