SMART AGRICULTURE THROUGH AI-ENABLED SOIL DIAGNOSTICS AND CROP FORECASTING

Agriculture is facing increasing challenges due to population growth, climate change, soil degradation, and limited natural resources. The adoption of Artificial Intelligence and related digital technologies provides an effective solution to address these challenges through data-driven and intelligent farming practices. This study proposed an AI-enabled Smart Agriculture framework for soil diagnostics and crop forecasting that integrates IoT sensors, machine learning algorithms, and predictive analytics to improve agricultural productivity and sustainability. The experimental results demonstrated that the proposed system effectively analyzed soil health conditions, identified nutrient deficiencies, recommended suitable crops, and accurately predicted crop yields. The Random Forest algorithm achieved high accuracy in soil fertility classification, while the LSTM model delivered superior crop yield forecasting performance. Additionally, the system optimized fertilizer and water usage, reduced operational costs, and enhanced resource efficiency. The findings confirm that AI-enabled soil diagnostics and crop forecasting can significantly improve agricultural decision-making, increase productivity, and support sustainable farming practices. The integration of intelligent technologies into agriculture not only enhances farm management but also contributes to food security and environmental conservation. Therefore, the widespread adoption of AI-based smart agriculture systems can play a vital role in transforming traditional farming into a more efficient, productive, and sustainable agricultural ecosystem.

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