Deep Learning-Based Plant Disease Detection Using Convolutional Neural Networks (CNN)

Author

Vijay S


Abstract

 Plant diseases are a major challenge in agriculture because they can reduce crop productivity, affect food quality, and cause economic losses. Conventional plant disease identification depends largely on visual inspection by farmers and agricultural experts, which can be time-consuming and may result in inaccurate classification. Recent advances in deep learning and computer vision provide opportunities to automate disease identification from plant leaf images. This paper proposes a deep learning-based plant disease detection system using Convolutional Neural Networks (CNN). The proposed framework consists of image acquisition, pre processing, feature extraction, model training, disease classification, and result visualization. The Plant Village dataset is proposed as the initial dataset for training and evaluating the model. The system aims to classify leaf images into healthy and disease-specific categories and provide preliminary disease information to users. Performance will be assessed using accuracy, precision, recall, F1-score, and a confusion matrix. A transfer-learning model such as MobileNetV2 may also be evaluated to compare classification performance and computational efficiency. The proposed system is intended to support early disease screening and improve access to plant health information. Further validation using images collected under real agricultural conditions is necessary before practical deployment.



Keywords

Artificial Intelligence, Deep Learning, Plant Disease Detection, Convolutional Neural Network, Image Classification, Computer Vision, Precision Agriculture.



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References


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