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Texture-based fruit detection

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Abstract

In this paper, a technique based on texture analysis is proposed for detecting green fruits on plants. The method involves interest point feature extraction and descriptor computation, interest point classification using support vector machines, candidate fruit point mapping, morphological closing and fruit region extraction. In an empirical study using low-cost web camera sensors suitable for use in mechanized systems, 24 combinations of interest point features and interest point descriptors were evaluated on two fruit types (pineapple and bitter melon). The method is highly accurate, with single-image detection rates of 85 % for pineapples and 100 % for bitter melons. The method is thus sufficiently accurate for precise location and monitoring of textured fruit in the field. Future work will explore combination of detection and tracking for further improved results.

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Acknowledgments

SC was supported by the Thailand National Science and Technology Development Agency (NSTDA). The authors thank the members of the AIT Vision and Graphics Lab (VGL) for suggestions and help with data collection.

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Correspondence to Supawadee Chaivivatrakul.

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Chaivivatrakul, S., Dailey, M.N. Texture-based fruit detection. Precision Agric 15, 662–683 (2014). https://doi.org/10.1007/s11119-014-9361-x

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