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Statistical models for fruit detectability: spatial and temporal analyses of sweet peppers
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.ORCID iD: 0000-0003-4685-379x
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.ORCID iD: 0000-0002-7430-8468
2018 (English)In: Biosystems Engineering, ISSN 1537-5110, E-ISSN 1537-5129, Vol. 171, p. 272-289Article in journal (Refereed) Published
Abstract [en]

Statistical models for fruit detectability were developed to provide insights into preferable variable configurations for better robotic harvesting performance.

The methodology includes several steps: definition of controllable and measurable variables, data acquisition protocol design, data processing, definition of performance measures and statistical modelling procedures. Given the controllable and measurable variables, a data acquisition protocol is defined to allow adequate variation in the variables, and determine the dataset size to ensure significant statistical analyses. Performance measures are defined for each combination of controllable and measurable variables identified in the protocol. Descriptive statistics of the measures allow insights into preferable configurations of controllable variables given the measurable variables values. The statistical model is performed by back-elimination Poisson regression with a loglink function process. Spatial and temporal analyses are performed.

The methodology was applied to develop statistical models for sweet pepper (Capsicum annuum) detectability and revealed best viewpoints. 1312 images acquired from 10 to 14 viewpoints for 56 scenes were collected in commercial greenhouses, using an eye-in-hand configuration of a 6 DOF manipulator equipped with a RGB sensor and an illumination rig. Three databases from different sweet-pepper varieties were collected along different growing seasons.

Target detectability highly depends on the imaging acquisition distance and the sensing system tilt. A minimum of 12 training scenes are necessary to discover the statistically significant spatial variables. Better prediction was achieved at the beginning of the season with slightly better prediction achieved in a temporal split of training and testing sets.

Place, publisher, year, edition, pages
Academic Press, 2018. Vol. 171, p. 272-289
Keywords [en]
Detectability modelling, Spatial viewpoints, Sweet pepper, Fruit detectability, Harvesting, Robotics
National Category
Robotics
Research subject
Statistics; Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-79406DOI: 10.1016/j.biosystemseng.2018.04.017ISI: 000437080100021Scopus ID: 2-s2.0-85047946480OAI: oai:DiVA.org:oru-79406DiVA, id: diva2:1388956
Funder
EU, Horizon 2020, 644313
Note

This research was supported by the European Commission (SWEEPER GA no. 644313) and by Ben-Gurion University of the Negev through the Helmsley Charitable Trust (2012PG-ISL007), the Agricultural, Biological and Cognitive Robotics Initiative, the Marcus Endowment Fund and the Rabbi W. Gunther Plaut Chair in Manufacturing Engineering.

Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-01-28Bibliographically approved

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Kurtser, Polina

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