Paper Title
DEEP CONVOLUTIONAL NETWORKS FOR EARLY DETECTION AND CLASSIFICATION OF PLANT PATHOLOGIES

Abstract
Plant diseases significantly impact global agricultural productivity, making early and accurate identification essential for reducing yield loss. Traditional diagnosis methods rely heavily on human expertise and manual inspection, which are subjective, time-consuming, and often inaccessible to farmers in remote areas. Existing machine learning based approaches depend on handcrafted features that fail to capture complex disease patterns, resulting in limited performance in real world conditions. This study addresses these challenges by proposing an automated plant disease detection system using Convolutional Neural Networks (CNNs) and a fine-tuned Alex Net architecture. The rationale behind this approach is to utilize the superior feature extraction capability of deep learning to eliminate the need for manual feature engineering. The system is trained on a diverse and augmented dataset of healthy and diseased leaf images, improving the model’s robustness under varied conditions. Comparative analysis with classical machine learning techniques demonstrates that the proposed deep learning model achieves significantly higher accuracy and generalization. Experimental results validate its effectiveness as a scalable and practical solution for real-time diagnosis in precision agriculture. Keywords - 13T SRAM, Radiation-Hardened Memory, Single Event Upset (SEU), Low-Power VLSI, Soft Error Resilience, Memory Architecture.