GourNet: A CNN-Based Model for Mango Leaf Disease Detection

arXiv cs.CV / 5/1/2026

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Key Points

The paper introduces “GourNet,” a CNN-based deep learning model designed to detect mango leaf diseases and distinguish infected leaves from healthy ones.
It trains and evaluates the model using the MangoLeafBD (MBD) dataset, which includes seven disease classes plus a healthy class (eight total categories).
The authors preprocess images with resizing, rescaling, and data augmentation to improve performance before training.
For evaluation, the dataset is split into 80% training and the remaining 20% divided evenly between validation and testing.
GourNet reportedly achieves 97% classification accuracy while using a relatively small model size of 683,656 total parameters, and the code is published on GitHub.

Abstract

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.