Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots
Nan Hu, Daobilige Su, Shuo Wang, Xuechang Wang, Huiyu, Zhong, Zimeng Wang, Yongliang Qiao, Yu Tan

TL;DR
This paper introduces a novel multiple object tracking and segmentation method tailored for vegetable plants, enhancing precision in robotic agricultural spraying by accurately identifying and tracking individual plants despite visual similarities.
Contribution
The proposed method improves vegetable plant tracking by combining contour and blob features, enabling re-identification and ensuring each plant is sprayed exactly once, outperforming existing methods.
Findings
Superior performance over two SOTA MOTS methods
Effective re-identification of re-appearing vegetables
Applicable to various vegetable types beyond lettuce
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…
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Taxonomy
TopicsSmart Agriculture and AI
