Robust leaf detection and tracking in greenhouse environments

Abstract

Over the last three decades, labor shortages and increased labor costs in greenhouses have driven investments in the development of agricultural robots. Priva has been developing a robot to automate the repetitive task of deleafing tomato plants. The main challenges for commercializing the robot are in terms of cost-efficiency and cut-quality. High success rates in the detection of leaves and subsequent cutting action are required, which are limited by occlusion and the unstructured, dynamic greenhouse environment. As a consequence of manipulator constraints, detected leaves can not always be cut from the current robot position. In addition, detected leaves with an unfavorable approach angle are skipped as cut quality can not be guaranteed. Conversely, many leaves are not detected at positions from where they can be cut due to detection limitations. By combining detections from different viewpoints, the detection rate can be increased significantly. Moreover, fusing multiple detections of the same object is known to improve detection accuracy. In this thesis, object trackers are investigated, aiming to increase the number of successfully cut leaves by tracking leaves over different robot positions and fusing multiple detections of the same leaf.