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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(only in Japanese). In: Proceedings of the Robotics and Mechatronics Conference, Yokohama, Japan, 8–11 June 2016
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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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