Kirk, R., Mangan, M. orcid.org/0000-0002-0293-8874 and Cielniak, G. (2020) Feasibility Study of In-Field Phenotypic Trait Extraction for Robotic Soft-Fruit Operations. In: Fox, C., Duckett, T. and Richards, A., (eds.) UKRAS20 Conference : “Robots into the real world” Proceedings. UKRAS20 Conference: "Robots into the real world", 17 Apr 2020, Virtual Conference. EPSRC UK-RAS Network , pp. 21-23.
Abstract
There are many agricultural applications that would benefit from robotic monitoring of soft-fruit, examples include harvesting and yield forecasting. Autonomous mobile robotic platforms enable digitisation of horticultural processes in-field reducing labour demand and increasing efficiency through con- tinuous operation. It is critical for vision-based fruit detection methods to estimate traits such as size, mass and volume for quality assessment, maturity estimation and yield forecasting. Estimating these traits from a camera mounted on a mobile robot is a non-destructive/invasive approach to gathering qualitative fruit data in-field. We investigate the feasibility of using vision- based modalities for precise, cheap, and real time computation of phenotypic traits: mass and volume of strawberries from planar RGB slices and optionally point data. Our best method achieves a marginal error of 3.00cm3 for volume estimation. The planar RGB slices can be computed manually or by using common object detection methods such as Mask R-CNN.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. | ||||
Keywords: | phenotyping; mobile robots; computer vision | ||||
Dates: |
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Institution: | The University of Sheffield | ||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) | ||||
Funding Information: |
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 15 Dec 2021 14:18 | ||||
Last Modified: | 15 Dec 2021 14:18 | ||||
Status: | Published | ||||
Publisher: | EPSRC UK-RAS Network | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.31256/uk4td6i |