Hybrid IGWO-Dingo optimized DeMoHybridNet model for multi-class leaf disease identification.

Abstract

To achieve efficient crop management, exact plant disease detection in leaves is required. This study proposes DeMoHybridNet (Bottleneck Reduction Fusion) for the automated classification of Corn, Apple, Citrus, and Mango crop diseases. Input images are processed through augmentation and resizing, and then features are learned using DenseNet-201 and MobileNetV2. Global Average Pooling is applied, which produces condensed features. The features are then compressed using bottleneck layers of 512 features. The features are concatenated and classified by Random Forest (RF) classifier. To further improve the performance, a hybrid meta-heuristic method called IGWO-DOA (Improved Grey Wolf Optimization-Dingo Optimization Algorithm) is used to optimize the hyperparameters of the model for better convergence and generalization. The proposed optimized model gives the classification accuracy is 98.56% for Corn, 98.99% for Apple, 97.83% for Citrus and 99.35% for Mango leaf dataset. Statistical analysis confirms its robustness and reliability, demonstrating its effectiveness for precision agriculture applications.

Plant phenotyping relevance

葉画像から植物の病害状態を自動分類する深層学習・特徴抽出・分類ワークフローが研究の中心であり、植物病害表現型の画像ベース推定手法に該当する。

Code and data availability

The paper uses publicly available Kaggle/Mendeley leaf image datasets, but these are cited prior-work datasets, not paper-specific assets. No author code, models, or supplements are deposited; the Data Availability Statement only offers datasets from the corresponding author on request.

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