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基于作物信号和多视图成像的豆苗自动定位

Bo Jiang¹, He-Yi Zhang¹, Wen-Hao Su¹

¹College of Engineering, China Agricultural University, Haidian, Beijing 100083, China.

Sensors (Basel, Switzerland)

|May 25, 2024

概括

研究人员开发了一种使用Rhodamine B (Rh-B) 和计算机视觉的新方法,以准确检测用于机械除草的大豆. 这种基于光的作物信号技术在不损害植物生长的情况下达到96.7%的准确性.

科学领域:

农业工程农业工程
植物科学植物科学
计算机视觉计算机视觉

背景情况:

大豆种植中的杂草控制受到除草剂耐药性和手工除草成本上升的挑战.
机械杂草控制是首选的,但与精确的行内杂草和作物检测斗争.
缺乏快速准确的大豆定位技术,阻碍了高效的机械除草.

研究的目的:

利用系统性作物化合物和计算机视觉开发和验证一种用于精确识别和定位大豆的系统.
探索大豆中的罗达胺B (Rh-B) 的光特性及其对植物发育的影响.
为了建立一个最佳的Rh-B治疗有效的大豆信号,而不会损害作物健康.

主要方法:

用不同度的罗达胺B (Rh-B) 溶液对待大豆苗.
分析了对待的大豆中的光分布和强度.
在处理后评估了植物生长,生物质和种子发芽.
一个光成像系统被开发用于大豆的识别和定位.
使用300个数据集评估了系统的准确性.

主要成果:

24小时内180ppm的Rh-B度被确定为最佳剂量,产生显著的光,不会对大豆生长产生不良影响.
较高的Rh-B度减少了作物生物质,长时间暴露减少了种子发芽.
诱导的光持续了20天,在早期生长阶段提供了稳定的信号.
开发的光成像系统在识别和定位大豆植物方面实现了96.7%的准确性.

结论:

系统性作物化合物,如Rh-B,与计算机视觉相结合,为精确的作物定位提供了可行的解决方案.
这种基于光的作物信号技术可以显著提高机械除草系统的效率和准确性.
这些发现表明机器视觉和作物信号在自动化农业操作中的潜力.

关键词:

计算机视觉计算机视觉精准农业精准农业精准农业快速检测植物检测快速检测植物系统作物信号信号系统

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