  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.
