Abstract:

Rehmannia glutinosa is an important traditional Chinese medicinal herb. Its large-scale cultivation is often affected by various pests, leading to frequent leaf pest-induced holes that severely impact yield and quality. However, current deep learning-based Pest-induced Hole detection methods face challenges such as complex field environments, small target scales, and irregular morphological characteristics. To address these issues, this paper presents the first Rehmannia Leaf Pest-induced Hole Dataset (RPHD) specifically designed for complex field environments. The dataset comprises an initial version (298 images with 5,059 annotations) and a refined version (291 images with 2,678 high-quality annotations). Through a systematic image preprocessing pipeline, including content-aware cropping and resolution normalization (1024×1024), adaptive bilateral filtering for noise reduction, and illumination equalization, combined with meticulous manual annotation and multiple rounds of cross-validation quality control mechanisms, the annotation consistency and target discriminability of the dataset were significantly improved. Three mainstream object detection models—YOLOv10n, YOLOv11n, and YOLOv12n—were employed to evaluate the dataset, comparing the performance of the two versions across different models. Experimental results show that training on the refined dataset yields substantial performance improvements across all models:YOLOv10n: mAP@0.5 increased from 40.3% to 87.2%;YOLOv11n: mAP@0.5 increased from 48.9% to 92.1%;YOLO12n: mAP@0.5 increased from 51.2% to 93.0%. This dataset provides standardized, high-quality benchmark data for research on small-target pest and disease detection algorithms for Rehmannia and other crops, contributing to the practical and intelligent development of agricultural vision detection technologies.

Data summary:

Key words: Rehmannia glutinosa, Pest-induced Hole detection, image dataset, data annotation, YOLO, agricultural artificial intelligence