[基于Faster R-CNN的作物密度智能识别方法]
Xiuhua Li1,2, Qian Li1, Hanwen Zhang1
1School of Electrical Engineering, Guangxi University, Nanning 530004, Guangxi, China.
Sheng wu gong cheng xue bao = Chinese journal of biotechnology
|November 7, 2025
概括
本研究介绍了一种有效的方法,用于使用无人机 (UAV) 计数密集的香苗,并改进了Faster R-CNN算法. "切割-识别-合"策略显著提高了精准农业中作物密度监测的准确性.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 准确的作物数量和密度评估对于优化资源分配 (水,肥料) 和确保作物产量和质量至关重要.
- 无人机为农作物监测提供快速大面积数据采集,但识别密集的小目标仍然具有挑战性.
- 现有的算法在空中成像中扎着高分辨率和目标密度,需要先进的识别方法.
研究的目的:
- 开发和验证一种有效和准确的方法,用于识别和计数基于无人机的空中图像的密香苗.
- 为应对用于精密农业的高分辨率图像中密集的小物体识别所带来的挑战.
- 通过先进的图像处理和深度学习技术,提高作物密度监测的可靠性.
主要方法:
- 采用了"切割识别拼接"的策略,包括将高分辨率图像切割成,并使用改进的Faster R-CNN算法来识别幼苗.
- 应用了对比限度自适应式直方体平衡 (CLAHE) 来提高图像质量,并构建了一个由36000个图像组成的数据集.
- 开发了一个边界除重复算法,以纠正在切割的图像上重复识别幼苗,从而完善最终计数.
主要成果:
- 优化的Faster R-CNN模型在不同尺寸的香苗数据集中实现了0.99的最大识别精度.
- 脱复制算法将原始空中图像的平均计数错误从1.60%降低到0.60%.
- 香苗的总体平均计数准确率达到令人印象深刻的99.4%.
结论:
- 提出的"切割-识别-合"方法有效地克服了在高分辨率空中图像中识别密集的小物体的困难.
- 这种方法为智能作物密度监测提供了高效可靠的技术解决方案,这对精准农业至关重要.
- 该研究证明了先进的计算机视觉技术在提高农业管理和生产率方面的潜力.
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