Plant Disease Detection System Using CNN

Kadem Maithili

Plant Disease Detection System Using CNN

Author(s)	Ms. Kadem Maithili
Country	India
Abstract	Agriculture plays a vital role in the global economy and food security; however, plant diseases significantly reduce crop yield and quality, leading to economic losses for farmers. Early and accurate detection of plant diseases is essential to minimize damage and ensure sustainable agricultural practices. The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage. This system utilizes image processing and convolutional neural networks (CNNs) to analyse leaf images and detect disease symptoms such as discoloration, spots, and texture variations. The proposed system captures images of plant leaves using smartphones or digital cameras and processes them through preprocessing techniques including resizing, noise removal, and contrast enhancement. Feature extraction is performed automatically by deep learning models trained on large, labelled datasets containing healthy and diseased plant images. The trained model classifies the input image into specific disease categories or identifies it as healthy with high accuracy. This approach reduces the dependency on agricultural experts and manual inspection, which are often time-consuming and inaccessible to small-scale farmers. Additionally, the system can be integrated with web or mobile applications to provide real-time disease diagnosis and actionable recommendations such as suitable pesticides, preventive measures, and crop management practices. The scalability of the system allows it to support multiple crops and disease types, making it adaptable to diverse agricultural environments. By enabling early detection and informed decision-making, the Plant Disease Detecting System contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices. Overall, this intelligent system demonstrates the potential of AI-driven technologies in transforming traditional agriculture into smart and precision-based farming.
Keywords	Convolutional Neural Network (CNN),Deep Learning, Image Processing, Computer Vision, Feature Extraction, Transfer Learning, Crop Health Monitoring, Smart Agriculture, Leaf Disease Classification, Agricultural Productivity.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-10

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International Journal For Multidisciplinary Research

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Plant Disease Detection System Using CNN

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