机采茶叶嫩芽的图像采集与识别

Image acquisition and recognition of machine-harvested tea buds

为了提高茶叶机采的智能化水平,笔者设计了由支架、弧形采收刀、割刀丝杆升降板、4个滚轮、2个驱动电机、控制器与蓄电池组等组成的茶叶采摘机试验平台;以YOLOv5s 6.0作为基础模型,将主干网络替换为MobilenetV3网络,在算法检测层前引入CBAM注意力模块,同时引入轻量级通用上采样算子CARAFE代替最近邻插值法,并通过添加权衡函数,改进CIOU损失函数等,建立茶叶嫩芽图像采集的数学模型YOLOv5s+。随后,以不同高度(10、20、30、40、50 cm)和角度(15°、30°、45°、60°、75°、90°)拍照的茶叶嫩芽图片为样本,检测其对网络识别精度的影响,发现当图像采集距离茶树顶部20 cm、拍摄角度为45°时,识别模型的训练结果最优。采用此参数下拍摄的图片集进行消融试验,YOLOv5s+对茶叶嫩芽识别的平均精度均值和召回率分别为0.935、0.912,较YOLOv5s的分别提高了2.97%、2.82%。

To enhance the intelligence level of mechanical tea harvesting,the author designed a tea harvesting experimental platform consisting of a support frame,arc-shaped harvesting blade,blade screw lifting plate,4 rollers,2 drive motors,controller,and battery pack.Using YOLOv5s 6.0 as the baseline model,several modifications were implemented:the backbone network was replaced with MobilenetV3;a CBAM attention module was integrated before the detection layer;the lightweight universal upsampling operator CARAFE was adopted to substitute the nearest neighbor interpolation method.Furthermore,by incorporating trade-off functions and enhancing the CIOU loss function,a novel mathematical model YOLOv5s+was developed for tea leaf detection.Subsequently,tea bud images taken at different heights(10,20,30,40,50 cm)and angles(15°,30°,45°,60°,75°,90°)were used as samples to test their impact on network recognition accuracy.The results demonstrated that optimal model performance was achieved when images were acquired at a 20 cm vertical distance from the tea tree canopy with a 45°shooting angle.Using the image set captured under these parameters for ablation experiments,YOLOv5s+achieved mean average precision and recall rates of 0.935 and 0.912 respectively for tea bud recognition,showing improvements of 2.97%and 2.82%compared to YOLOv5s.