nt and independent field-test data and should pub- lish the class-wise split manifest, random seeds, WOA-GWO numerical settings, and evaluation code so that the reported pro- cedure can be reproduced and extended. Data availability The data used in this study are available at https:// data.mendeley.com/datasets/bwh3zbpkpv/1 and https://www.kaggle.com/datasets/rahimanshu/ccmt-plant-disease-dataset.Declaration of competing interest The authors declare that they have no known competing fi- nancial interests or personal relationships that could have ap- peared to influence the work reported in this manuscript. Funding The authors received no specific funding from any public, commercial, or not-f
Open resource ↗Kaggle · rahimanshu/ccmt-plant-disease-dataset · pdf-raw-page:12 lines:1-78Unverified paper record
HybOptic-CNN: A hybrid WOA-GWO-optimized convolutional neural network model for enhanced plant disease detection in the Nigerian environment
Journal of the Nigerian Society of Physical Sciences · 11 Sept 2026 · 10.46481/jnsps.2026.3470
Abstract
Plant diseases threaten agricultural productivity, and automated image analysis can support early identification of visible disease symptoms. This study introduces HybOptic-CNN, a convolutional neural network (CNN) whose learning rate and batch size are selected using a hybrid Whale Optimization Algorithm--Grey Wolf Optimizer (WOA-GWO). Nine disease classes were selected from the 22-class CCMT field-image dataset, and 152 local farm leaf images were collected in Enugu State, Nigeria. Of the local images, 122 (80.3%) were added to the model-development data for training and validation, whereas 30 (19.7%) formed an independent Nigerian hold-out set excluded from augmentation, class balancing, early stopping, validation, and hyperparameter selection. Across 10 model-development runs, the optimized model achieved 96.8 ± 0.4% mean validation accuracy, 95.2 ± 0.5% macro-precision, 94.9 ± 0.6% macro-recall, and 95.0 ± 0.5% macro-F1, compared with 90.3 ± 0.9% validation accuracy and 86.3 ± 1.2% macro-F1 for the baseline. The optimized model improved mean validation accuracy by 6.5 percentage points and converged 14.6 epochs earlier. On the independent 30-image Nigerian hold-out, HybOptic-CNN achieved 93.3% accuracy and 93.1% macro-F1 across four represented disease classes. A web application integrating the trained classifier was also demonstrated. These results support improved model-development performance through hybrid hyperparameter selection and motivate broader multi-location field evaluation.
Plant phenotyping relevance
植物の可視病徴を画像から分類するCNN手法を開発・検証しており、病害状態のフェノタイピング手法が中心である。
abstractautomated image analysis can support early identification of visible disease symptoms
abstractThis study introduces HybOptic-CNN, a convolutional neural network (CNN) whose learning rate and batch size are selected using a hybrid Whale Optimization Algorithm--Grey Wolf Optimizer (WOA-GWO).
abstractOn the independent 30-image Nigerian hold-out, HybOptic-CNN achieved 93.3% accuracy and 93.1% macro-F1 across four represented disease classes.
abstractA web application integrating the trained classifier was also demonstrated.
Code and data availability
The paper's Data availability statement points to two public sources: a Mendeley dataset (the locally collected Nigerian field images) and the Kaggle CCMT plant disease dataset used as the principal image source. Only the Kaggle URL matches an allowed URL; the Mendeley URL is not in the allowed list, so only the CCMT/K
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