Hybrid metaheuristic optimization of a DeepFusionNet for plant leaf disease diagnosis and recommendation.

Abstract

Early diagnosis of plant leaf diseases plays an important role in protecting crop yields and supporting sustainable agriculture. This paper proposes an improved DeepFusionNet model optimized through a hybrid Flower Pollination Algorithm and Butterfly Optimization Algorithm, balancing global exploration with local refinement for faster and more stable convergence. The model combines DenseNet201 and MobileNetV2 by compressing their final convolutional feature maps with 1×1 convolutions and fusing them along the channel dimension to form a compact and discriminative representation. This fused representation is then classified using a Random Forest classifier. This framework consistently achieves high accuracy on all eight datasets, with performance ranging between 97.07% and 99.66%. Extensive experiments are performed that include statistical validation, convergence studies, and reliability tests to prove the robustness of the approach. Furthermore, to make it practically useful, the whole system is embedded into a mobile application capable of real-time disease detection and providing actionable recommendations to farmers for the effective treatment and prevention of diseases.

Plant phenotyping relevance

植物葉の病徴を画像から診断する深層学習手法の開発・検証が中心であり、植物の病害状態を直接推定する画像ベース表現型解析に該当する。

Code and data availability

The supplied blocks describe a DeepFusionNet leaf-disease model trained on eight Kaggle/Mendeley leaf image datasets, but contain no authors' public code, trained model checkpoints, or paper-specific data deposit. The datasets are cited third-party repositories (refs 34-41), not paper-specific assets, and no code or数据-

No evidence-backed public reproduction asset is currently recorded.