Abstract: Leaf disease detection is a critical task in agriculture, aiding in the early identification and treatment of plant diseases to ensure optimal crop health. This paper presents a comprehensive approach to automating leaf disease detection using advanced image processing and deep learning techniques in Python. The methodology involves preprocessing the input images to enhance features and extract meaningful information. Subsequently, a Convolutional Neural Network (CNN) model is trained on a curated dataset comprising healthy and diseased plant leaves. The CNN learns to classify leaves into respective disease categories, enabling automated detection. The trained model is evaluated based on various metrics such as accuracy, precision, recall, and F1-score to assess its performance. Additionally, a real-world application of the model is demonstrated through predictions on unseen leaf images. The results showcase the efficacy of the proposed approach in accurately identifying plant leaf diseases, laying the foundation for further advancements and integration into agricultural practices.

Keywords: Python Programming, Leaf Disease Detection.Image Processing, Convolutional Neural Networks (CNN)

Works Cited:

V.Shankar, Mrs. R.Femila Goldy " LEAF DISEASE DETECTION USING PYTHON ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 11-16, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12902

PDF | DOI: 10.17148/IJARCCE.2023.12902

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