A review of key techniques of vision-based control for harvesting robot

By Yuanshen Zhao, Liang Gong, Yixiang Huang, Chengliang Liu. Computers and Electronics in Agriculture, 2016

357 citations recorded by the source corpus.

How researchers see this paper

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Research and development in agricultural robotics: a perspective of digital farming. (2018, background): “Improvements in vision-based control system [7,48,49,86,87] have enabled several applications of robotic manipulators for greenhouse and orchard tasks and have contributed to the decrease in workload and labor’s fatigue while improving the efficiency and safety of the operations.”
YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3 (2020, background): “With the development of artificial intelligence, much of this work can be replaced by a harvesting robot [1].”
Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk (2019, background): “Considering the robot services, many researchers have done intensive research in order to mature the sensitivity of fruit detection, its shape, size, color, and localization [166]–[169].”
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021, methodology): “Moreover, the proposed method was compared with the other approaches like AdaBoost (Zhao et al., 2016b), YOLO model (Redmon et al., 2016), Circular Gabor Filter, and Eigen Fruit (Kurtulmus et al., 2011).”