Integrating UAVs and Deep Learning for Plant Disease Detection: A Review of Techniques, Datasets, and Field Challenges with Examples from Cassava

Abstract

Cassava remains a critical food-security crop across Africa and Southeast Asia but is highly vulnerable to diseases such as cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). Traditional diagnostic approaches are slow, labor-intensive, and inconsistent under field conditions. This review synthesizes current advances in combining unmanned aerial vehicles (UAVs) with deep learning (DL) to enable scalable, data-driven cassava disease detection. It examines UAV platforms, sensor technologies, flight protocols, image preprocessing pipelines, DL architectures, and existing datasets, and it evaluates how these components interact within UAV–DL disease-monitoring frameworks. The review also compares model performance across convolutional neural network-based and Transformer-based architectures, highlighting metrics such as accuracy, recall, F1-score, inference speed, and deployment feasibility. Persistent challenges—such as limited UAV-acquired datasets, annotation inconsistencies, geographic model bias, and inadequate real-time deployment—are identified and discussed. Finally, the paper proposes a structured research agenda including lightweight edge-deployable models, UAV-ready benchmarking protocols, and multimodal data fusion. This review provides a consolidated reference for researchers and practitioners seeking to develop practical and scalable cassava-disease detection systems.

Plant phenotyping relevance

UAV画像と深層学習によるカッサバ病害の検出手法を中心に、センサー、撮影プロトコル、画像処理、モデル、データセット、性能指標を体系的にレビューしているため、植物の病害状態を対象とするフェノタイピング手法レビューに該当する。

Code and data availability

This is a review article synthesizing prior UAV–DL cassava disease studies. No public phenotype datasets, UAV imagery, author analysis code, or trained models specific to this paper are described; no data or code availability statement appears in the supplied blocks. Figures are AI-assisted visualizations of literature

No evidence-backed public reproduction asset is currently recorded.