Volume: 2 Issue: 2
Year: 2025, Page: 59-63, Doi: https://doi.org/10.70968/ijeaca.v2i2.ML109
Received: July 27, 2025 Accepted: Nov. 26, 2025 Published: Dec. 12, 2025
Leaf diseases pose a serious difficulty challenge in the management of mango growers and may cause significant yield loss if diagnosis is delayed. In this paper, we describe a novel deep learning-based approach for the classification of diseases in mango leaves using images. The method proposed takes advantage of a pretrained MobileNetV2 model to predict whether a leaf image is healthy or belongs to one of its diseased classes, including Anthracnose, Bacterial Canker, Powdery Mildew Cutting Weevil (Die Back), Gall Midge and Sooty Mould. In addition to detecting diseases, it provides treatment suggestions and general tips for leaf care, such as organic solutions. High-risk periods for disease spread are continuously analyzed, and alerts are generated based on prevailing weather conditions. System supports multiple languages (Marathi, English and Hindi) which makes the user interface Farmer Friendly; increase usability in different regions, this will contribute for early diagnosis improved crop management.
Keywords: Deep Learning-Driven Mango Leaf Disease Identification and Management System
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© 2025 Patil, et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Patil PS, Bhatkar SS, Momin MJ, Tanpure GR, Patil MM. Deep Learning-Driven Mango Leaf Disease Identification and
Management System. 2025; 2(2):59-63.https://doi.org/10.70968/ijeaca.v2i2.ML109