LettuceTrack: Detection and tracking of lettuce for robotic precision spray in agriculture - Hu, Nan; Su, Daobilige; Wang, Shuo; Nyamsuren, Purevdorj; Qiao, Yongliang; Jiang, Yu; Cai, Yu (Frontiers in Plant Science, 2022)
Precision spray of liquid fertilizer and pesticide to plants is an important task for agricultural robots in precision agriculture. By reducing the amount of chemicals being sprayed, it brings in a more economic and eco-friendly solution compared to conventional non-discriminated spray. The prerequisite of precision spray is to detect and track each plant. Conventional detection or segmentation methods find all plants in the image captured under the robotic platform, without knowing the ID of the plant. To spray each plant exactly once, tracking of every plant is needed in addition to detection. In this paper, we present LettuceTrack, a novel MOT method to simultaneously detect and track lettuces. When the ID of each plant is obtained from the tracking method, the robot knows whether a plant has been sprayed before so that it will only spray the plant that has not been sprayed. The proposed method adopts YOLO-V5 for detection of the lettuces, and a novel plant feature extraction and data association algorithms are introduced to effectively track all plants. The proposed method can recover the ID of a plant even if the plant moves out of field of view of camera before, for which existing MOT methods usually fail and assign a new plant ID.Experiments are conducted to show the effectiveness of the proposed method, and comparison with four state-of-the-art MOT methods is shown to prove the superior performance of the proposed method in the lettuce tracking application as well as its limitations. Though the proposed method is tested with lettuce, it can be potentially applied to other vegetables such as broccoli or sugar beat.