Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots

• URL: http://arxiv.org/abs/2306.13518v2
• Date: Mon, 26 Jun 2023 12:24:26 GMT
• Title: Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots
• Authors: Nan Hu and Daobilige Su and Shuo Wang and Xuechang Wang and Huiyu Zhong and Zimeng Wang and Yongliang Qiao and Yu Tan
• Abstract summary: A novel method of Multiple Object Tracking and Tracking (MOTS) is proposed for instance segmentation and tracking of multiple vegetable plants. 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. Although the method is tested on lettuce farm, it can be applied to other similar vegetables such as broccoli and canola.
• Score: 8.328453345461478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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