西兰花选择性采收作业平台识别切割装置设计与试验

针对当前人工采收西兰花存在季节性劳动力需求强、劳动强度大以及成本高等问题,该研究基于西兰花的农艺特性与形态特征设计了一种西兰花选择性采收作业平台,旨在能够实现对西兰花的自主识别切割作业。首先,该平台采用“识别-采收”一体化作业模式,通过对采收作业平台的关键部件进行设计与选型,建立了西兰花的视觉识别系统和定心切割机构。其次,根据西兰花茎秆与割刀之间的相互作用关系,采用对数螺线作为切割曲线设计了一种等滑切角割刀,确定了割刀滑切角40o、切割半径135 mm、割刀长度260 mm等关键切割参数。根据西兰花茎秆的材料属性参数,基于ANSYS Workbench/LS-DYNA软件对茎秆切割过程进行显式动力学仿真分析,以割刀刃角和转速为控制因子,以最大切割力为试验指标,利用正交试验优化设计,确定了茎秆切割过程的最优参数组合为割刀刃角20°、转速1 rad/s,在此参数下最大切割力为725.82 N,切割质量较优。最后,对采收作业平台进行性能试验,结果表明视觉系统能够有效识别自然环境下的成熟西兰花植株,检测效果良好;定心切割机构可快速平稳的切入并切断西兰花茎秆,切断表面平整光滑;采收作业平台整体漏收率在10%以下、检测准确率为90%、切茎合格率为88.9%,可满足西兰花选择性采收的作业需求。本研究可为西兰花选择性采收作业装备的设计开发提供理论参考和实际借鉴。

Design and experiment of the identification cutting devices for a selective harvesting platform of broccoli

Broccoli (Brassica oleracea L.var.Italica Plenck) is one of the most important vegetables in recent years. However, manual batch harvesting cannot fully meet the high requirement of selective harvesting, due to the strong seasonal labor demand, high labor intensity, and high cost. In this study, a selective harvesting platform was designed to automatically identify and then cut the broccoli, according to the agronomic properties and morphologies. Firstly, the selective harvesting platform primarily consisted of a walking module, identification in cutting device, triggering collection device, and control system. The control system was acquired for the visual information of broccoli through the upper computer. The lower computer was used to control the overall functioning of the harvesting platform, including overall walking, plant localization, flower ball positioning, stalk cutting, and flower ball grasping. Secondly, an "identify-harvesting" integrated operation mode was designed to autonomously identify and cut operations in the broccoli selective harvesting platform. This operational mode was used to mitigate the interference from independent movements of the executing mechanisms during identification. The precise movement distances of executing mechanisms were calculated to enhance the operational accuracy. The key components of the harvesting platform were selected for the visual identity system and centering cutting mechanism for broccoli. The original image of broccoli was collected by the visual system and then processed through grayscale conversion, Gaussian filtering, and threshold segmentation. Thirdly, the dimension and centroid position of the broccoli head were calculated using pixel area and moment. A better performance was achieved in the image processing time of 2.5 s, the average contour deviation of 2.2%, and the measurement centroid position error of 4.2%. According to the interaction between the broccoli stalks and the cutting knife, a logarithmic spiral was utilized as the cutting curve, in order to design a constant slip angle cutting knife. The key cutting parameters were determined, such as a cutting knife sliding angle of 40o, a cutting radius of 135 mm, and a cutting knife length of 260 mm. Additionally, the explicit dynamic simulation of stalk cutting was conducted using ANSYS Workbench/LS-DYNA software, according to the material property parameters of broccoli stalks. Taking the cutting knife edge angle and rotational speed as the control factors, and the maximum cutting force as the experimental indicator, the optimal parameter combination of the stalk cutting was determined by the orthogonal experimental optimization. An optimal combination was obtained in the cutting knife edge angle of 20° and rotational speed of 1 rad/s. The maximum cutting force was 725.82 N, indicating better cutting quality. Finally, the cutting performance test showed that the centering cutting mechanism rapidly and smoothly cut into and cut off broccoli stalks, where the cutting surface was flat and smooth, with a cutting time of approximately 0.6 s per unit, and the 100% success rate of cutting. The performance tests on the harvesting platform showed that the visual system effectively recognized the mature broccoli plants in natural environments with better detection. The overall leakage rate of the harvesting platform was less than 10%, the detection accuracy was 90%, and the qualified rate of cutting stalks was 88.9%, fully meeting the operational requirements of selective harvesting of broccoli. This finding can provide the theoretical and practical reference for the design and development of selective harvesting equipment for broccoli.