面向大豆害虫的改进计算机视觉检测与计数方法
面向大豆害虫的改进计算机视觉检测方法，属于智慧农业细分领域，方法新颖且有数据支撑，但影响范围限于专业领域。
原文：Automated Detection and Counting of Redbanded Stink Bugs in Soybean Using an Improved Computer Vision Model。研究使用2281张田间图像训练YOLOv8变体，加入注意力模块和自适应空间特征融合后，最佳模型精确率96.30%、召回率76.40%、F1值85.20%，mAP50为82.00%。成果可辅助大豆害虫监测，但数据集与部署环境仍限制外推。
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ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Food, Agriculture and Water
Volume 9 - 2026 | doi: 10.3389/frai.2026.1875232
Frontiers in Artificial Intelligence * #### AI in Food, Agriculture and Water * 6.7 impact factor * 8 citescoreReviewers
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Outline
ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. AI in Food, Agriculture and Water
Automated Detection and Counting of Redbanded Stink Bugs in Soybean Using an Improved Computer Vision Model
-
S U
Saurav Upadhyaya -
Louisiana State University, Baton Rouge, United States
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Abstract
The redbanded stink bug (RBSB), Piezodorus guildinii, is a major economic pest of soybean, with feeding damage that leads to significant yield losses and increased reliance on pesticide applications. Current management practices depend on manual identification and repeated field counting, which are labor-intensive, time-consuming, and prone to human error, particularly across large production areas. To address these limitations, this study evaluated the potential of computer vision models to automatically detect and count RBSB adults using imagery. A total of 2,281 field images were collected under varying stink bug densities using different sensors. These images were used to train multiple YOLOv8 model variants for automated detection and counting. The best-performing model was further enhanced by integrating a convolutional block attention modu