@article{10.37349/eff.2026.1010200,
abstract = {Aim: Timely and accurate identification of crop leaf diseases is crucial for food security and sustainable agriculture. This study aims to develop a multi-crop leaf disease recognition framework that covers corn, potato, rice, and tomato, addressing the challenges associated with maintaining high performance under diverse agricultural imaging conditions. Methods: We utilized a balanced dataset of 21,000 curated leaf images spanning fourteen classes, including healthy and diseased samples. A custom convolutional neural network (CNN) was specifically designed to capture fine-grained lesion patterns. The model was trained using a unified pipeline with standardized data splits and preprocessing, ensuring reproducible evaluation. The performance was compared against standard deep learning architectures (ResNet-50, VGG-16) and traditional machine learning classifiers, including k-nearest neighbors (k-NN), support vector machine (SVM), and Random Forest. Results: The custom CNN achieved near-perfect classification accuracy on the test set, outperforming ResNet-50, VGG-16, and traditional machine learning classifiers under identical conditions. While the model showed potential for generalization to diverse field scenarios, the evaluation was limited to curated datasets. Additionally, the trained model was deployed in a web application to demonstrate real-time inference and practical utility for leaf disease assessment. Conclusions: This study establishes a robust and reproducible benchmark for multi-crop disease classification. The results highlight the model’s effectiveness in precision agriculture, supporting early intervention strategies, though further validation in field conditions is needed to confirm generalization.},
author = {Bhuiyan, Zarif Wasif and Atib, Zaed Bin Monir and Hasan, Mahady and Habib, Md. Tarek},
doi = {10.37349/eff.2026.1010200},
journal = {Exploration of Foods and Foodomics},
elocation-id = {1010200},
title = {Unified CNN-based recognition of multi-crop leaf diseases with web deployment},
url = {https://www.explorationpub.com/Journals/eff/Article/1010200},
volume = {4},
year = {2026}
}