Automatic mango leaf disease detection using different transfer learning models

被引：3

作者：

Varma T. ^{[1
]}

Mate P. ^{[1
]}

Azeem N.A. ^{[1
]}

Sharma S. ^{[1
]}

Singh B. ^{[1
]}

机构：

[1] Indian Institute of Information Technology, Pune

来源：

Multimedia Tools and Applications | 2025年 / 84卷 / 11期

关键词：

Computer vision; Convolution neural network; Deep learning; Leaf disease classification; Transfer learning;

D O I：

10.1007/s11042-024-19265-x

中图分类号：

学科分类号：

摘要：

The cultivation of mangoes contributes significantly to the economy and food security of many tropical and subtropical regions. However, mango trees are susceptible to various leaf diseases that can significantly affect crop yield and quality. Detecting and diagnosing these diseases early is crucial for sustainable mango production. As a subset of machine learning, deep learning has emerged as a powerful tool for image analysis and classification, offering promising solutions for mango leaf disease detection. This work presents a systematic analysis of existing studies, including data collection, pre-processing techniques, network architectures, and performance evaluation metrics. It uses various deep learning pre-trained models like VGG19, InceptionV3, ResNet152V2, DenseNet121, InceptionResNetV2, MobileNetV2, and Xception Models. InceptionV3 model performed best with a 99.87% accuracy rate, compared to other models. A comparison with earlier research is also presented to demonstrate the effectiveness of the research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

引用

页码：9185 / 9218

页数：33

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