Rice Canopy Disease and Pest Identification Based on Improved YOLOv5 and UAV Images.

Abstract

Traditional monitoring methods rely on manual field surveys, which are subjective, inefficient, and unable to meet the demand for large-scale, rapid monitoring. By using unmanned aerial vehicles (UAVs) to capture high-resolution images of rice canopy diseases and pests, combined with deep learning (DL) techniques, accurate and timely identification of diseases and pests can be achieved. We propose a method for identifying rice canopy diseases and pests using an improved YOLOv5 model (YOLOv5_DWMix). By incorporating deep separable convolutions, the MixConv module, attention mechanisms, and optimized loss functions into the YOLOv5 backbone, the model's speed, feature extraction capability, and robustness are significantly enhanced. Additionally, to tackle the challenges posed by complex field environments and small datasets, image augmentation is employed to train the YOLOv5_DWMix model for the recognition of four common rice canopy diseases and pests. Results show that the improved YOLOv5 model achieves 95.6% average precision in detecting these diseases and pests, a 4.8% improvement over the original YOLOv5 model. The YOLOv5_DWMix model is effective and advanced in identifying rice diseases and pests, offering a solid foundation for large-scale, regional monitoring.

Plant phenotyping relevance

UAV画像からイネの作物病害を識別する改良YOLOv5手法の開発と性能評価が中心であり、植物の病害状態を画像ベースで推定するため、植物フェノタイピング手法に該当します。

Code and data availability

The paper describes a self-collected UAV rice canopy disease/pest dataset (1000 images, 600 annotated, augmented to 2000) and an improved YOLOv5 model, but no supplied block contains any public dataset deposit, image repository, code release, or trained model checkpoint. There is no data or code availability statement,

No evidence-backed public reproduction asset is currently recorded.