AI-Driven Plant Health Assessment: A Comparative Analysis of Inception V3, ResNet-50 and ViT with SHAP for Accurate Disease Identification in Taro

Early diagnosis and preventive measures are necessary to mitigate diseases&rsquo: impact on the yield of Colocasia esculenta (Taro). This study addresses the challenges of Taro disease identification by employing two key strategies: integrating explainable artificial intelligence techniques to interpret deep learning models and conducting a comparative analysis of advanced architectures Inception V3, ResNet-50, and Vision Transformers for classifying common Taro diseases, including leaf blight and mosaic virus, as well as identifying healthy leaves. The novelty of this work lies in the first-ever integration of SHapley Additive exPlanations (SHAP) with deep learning architectures to enhance model interpretability while providing a comprehensive comparison of state-of-the-art methods for this underexplored crop. The proposed models significantly improve the ability to recognize complex patterns and features, achieving high accuracy and robust performance in disease classification. The model&rsquo:s efficacy was evaluated through multi-class statistical metrics, including accuracy, precision, F1 score, recall, specificity, Chohen&rsquo:s kappa, and area under the curve. Among the architectures, Inception V3 exhibited superior performance in accuracy (0.9985), F1 score (0.9985), recall (0.9985), and specificity (0.9992). The explainability of Inception V3 was further enhanced using SHAP, which provides insights by dissecting the contributions of individual features in Taro leaves to the model&rsquo:s predictions. This approach facilitates a deeper understanding of the disease classification process and supports the development of effective disease management strategies, ultimately contributing to improved Taro cultivation practices.