NDLI: Low and high-level visual feature-based apple detection from multi-modal images

Content Provider	Springer Nature Link
Author	Wachs, J. P. Stern, H. I. Burks, T. Alchanatis, V.
Copyright Year	2010
Abstract	Automated harvesting requires accurate detection and recognition of the fruit within a tree canopy in real-time in uncontrolled environments. However, occlusion, variable illumination, variable appearance and texture make this task a complex challenge. Our research discusses the development of a machine vision system, capable of recognizing occluded green apples within a tree canopy. This involves the detection of “green” apples within scenes of “green leaves”, shadow patterns, branches and other objects found in natural tree canopies. The system uses both thermal infra-red and color image modalities in order to achieve improved performance. Maximization of mutual information is used to find the optimal registration parameters between images from the two modalities. We use two approaches for apple detection based on low and high-level visual features. High-level features are global attributes captured by image processing operations, while low-level features are strong responses to primitive parts-based filters (such as Haar wavelets). These features are then applied separately to color and thermal infra-red images to detect apples from the background. These two approaches are compared and it is shown that the low-level feature-based approach is superior (74% recognition accuracy) over the high-level visual feature approach (53.16% recognition accuracy). Finally, a voting scheme is used to improve the detection results, which drops the false alarms with little effect on the recognition rate. The resulting classifiers acting independently can partially recognize the on-tree apples, however, when combined the recognition accuracy is increased.
Starting Page	717
Ending Page	735
Page Count	19
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	11
Issue Number	6
e-ISSN	15731618
Language	English
Publisher	Springer US
Publisher Date	2010-10-14
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mutual information Multi-modal registration Sensor fusion Haar detector Apple detection Meteorology/Climatology Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Remote Sensing/Photogrammetry Soil Science & Conservation Agriculture
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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