DLCPD-25: A Large-Scale and Diverse Dataset for Crop Disease and Pest Recognition.

Abstract

The accurate identification of crop pests and diseases is critical for global food security, yet the development of robust deep learning models is hindered by the limitations of existing datasets. To address this gap, we introduce DLCPD-25, a new large-scale, diverse, and publicly available benchmark dataset. We constructed DLCPD-25 by integrating 221,943 images from both online sources and extensive field collections, covering 23 crop types and 203 distinct classes of pests, diseases, and healthy states. A key feature of this dataset is its realistic complexity, including images from uncontrolled field environments and a natural long-tail class distribution, which contrasts with many existing datasets collected under controlled conditions. To validate its utility, we pre-trained several state-of-the-art self-supervised learning models (MAE, SimCLR v2, MoCo v3) on DLCPD-25. The learned representations, evaluated via linear probing, demonstrated strong performance, with the SimCLR v2 framework achieving a top accuracy of 72.1% and an F1 score (Macro F1) of 71.3% on a downstream classification task. Our results confirm that DLCPD-25 provides a valuable and challenging resource that can effectively support the training of generalizable models, paving the way for the development of comprehensive, real-world agricultural diagnostic systems.

Plant phenotyping relevance

作物の病害・健全状態を画像で認識する大規模公開ベンチマークデータセットを構築・評価しており、植物状態の画像ベース表現型解析基盤が中心です。害虫認識も含まれますが、病害・健全状態の評価は植物フェノタイピングに該当します。

Code and data availability

The paper introduces DLCPD-25, a public crop pest/disease image dataset (221,943 images, 203 classes), with an explicit Data Availability Statement pointing to the authors' GitHub repository containing all image data and documentation.