With the increasing deployment of agricultural robots, the traditional manual spray of liquid fertilizer and pesticide is gradually being replaced by agricultural robots. For robotic precision spray application in vegetable farms, accurate plant phenotyping through instance segmentation and robust plant tracking are of great importance and a prerequisite for the following spray action. Regarding the robust tracking of vegetable plants, to solve the challenging problem of associating vegetables with similar color and texture in consecutive images, in this paper, a novel method of Multiple Object Tracking and Segmentation (MOTS) is proposed for instance segmentation and tracking of multiple vegetable plants. In our approach, contour and blob features are extracted to describe unique feature of each individual vegetable, and associate the same vegetables in different images. By assigning a unique ID for each vegetable, it ensures the robot to spray each vegetable exactly once, while traversing along the farm rows. Comprehensive experiments including ablation studies are conducted, which prove its superior performance over two State-Of-The-Art (SOTA) MOTS methods. Compared to the conventional MOTS methods, the proposed method is able to re-identify objects which have gone out of the camera field of view and re-appear again using the proposed data association strategy, which is important to ensure each vegetable be sprayed only once when the robot travels back and forth. Although the method is tested on lettuce farm, it can be applied to other similar vegetables such as broccoli and canola. Both code and the dataset of this paper is publicly released for the benefit of the community: https://github.com/NanH5837/LettuceMOTS.

翻译：随着农业机器人的日益普及，传统的液体肥料和农药人工喷洒方式正逐渐被农业机器人所取代。在蔬菜农场的机器人精准喷雾应用中，通过实例分割实现精确的植物表型分析以及稳健的植物跟踪至关重要，这是后续喷雾动作的前提。针对蔬菜植物的稳健跟踪问题，为解决连续图像中颜色和纹理相似的蔬菜关联这一挑战性难题，本文提出了一种新颖的多目标跟踪与分割（MOTS）方法，用于多株蔬菜植物的实例分割与跟踪。在我们的方法中，提取轮廓和斑点特征以描述每株蔬菜的独特特征，并关联不同图像中的同一蔬菜。通过为每株蔬菜分配唯一标识符（ID），可确保机器人在沿农场行间移动时对每株蔬菜仅喷洒一次。包括消融实验在内的综合实验证明，该方法优于两种最先进的MOTS方法。与传统的MOTS方法相比，本方法能够利用所提出的数据关联策略，重新识别已移出相机视野后又重新出现的对象，这对于机器人在往返移动时确保每株蔬菜仅被喷洒一次至关重要。尽管该方法在生菜农场进行了测试，但它可应用于其他类似蔬菜，如西兰花和油菜。本文的代码和数据集已公开发布，以惠及学术界：https://github.com/NanH5837/LettuceMOTS。