整合无人机遥感和半监督学习,用于早期玉米苗木监测和地理定位
Rui Yang1, Mengyuan Chen1, Xiangyu Lu1
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
Plant phenomics (Washington, D.C.)
|December 19, 2025
概括
这项研究提高了使用无人机 (UAV) 技术监测玉米苗的效果. 它改善了缺失的苗木的早期检测和地理位置,降低了成本,并帮助重新种植决策,以获得更好的作物产量.
科学领域:
- 农业工程 农业工程
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 有效的玉米苗木管理对于优化作物产量和质量至关重要.
- 无人驾驶飞行器 (UAV) 遥感提供了非破坏性的作物监测方法.
- 现有的基于无人机的玉米苗木监测面临诸如晚期检测,高标注成本和缺失植物的地理位置差等挑战.
研究的目的:
- 开发和评估一种改进的方法,用于在早期植被阶段 (至V2) 检测玉米苗.
- 调查半监督学习的有效性,使用未标记的数据来提高幼苗检测的准确性.
- 创建一个完整的管道,用于准确地质定位和缺失的玉米苗的状况分析,以支持再种植战略.
主要方法:
- 利用完整标记的数据集来评估现有和缺失的玉米苗的初始检测性能.
- 采用高效的教师半监督学习框架,利用未标记的数据来改善对象检测.
- 开发了一个端到端的管道,集成图像采集,种子分析和精确的地理位置测绘.
主要成果:
- 初始检测实现了高性能,92.06%的平均精度,87.44%的回忆率和91.23%的AP50.
- 在使用未标记数据时,半监督学习显著改善了检测指标 (平均精度,回忆,AP50分别高达2.69%,2.65%和2.35%).
- 开发的管道准确地绘制了缺失的苗木,平均偏差为0.462m,提供可操作的重新种植反.
结论:
- 早期的玉米苗监测 (V2阶段) 是可行的和有效的无人机遥感.
- 半监督学习提供了一种具有成本效益的方法,通过使用未标记的数据来提高检测准确性.
- 综合管道为识别和定位缺失的苗木提供了精确和实用的解决方案,优化了玉米田地管理.
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