Plant Leaf Detection System

Authors: Prof. Kenge Jayant Pandurang, Miss. Nikam Samruddhi Dattu, Miss. Patil Vaishnavi Bhaskar, Miss. Thorat Shruti Santosh, Mr. Pawar Bhushan Avinash

Country: India

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Abstract: The increasing demand for sustainable agriculture underscores the importance of early and accurate detection of plant diseases. This paper presents a novel approach for automated detection of plant leaf diseases using image processing techniques. The proposed system leverages advanced computer vision algorithms to analyze digital images of plant leaves, enabling rapid and reliable identification of diseases. The methodology involves image acquisition, preprocessing, feature extraction, and classification. Initially, high-resolution images of plant leaves are captured using digital cameras or smartphones. Preprocessing techniques such as image enhancement and noise reduction are applied to improve the quality of the images. Feature extraction is performed to identify distinctive patterns and characteristics related to disease symptoms. A comprehensive dataset comprising healthy and diseased plant leaves is utilized to train and validate the classification model. Machine learning algorithms, including deep learning models, are employed for the classification task. The trained model can accurately classify leaves into healthy or diseased categories, providing a valuable tool for farmers and researchers. The proposed system demonstrates promising results in terms of accuracy and efficiency, offering a non-invasive and cost-effective solution for plant disease detection. The implementation of this automated approach can significantly contribute to the early detection and management of plant diseases, ultimately enhancing crop yield and promoting sustainable agriculture practices.

Keywords: Plant disease detection, Image processing, Computer vision, Automated diagnosis, Digital image analysis, Feature extraction, Classification model, Machine learning, Deep learning, Agriculture

Paper Id: 230449

Published On: 2024-01-23

Published In: Volume 12, Issue 1, January-February 2024

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