Abstract
Automatic harvesting consists of two main sub-steps: target recognition and picking/detachment of recognized targets. Target fruit recognition is a machine vision task that has been the subject of much research ever since the automatic harvesting was first introduced in the early 1960s [1]. The methods used for recognition largely depend on the properties of the fruit being harvested. Fruits, such as strawberries and tomatoes, can be relatively easily detected by a simple RGB color segmenting as the color of a ripe fruit differs significantly from both unripe fruits and the surrounding foliage, while fruits, such as green apples and green peppers, might require spectral analysis to distinguish them from the surrounding foliage [2]. In all cases, however, the recognition is greatly complicated by issues such as changing illumination conditions, shadows, occlusions of fruits by surrounding leaves and other fruits, color and shape variations and reflectance.
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References
Li, P., Lee, S., Hsu, H.Y.: Review on fruit harvesting method for potential use of automatic fruit harvesting systems. Procedia Eng. 23, 351–366 (2011)
Kapach, K., Barnea, E., Mairon, R., Edan, Y., Ben-Shahar, O.: Computer vision for fruit harvesting robots – state of the art and challenges ahead. Int. J. Comput. Vis. Robot. 3(1/2), 4–34 (2012)
Blanes, C., Mellado, M., Ortiz, C., Valera, A.: Review technologies for robot grippers in pick and place operations for fresh fruits and vegetables. Span. J. Agric. Res. 9(4), 1130–1141 (2011)
Bouguet, J.: Camera Calibration Toolbox For Matlab (1999). http://www.vision.caltech.edu/bouguetj/index.html. Accessed 22 Mar 2016
Tran, T.N., Drab, K., Daszykowski, M.: Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometr. Intell. Lab. Syst. 120, 92–96 (2013)
Myronenko, A., Song, X.: Point set registration: coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2262–2275 (2010)
Eizentals, P., Tokunaga, T., Oka K.: Design of a monorail type green pepper automatic harvesting robot. In: Proceedings of the Robotics and Mechatronics Conference, Yokohama, Japan, 8–11 June 2016
Tokunaga, T., Eizentals, P., Oka, K.:
(only in Japanese). In: Proceedings of the Robotics and Mechatronics Conference, Yokohama, Japan, 8–11 June 2016
(only in Japanese). In: Proceedings of the Robotics and Mechatronics Conference, Yokohama, Japan, 8–11 June 2016Author information
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Eizentals, P., Oka, K., Harada, A. (2017). Fruit Pose Estimation and Stem Touch Detection for Green Pepper Automatic Harvesting. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_38
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DOI: https://doi.org/10.1007/978-3-319-50115-4_38
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