[论文解读] Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

Pappu Kumar Yadav, J. Alex Thomasson+10 more|arXiv (Cornell University)|Jul 14, 2022

Forest Insect Ecology and Management被引用 4

一句话总结

本研究提出了一种基于YOLOv3的深度学习方法，利用无人飞行器（UAV）获取的RGB航拍图像检测玉米田中的志愿棉株。该方法在三种图像尺度（320×320、416×416、512×512）下均实现了稳定的mAP，展示了其在自动化检测和靶向喷洒方面防止害虫再侵染的稳健实时潜力。

ABSTRACT

The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.

研究动机与目标

• 评估使用YOLOv3通过RGB UAV影像检测玉米田中志愿棉株的可行性。
• 评估YOLOv3在三种图像分辨率尺度（320×320、416×416、512×512）下的检测精度表现。
• 确定该模型是否能够支持农业害虫管理的近实时遥感应用。
• 研究图像尺度对关键指标（平均精度（AP）、平均平均精度（mAP）和F1-score）的影响。
• 支持开发用于检测和靶向志愿棉株的自动化系统，以防止棉铃象虫再侵染。

提出的方法

• 使用无人驾驶航空器系统（UAS）在含有志愿棉株的玉米田上获取三波段RGB航拍图像。
• 在标注的航拍图像上训练YOLOv3目标检测模型，以识别处于针头大小棉桃阶段（5–6叶期）的志愿棉株。
• 在三种固定输入图像尺寸下评估模型性能：S1（320×320）、S2（416×416）和S3（512×512像素）。
• 使用标准指标测量检测性能：在95%置信水平下的平均精度（AP）、平均平均精度（mAP）和F1-score。
• 进行统计分析（p值）以比较三种尺度下模型性能的差异。
• 采用基于计算机视觉的遥控航空应用系统（RPAAS）框架，以支持潜在的实时部署。

实验结果

研究问题

• RQ1 YOLOv3能否仅通过RGB UAV影像有效检测玉米田中的志愿棉株？
• RQ2 输入图像分辨率（320×320、416×416、512×512）如何影响YOLOv3在mAP、AP和F1-score方面的检测性能？
• RQ3 三种图像尺度之间的检测性能是否存在显著统计差异？
• RQ4 训练好的YOLOv3模型能否支持农业遥感系统中实时检测与喷洒应用？
• RQ5 该模型是否在不同尺度下保持一致的mAP，表明其在实际应用中的可扩展性？

主要发现

• 在三种图像尺度（S1、S2、S3）之间未发现平均平均精度（mAP）存在显著差异，表明模型具有稳定的鲁棒性。
• 在S1与S3之间（p = 0.04）以及S2与S3之间（p = 0.02）的平均精度（AP）存在显著统计差异。
• S2与S3之间的F1-score也存在显著差异（p = 0.02），表明S3在精确率与召回率之间具有更好的平衡。
• mAP在各尺度间无显著差异，支持将训练好的YOLOv3模型用于RPAAS平台上的近实时应用。
• 该模型在自动化、基于UAV的志愿棉株检测与靶向系统中展现出强大的部署潜力。
• 结果证实，YOLOv3可有效用于仅基于RGB影像在玉米田中早期检测志愿棉株。