Soil Analysis for Optimized Plant Cultivation Using Machine Learning and IOT Technologies

Precision agriculture relies on accurate soil characterization to optimize crop selection and management. This paper introduces a dual-mode framework for soil analysis, combining (i) image-based soil classification using advanced convolutional neural networks (CNNs) and (ii) IoT-driven real-time soil nutrient detection. The primary focus is on the image-based module, which employs rigorous preprocessing, data augmentation, and advanced feature extraction techniques to differentiate subtle variations in soil texture and composition. Experimental results demonstrate that the proposed system achieves high classification accuracy, enabling precise and actionable recommendations for improved plant cultivation. Additionally, the integration of IoT- based nutrient detection enhances real-time monitoring, ensuring more adaptive and data-driven agricultural practices. The find- ings highlight the potential of combining machine learning and IoT technologies to revolutionize soil assessment and decision- making in precision farming.