无人机图像数据驱动的莴苣属株高检测

Unmanned aerial vehicle image data-driven detection of lettuce plant height

莴苣属作物高通量株高数据采集技术的研究和应用较少,对此提出一种基于深度学习和无人机倾斜摄影的莴苣属株高检测方法。首先针对高通量植株株高获取,采用无人机倾斜摄影,生成区域内植株三维模型,提取高程信息;然后利用改进注意力机制的YOLOv5算法,在主干网络C3模块中嵌入CBAM注意力机制,减少浅层噪声信息,提高对小目标及密集目标的检测能力,以达到对区域内植株的目标检测,对应得到每株植株的估算株高。实验结果表明,CBAM-YOLOv5模型识别效果提升明显,对莴苣属作物识别的AP值提升到了96.19%,相较于原始的YOLOv5模型,本文模型的AP值提升了1.5%,植株目标检测对应三维模型计算出的估算值与实测值具有较高的相关性,直线斜率为0.9911,R^(2)为0.9311,实现了对莴苣属作物高通量株高数据的检测。

The study and application of high-throughput plant height data acquisition technology for lettuce crops are limited.A lettuce plant height detection method based on deep learning and drone oblique photography is proposed to address this.Firstly,oblique photography by drone is used to obtain high-throughput plant height data,and a 3D model of plants within the region is generated to extract elevation information.Then,an improved YOLOv5 algorithm with a CBAM attention mechanism embedded in the C3 module of the backbone network is proposed.This algorithm is designed to reduce shallow noise information,enhance the detection capability of small and dense targets,and achieve target detection of plants in the region.This will result in estimated plant heights for each plant.The experimental results show that the CBAM-YOLOv5 model significantly improves the recognition effect,increasing the AP value for lettuce crop recognition to 96.19%.Compared with the original YOLOv5 model,the AP value of our model has increased by 1.5%.The plant target detection has a high correlation between the estimated values calculated from the 3D model and the measured values,with a linear slope of 0.9911 and R^(2)-value of 0.9311,achieving the detection of high-throughput plant height data for lettuce crops.