在基于YOLOX的田中在播种阶段检测杂草目标
Xiangwu Deng1, Long Qi2, Zhuwen Liu1
1College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming, China.
PloS one
|December 13, 2023
概括
这项研究开发了一种使用YOLOX-tiny的计算机视觉方法,用于精确检测大米田中的杂草,提高作物产量并减少除草剂浪费. YOLOX-tiny模型为早期米生长提供了高精度和效率,支持自动杂草控制.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 杂草对大米作物构成重大威胁,特别是在早期生长阶段,导致大量的收获损失.
- 传统的大面积喷方法用于除草控制是低效的,导致除草剂浪费,并导致环境污染.
- 精确喷需要快速准确地检测大米田中的杂草分布.
研究的目的:
- 开发和评估基于深度学习的计算机视觉方法,用于检测大米田中的杂草目标.
- 为了应对在大米播种阶段识别小而密集的杂草目标的挑战.
- 为了使农业从广播喷转向精确杂草管理.
主要方法:
- 一种基于YOLOX模型的杂草目标检测方法,包含CSPDarknet骨干,特征金字塔网络 (FPN) 和YOLO头部探测器.
- 在多个尺度 (80x80,40x40,20x20像素) 中使用CSPDarknet进行特征提取.
- 与YOLOv3,YOLOv4-tiny,YOLOv5-s和SSD等其他模型相比,YOLOX-tiny的性能比较.
主要成果:
- YOLOX-tiny模型表现出卓越的性能,平均平均精度 (mAP) 为0.980,F1得分为0.95,回忆率为0.983.
- YOLOX-tiny只需要259.62 MB的中间变量内存,因此适合在智能农业设备上部署.
- 提出的方法有效地提高了在早期的田中检测小,庇护和密集的杂草的效果.
结论:
- YOLOX-tiny模型在田中检测杂草方面非常有效,特别是在播种阶段的小型和密集的目标.
- 开发的杂草检测模型适用于嵌入式系统,为农业机器人的自主向除草剂喷铺平了道路.
- 这项研究通过实现精确的杂草管理和减少对环境的影响,为可持续农业做出了贡献.
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