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

TL;DR

GourNet is a CNN-based model that accurately detects mango leaf diseases using the MangoLeafBD dataset, achieving 97% accuracy with a lightweight architecture.

Contribution

This paper introduces GourNet, a novel CNN model optimized for mango leaf disease detection, with high accuracy and efficient parameter usage.

Findings

Achieved 97% classification accuracy on MangoLeafBD dataset.

Utilized data augmentation and preprocessing for improved performance.

Model has only 683,656 parameters, enabling efficient deployment.

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.