Feasibility Study of In-Field Phenotypic Trait Extraction for Robotic Soft-Fruit Operations

Feasibility Study of In-Field Phenotypic Trait Extraction for Robotic Soft-Fruit Operations

Title: Feasibility Study of In-Field Phenotypic Trait Extraction for Robotic Soft-Fruit Operations
Authors: Raymond Kirk (Lincoln Centre for Autonomous Systems, University of Lincoln); Michael Mangan (Sheffield Robotics, University of Sheffield); Grzegorz Cielniak (Lincoln Centre for Autonomous Systems, University of Lincoln);
Year: 2020
Citation: Kirk, R., Mangan, M., Cielniak, G., (2020). Feasibility Study of In-Field Phenotypic Trait Extraction for Robotic Soft-Fruit Operations. UKRAS20 Conference: “Robots into the real world” Proceedings, 21-23. doi: 10.31256/Uk4Td6I

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.

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