An Integrated Deep Learning-Based System for Plant Disease Detection and Smart Agriculture

Abstract

Plant diseases remain a major challenge in modern agriculture, directly affecting crop yield, quality, and economic stability. Early and accurate identification of plant diseases is essential for minimizing losses and improving productivity. This paper presents an integrated deep learning-based framework for automated plant disease detection and smart agricultural decision support. The proposed system utilizes a Convolutional Neural Network (CNN) to analyze leaf images and classify them into multiple disease categories based on learned visual patterns such as texture, color variation, and structural features. To enhance practical applicability, the system is designed to operate as part of a broader decision-support pipeline, providing insights that assist in timely intervention. The model is trained on a labeled dataset containing diverse plant disease classes and optimized using standard preprocessing and regularization techniques to improve generalization. Experimental evaluation demonstrates that the model achieves high classification accuracy under realistic conditions while maintaining computational efficiency. The proposed approach reduces dependency on manual inspection and enables scalable deployment in agricultural environments. By combining deep learning with application-oriented design, this work contributes toward the development of intelligent and accessible solutions for precision agriculture.