Explainable AI-based CNN Optimization Model for Plant Leaf Disease Detection

Abstract

Plant leaf disease is a grave risk to crop suitability and agricultural sustainability, and therefore, the ability to make early and accurate diagnosis is a mandatory need in the contemporary precision farming system.In recent years, deep learning has gained significant attention for image-based plant disease detection.Despite its effectiveness, model performance can be influenced by factors such as redundant feature representations and sensitivity to hyperparameter selection.To address these challenges, this study proposes a nested hybrid optimization framework that combines the Cuckoo Search Algorithm (CSA) for channel selection with the Beluga Whale Optimization Mechanism (BWOM) for tuning the hyperparameters of a Convolutional Neural Network (CNN).The proposed approach is independently evaluated on bean and grape leaf datasets under consistent experimental conditions to assess its disease classification performance.In addition to strong predictive performance, the framework incorporates explainable AI (XAI) techniques, namely Gradient-weighted Class Activation Mapping (Grad-CAM) and Gradientweighted Class Activation Mapping Plus Plus (Grad-CAM++), to enhance model interpretability.These approaches highlight the most significant visual features influencing predictions, thereby providing valuable insights for agronomists and fostering trust in AI-based systems.Experimental results show that the proposed CSA-BWOM optimized CNN achieves classification accuracies of 99.61% and 99.38% on the bean and grape datasets, respectively, outperforming baseline CNN models and exhibiting competitive performance when compared to a few existing approaches.

Plant phenotyping relevance

植物葉の病徴を画像から分類するCNN最適化・説明可能AI手法が研究の中心であり、植物病害状態の表現型推定に該当する。

Code and data availability

The supplied blocks describe a CSA-BWOM optimized CNN for bean and grape leaf disease detection, but contain no authors' public code, trained models, or paper-specific data deposits. The Bean Leaf Dataset [32] and PlantVillage Grape Leaf Dataset [33] are cited third-party benchmarks, not paper-specific assets, and no c

No evidence-backed public reproduction asset is currently recorded.