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While deep learning models like CNNs and Vision Transformers show strong potential for banana pest and disease monitoring across mobile, UAV, and IoT platforms, scalable deployment requires overcoming significant barriers in data quality, model generalization, and infrastructure. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 21, 2025Discover Internet of Things3 citationsOpen Access

AI-driven banana pest and disease management: methods, applications, challenges, and future directions

JLJhih‐Rong Liao

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Abstract

Banana production faces escalating threats from pests and diseases, especially in smallholder systems. This review provides a comprehensive assessment of artificial intelligence applications in banana health monitoring, focusing on deep learning models including convolutional neural networks, YOLO variants, and Vision Transformers. Unlike prior reviews, this work classifies model performance across mobile, UAV, and IoT-based systems and evaluates effectiveness under both controlled and field conditions. It introduces recent advances such as Swin Transformer and lightweight ViT architectures. Beyond technical capabilities, the review highlights barriers related to data quality, model generalization, cost, and infrastructure, and proposes strategies for improving adoption. By bridging technical advances with policy insights, the review offers a structured roadmap for scalable, inclusive AI deployment in banana pest and disease management.

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