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Authors: Kyle Ranslam and Ramon Lawrence

Affiliation: University of British Columbia, Kelowna, BC, Canada

Keyword(s): Database Integration, Deep Learning, Object Detection, Fruit, Apple.

Abstract: Feeding the world’s growing population requires research and development of fruit varieties that can be sustainably grown with high yields and quality and require low inputs of water and fertilizer. The process of developing new fruit varieties is data-intensive and traditionally uses manual processes that do not scale. The contribution of this work is a data analysis pipeline that automates the extraction of fruit characteristics from images and integrates multiple data sources (images, field measurements, human evaluation) to help direct the research to the most promising candidates and reduce the amount of manual time required for data collection and analysis. Initial results demonstrate that the image analysis is accurate and can be done at scale in a realworld environment.

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Paper citation in several formats:
Ranslam, K. and Lawrence, R. (2023). A Data Analysis Pipeline for Automating Apple Trait Analysis and Prediction. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 375-380. DOI: 10.5220/0012088300003541

@conference{data23,
author={Kyle Ranslam. and Ramon Lawrence.},
title={A Data Analysis Pipeline for Automating Apple Trait Analysis and Prediction},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA},
year={2023},
pages={375-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012088300003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - A Data Analysis Pipeline for Automating Apple Trait Analysis and Prediction
SN - 978-989-758-664-4
IS - 2184-285X
AU - Ranslam, K.
AU - Lawrence, R.
PY - 2023
SP - 375
EP - 380
DO - 10.5220/0012088300003541
PB - SciTePress