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Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
University of Sydney, Australia.
University of Sydney, Australia.
2016 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 11, article id 1915Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel multi-sensor framework to efficiently identify, track, localise and map every piece of fruit in a commercial mango orchard. A multiple viewpoint approach is used to solve the problem of occlusion, thus avoiding the need for labour-intensive field calibration to estimate actual yield. Fruit are detected in images using a state-of-the-art faster R-CNN detector, and pair-wise correspondences are established between images using trajectory data provided by a navigation system. A novel LiDAR component automatically generates image masks for each canopy, allowing each fruit to be associated with the corresponding tree. The tracked fruit are triangulated to locate them in 3D, enabling a number of spatial statistics per tree, row or orchard block. A total of 522 trees and 71,609 mangoes were scanned on a Calypso mango orchard near Bundaberg, Queensland, Australia, with 16 trees counted by hand for validation, both on the tree and after harvest. The results show that single, dual and multi-view methods can all provide precise yield estimates, but only the proposed multi-view approach can do so without calibration, with an error rate of only 1.36% for individual trees.

Place, publisher, year, edition, pages
MDPI AG , 2016. Vol. 16, no 11, article id 1915
Keywords [en]
computer vision; agrivision; fruit detection; yield estimation; field robotics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-133754DOI: 10.3390/s16111915ISI: 000389641700148PubMedID: 27854271OAI: oai:DiVA.org:liu-133754DiVA, id: diva2:1063809
Available from: 2017-01-11 Created: 2017-01-09 Last updated: 2018-03-23

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