Soil Nutrient Analysis and Optimization Using DL/ML Techniques

Soil nutrient analysis plays a crucial role in modern agriculture by ensuring optimal crop growth and sustainable farming practices. Traditional soil testing methods are time-consuming, labor-intensive, and require extensive laboratory analysis. To address these challenges, this research proposes a Soil Nutrient Analysis and Optimization System leveraging Deep Learning (DL) and Machine Learning (ML) techniques to enhance nutrient prediction and soil health management. The system integrates historical soil data, remote sensing imagery, and IoT-enabled real-time soil monitoring to assess nutrient composition accurately. DL models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are employed for soil texture classification and nutrient deficiency detection, while ML models like Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANNs) are used for nutrient level prediction and optimization. The proposed framework provides real-time insights into soil fertility, enabling data-driven decision-making for precision agriculture. Experimental results demonstrate that DL-based models outperform traditional soil testing techniques in terms of prediction accuracy, efficiency, and scalability. This study highlights the potential of AI-driven solutions in soil health monitoring, precision fertilization, and sustainable farming. By optimizing soil nutrient management, this research contributes to higher crop yield, reduced fertilizer wastage, and improved environmental sustainability in modern agriculture.