Machine Learning-Based Soil Nutrient Analysis for Optimized Plant Cultivation and Precision Agriculture

Soil fertility is important for sustainable agriculture and crop productivity but traditional ways of analyzing soil nutrients are labor-intensive, time-consuming, and expensive. Herein, we introduce an AI-based soil nutrient analysis system meant to enhance plant cultivation and precision agriculture. By abstracting machine learning (ML) models with remote sensing, IoT -based soil sensors, and geospatial information system, the proposed system allows enhanced monitoring of soil health as well as real-time decision-making on the part of farmers. The system employs hybrid ML models-artificial neural networks (ANN s), support vector machines (SVM), neuro-fuzzy systems, and random forest (RF)-for accurate prediction of soil critical parameters such as cation exchange capacity (CEC), soil organic carbon (SOC), soil water retention capacity (SWRC), and macro/micro nutrient levels. This will support optimized fertilizer recommendation and precision irrigation schedules, thus reducing wastage of resources and increasing crop yields. In addition, deep learning will facilitate remote soil mapping and nutrient deficiency detection thereby potentially catalyzing precision farming initiatives. The automated soil health assessment will thus replace time-consuming lab tests and undertake direct action in farmers' hands toward soil management enhancement, which means more in productivity and environmental sustainability. The AI architecture grows flexible to the requirements posed by current agriculture. It has transformed the traditional, time-, and labor-intensive activity of soil nutrient analysis toward sustainable agricultural practices.