Work

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

Year: 2025

Type: article

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.

Source: Discover Internet of Things

DOI: 10.1007/s43926-025-00175-9

Author: Jhih-Rong Liao

Institutions: Taiwan Banana Research Institute, Tokyo Metropolitan University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,590