Affiliation:
1. College of Environmental Resources, Chongqing Technology and Business University, Chongqing 400067, China
2. Chongqing Engineering Research Center for Processing, Storage and Transportation of Characterized Agro-Products, Chongqing 400067, China
3. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Abstract
An apple-picking robot is now the most widely accepted method in the substitution of low-efficiency and high-cost labor-intensive apple harvesting. Although most current research on apple-picking robots works well in the laboratory, most of them are unworkable in an orchard environment due to unsatisfied apple positioning performance. In general, an accurate, fast, and widely used apple positioning method for an apple-picking robot remains lacking. Some positioning methods with detection-based deep learning reached an acceptable performance in some orchards. However, apples occluded by apples, leaves, and branches are ignored in these methods with detection-based deep learning. Therefore, an apple binocular positioning method based on a Mask Region Convolutional Neural Network (Mask R-CNN, an instance segmentation network) was developed to achieve better apple positioning. A binocular camera (Bumblebee XB3) was adapted to capture binocular images of apples. After that, a Mask R-CNN was applied to implement instance segmentation of apple binocular images. Then, template matching with a parallel polar line constraint was applied for the stereo matching of apples. Finally, four feature point pairs of apples from binocular images were selected to calculate disparity and depth. The trained Mask R-CNN reached a detection and segmentation intersection over union (IoU) of 80.11% and 84.39%, respectively. The coefficient of variation (CoV) and positioning accuracy (PA) of binocular positioning were 5.28 mm and 99.49%, respectively. The research developed a new method to fulfill binocular positioning with a segmentation-based neural network.
Funder
Natural Science Foundation for Chongqing
National Foreign Expert Project, Ministry of Science and Technology, China
Subject
Agronomy and Crop Science
Reference39 articles.
1. UN Food & Agriculture Organization (2021, April 10). Production of Apple (Fruit) by Countries. Available online: http://www.fao.org/faostat/en/#data/QCL.
2. A hierarchical approach to apple identification for robotic harvesting;Silwal;Trans. ASABE,2016
3. Design and control of an apple harvesting robot;Zhao;Biosyst. Eng.,2011
4. System design and control of an apple harvesting robot;Zhang;Mechatronics,2021
5. Fruit detection in an apple orchard using a mobile terrestrial laser scanner;Gregorio;Biosyst. Eng.,2019
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献