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Sensors 2016, 16(11), 1915;

Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry

Division of Automatic Control Department of Electrical Engineering, Linköping University, Linköping SE-581 83, Sweden
The Australian Centre for Field Robotics (ACFR), Department of Aerospace, Mechanical and Mechatronic Engineering (AMME),The University of Sydney, Sydney, NSW 2006, Australia
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López
Received: 14 October 2016 / Revised: 9 November 2016 / Accepted: 9 November 2016 / Published: 15 November 2016
(This article belongs to the Special Issue Vision-Based Sensors in Field Robotics)
Full-Text   |   PDF [51264 KB, uploaded 15 November 2016]   |  


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. View Full-Text
Keywords: computer vision; agrivision; fruit detection; yield estimation; field robotics computer vision; agrivision; fruit detection; yield estimation; field robotics

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Stein, M.; Bargoti, S.; Underwood, J. Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry. Sensors 2016, 16, 1915.

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