AI-based UAV pest and disease detection: Time for a reset?

Abstract

Remote sensing using uncrewed aerial vehicles (UAVs) and AI, particularly machine learning and deep learning, is increasingly applied to crop pest and disease detection. However, the real-world robustness of these models remains uncertain. We conducted a meta-analysis of 121 UAV-based studies published between 2018 and 2024, examining dataset construction and model validation practices. We found that 89% of studies lacked truly independent test datasets, resulting in inflated performance estimates and limited generalisability. Only 11% evaluated models on independent fields, and successful transferability was uncommon. Our analysis identifies key methodological limitations underlying this issue and provides recommendations to improve robustness, reproducibility, and practical relevance. Overall, current validation practices require substantial improvement to ensure reported model performance reflects field-level applicability.

Plant phenotyping relevance

UAV・AIによる作物の病害検出手法を対象に、121研究のデータセット構築とモデル検証をメタ分析し、独立圃場での性能や再現性を評価する方法論的レビューである。病害検出は植物の病態・重症度に関わるため、手法中心の研究として採用する。

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