PLANT LEAF DISEASE IDENTIFICATION FOR  COTTON PLANTS

Abstract : Plant diseases pose a major threat to global agricultural productivity, often leading to significant yield losses and economic setbacks. Crops such as cotton, tomato, and apple are highly vulnerable to a range of diseases caused by fungal, bacterial, and viral pathogens. Conventional methods of disease detection primarily rely on manual observation, which is not only time-consuming and labor-intensive but also susceptible to human error. To address these limitations, this study presents a deep learning-based approach for automated plant leaf disease identification. Leveraging the capabilities of Convolutional Neural Networks (CNNs), the proposed system is trained to detect and classify various plant diseases from high-resolution images of healthy and diseased leaves. The dataset is curated and pre-processed to enhance image quality and improve feature extraction, ensuring model robustness under diverse environmental conditions. The CNN model demonstrates superior accuracy, precision, and recall rates compared to traditional image processing techniques. Experimental results confirm the system’s effectiveness in identifying multiple diseases across the selected crops. This approach not only accelerates the diagnosis process but also reduces dependency on agricultural experts, enabling timely intervention and effective crop management. The findings advocate for the integration of AI-based tools into precision agriculture practices, contributing to improved crop health monitoring, sustainable farming, and enhanced food security. This research highlights the transformative potential of deep learning in modern agriculture.

Keyword Plant Disease Detection, Deep Learning, Convolutional Neural Networks (CNN), Image Classification, Cotton Leaf Disease, Tomato Leaf Disease, Apple Leaf Disease, Precision Agriculture, Smart Farming, Sustainable Agriculture.