GourNet: A CNN-Based Model for Mango Leaf Disease Detection
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913076765261824 |
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| author | Alam, Ekram Sanyal, Jaydip Das, Akhil Kumar Bhattacharya, Arijit Sultana, Farhana |
| author_facet | Alam, Ekram Sanyal, Jaydip Das, Akhil Kumar Bhattacharya, Arijit Sultana, Farhana |
| contents | Mango cultivation is crucial in the agricultural sector, significantly contributing to economic development and food security. However, diseases affecting mango leaves can significantly reduce both the production and overall fruit grade. Detecting leaf diseases at an early stage with precision is key to effective disease prevention and sustaining crop productivity. In this paper, we introduce a "deep learning" model named "GourNet", which leverages "Convolutional Neural Networks" to identify infections in mango leaves. We utilize the "MangoLeafBD" (MBD) dataset to train and assess the effectiveness of the presented model. The MBD dataset contains seven disease classes and a Healthy class, making a total of eight classes. To enhance model performance, the images are preprocessed through steps like resizing, rescaling, and data augmentation prior to training. To properly evaluate the model, the dataset is separated into 80% for training, with the remaining 20% equally split between validation and testing. Our model uses only 683,656 total parameters and achieves a classification accuracy of 97%. This research's source code can be found at: https://github.com/ekramalam/GourNet-Repo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27764 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GourNet: A CNN-Based Model for Mango Leaf Disease Detection Alam, Ekram Sanyal, Jaydip Das, Akhil Kumar Bhattacharya, Arijit Sultana, Farhana Computer Vision and Pattern Recognition Mango cultivation is crucial in the agricultural sector, significantly contributing to economic development and food security. However, diseases affecting mango leaves can significantly reduce both the production and overall fruit grade. Detecting leaf diseases at an early stage with precision is key to effective disease prevention and sustaining crop productivity. In this paper, we introduce a "deep learning" model named "GourNet", which leverages "Convolutional Neural Networks" to identify infections in mango leaves. We utilize the "MangoLeafBD" (MBD) dataset to train and assess the effectiveness of the presented model. The MBD dataset contains seven disease classes and a Healthy class, making a total of eight classes. To enhance model performance, the images are preprocessed through steps like resizing, rescaling, and data augmentation prior to training. To properly evaluate the model, the dataset is separated into 80% for training, with the remaining 20% equally split between validation and testing. Our model uses only 683,656 total parameters and achieves a classification accuracy of 97%. This research's source code can be found at: https://github.com/ekramalam/GourNet-Repo. |
| title | GourNet: A CNN-Based Model for Mango Leaf Disease Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.27764 |