Texture-based fruit detection

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 a...

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Bibliographic Details
Published in:Precision agriculture 2014-12, Vol.15 (6), p.662-683
Main Authors: Chaivivatrakul, Supawadee, Dailey, Matthew N.
Format: Article
Language:English
Subjects:
Agriculture
Atmospheric Sciences
Biomedical and Life Sciences
Cameras
Chemistry and Earth Sciences
Computer Science
Crops
Embedded systems
Fruits
Harvest
Information processing
Life Sciences
Methods
Monitoring systems
Physics
Real time
Remote Sensing/Photogrammetry
Sensors
Soil Science & Conservation
Statistical analysis
Statistics for Engineering
Vision systems
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Summary: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.
ISSN:1385-2256
1573-1618
DOI:10.1007/s11119-014-9361-x