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A Lightweight Parallel CNN Framework for Real-Time Plant Disease Detection in Corn, Tomato, and Apple | ||
| Al-Noor Journal for Information Technology and Cybersecurity | ||
| Article 4, Volume 3, Issue 1, June 2026, Pages 27-47 PDF (1.33 M) | ||
| Document Type: Research paper | ||
| DOI: 10.69513/jncs.v3.i1.a4 | ||
| Author | ||
| Shamam Shehab Ahmed* | ||
| Department of Computer Engineering, College of Electronic Engineering, Ninevah University, Mosul, Iraq | ||
| Abstract | ||
| Plant leaf diseases present significant risks to agricultural productivity. The process for diagnosing plant leaf diseases involves the examination of leaves by experts, but this method is quite inefficient and inaccurate. This study presents a novel parallel scheme architecture of deep learning networks, which is based on parallel operation of individual binary classifiers, wherein each classifier is dedicated to identifying a particular disease. This architecture improves feature extraction while keeping low computational complexity and ensures reliability and portability of the model to be utilized on lightweight platforms such as mobile phones. Moreover, the proposed architecture is flexible and extensible to use new classifiers for particular diseases without adding any complexity. The model was trained with a part of the Plant Village data set that comprised images of corn, tomato, and apple crops, each with three types of diseases and a healthy one, with 513 images per category. Preprocessing techniques and data augmentation have been used to enhance generalization. This model has just 710,786 parameters and consumes 1.327 GFLOPs. Field images collected from nature were used to check the robustness of the model; however, environmental conditions and the scarcity of plants made it impossible to capture images of only 3–4 samples per crop through a mobile phone. This reason makes the evaluation preliminary. The accuracy by Parallel-CNN are: corn-Cercospora Leaf Spot 98.54%, Common Rust 100%, Northern Leaf Blight 100%; tomato-Bacterial Spot 100%, Early Blight 100%, Late Blight 98.54%; and apple-Apple Scab 95.63%, Black Rot 100%, and Cedar Apple Rust 100%. | ||
| Keywords | ||
| Plant disease detection; Parallel CNN; Lightweight CNN; Real-time monitoring; Smart agriculture; Corn; Tomato | ||
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