Abstract
At present, the primary technical deterrent to the use of strawberry harvesting robots is the low harvest rate, and there is a need to improve the accuracy and real-time performance of the localization algorithms to detect the picking point on the strawberry stem. The pose estimation of the fruit target (the direction of the fruit axis) can improve the accuracy of the localization algorithm. This study proposes a novel harvesting robot for the ridge-planted strawberries as well as a fruit pose estimator called rotated YOLO (R-YOLO), which significantly improves the localization precision of the picking points. First, the lightweight network Mobilenet-V1 was used to replace the convolution neural network as the backbone network for feature extraction. The simplified network structure substantially increased the operating speed. Second, the rotation angle parameter $\alpha $ was used to label the training set and set the anchors; the rotation of the bounding boxes of the target fruits was predicted using logistic regression with the rotated anchors. The test results of a set of 100 strawberry images showed that the proposed model's average recognition rate to be 94.43% and the recall rate to be 93.46%. Eighteen frames per second (FPS) were processed on the embedded controller of the robot, demonstrating good real-time performance. Compared with several other target detection methods used for the fruit harvesting robots, the proposed model exhibited better performance in terms of real-time detection and localization accuracy of the picking points. Field test results showed that the harvesting success rate reached 84.35% in modified situations. The results of this study provide technical support for improving the target detection of the embedded controller of harvesting robots.
Highlights
As one of the most widely grown berries in the world, strawberry can be cultivated in the outdoors or controlled environments such as greenhouses and polytunnels [1]
The model could predict the rotation of the bounding box of the fruit target, which greatly improved the localization precision of the picking point
rotated YOLO (R-YOLO) provided better detection performance and was more robust than traditional target detection methods based on machine vision for detecting strawberry fruits under different light intensities and for multiple overlapping fruits
Summary
As one of the most widely grown berries in the world, strawberry can be cultivated in the outdoors or controlled environments such as greenhouses and polytunnels [1]. Y. Yu et al.: Real-Time Visual Localization of the Picking Points for a Ridge-Planting Strawberry Harvesting Robot. A strawberry pose estimator called R-YOLO is proposed It can be transplanted into the embedded control device of the robot to address the problems discussed above and can determine the picking-point position in real-time. This estimator identifies the strawberry targets and generates the rotated bounding box containing the pose information of the fruit target. Designing a novel end-effector that is assembled on the servo control system of a strawberry harvesting robot suitable for the narrow ridge-planting mode.
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