异常水培生菜自动分选系统设计与试验

为解决水培生菜包装前分选机械化程度低、分选任务重的问题，结合深度学习方法设计了一种异常水培生菜自动分选系统。该系统由信息感知、信息处理以及分选动作执行3个子系统组成。根据水培生菜异常叶片与正常叶片间差异性进行水培生菜分类，采用从下向上的三摄像头配合拍摄方式进行图像信息感知，并基于语义分割DeepLabV3+深度学习网络实现水培生菜图像信息实时处理，其处理性能为：平均联合交并比达83.26%,像素精度为99.24%,单幅图像处理时间为(193.4±4) ms。为便于实现异常水培生菜分选，基于水培生菜的表型及采收模式，设计了一种托架式异常水培生菜分选执行子系统，并以横向支撑杆角度、纵向支撑杆角度和步进电机转速为试验因素，以分选动作执行子系统的分选成功率为评价指标，设计二次正交旋转组合试验。建立了各因素与指标间回归数学模型，运用Design-Expert软件的多目标优化算法进行参数优化。获得参数最优组合为：横向支撑杆角度146°、纵向支撑角度150°、步进电机转速11 r/min。依据参数最优组合进行性能试验，得到分选动作执行子系统的分选成功率为98%,异常水培生菜自动分选系统的分选成功率为...

Design and Experiment of Sorting System for Abnormal Hydroponic Lettuce

To address problems of low mechanization level and labor-intensive sorting tasks before packaging of hydroponic lettuce, an automatic sorting system for abnormal hydroponic lettuce was designed in combination with the deep learning method. The automatic sorting system was composed of an information perception sub-system, an information processing sub-system, and a sorting action execution sub-system. Hydroponic lettuce classification was based on the difference between abnormal and normal leaves. Three cameras from bottom to top were used to capture images. Real-time processing of hydroponic lettuce images was realized based on semantic segmentation DeepLabV3+. The image segmentation model had mIoU of 83.26%, PA of 99.24% and image processing velocity of (193.4±4)ms/frame. To realize sorting of abnormal hydroponic lettuce, a bracket-type hydroponic lettuce sorting sub-system was designed based on phenotype and harvesting mode of the hydroponic lettuce. Quadratic orthogonal rotational-combinational experiments were designed. Experiments on factoring in horizontal and longitudinal support rod angles and stepping motor speed were conducted to obtain the highest sorting success rate. Regression mathematical models between factors and index were multi-objectively optimized by using Design-Expert software. Optimal combination of parameters was obtained, including the horizontal support rod angle of 146°, the longitudinal support angle of 150°, and the stepping motor speed of 11r/min. Perform test was carried out according to the optimal combination of parameters. The sorting success rate of the sorting action execution sub-system was 98%, and the sorting success rate of the abnormal hydroponic lettuce automatic sorting system was 95%, which met technical standard requirements of lettuce refrigerated transportation.