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2021 | OriginalPaper | Chapter

Just-in-Time Biomass Yield Estimation with Multi-modal Data and Variable Patch Training Size

Authors : Patricia O’Byrne, Patrick Jackman, Damon Berry, Thomas Lee, Michael French, Robert J. Ross

Published in: Artificial Intelligence Applications and Innovations

Publisher: Springer International Publishing

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Abstract

This chapter delves into the optimization of silage production through accurate biomass yield estimation, crucial for sustaining milk and beef production. It highlights the challenges and limitations of traditional vegetation indices and introduces a study that leverages multi-modal data, including LiDAR and NIR, to enhance biomass estimation. The research explores the impact of patch sizes on model performance and the benefits of transfer learning from ImageNet. The study also considers the environmental implications of deep learning methods, making it a comprehensive and innovative contribution to the field of precision agriculture.

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Metadata
Title
Just-in-Time Biomass Yield Estimation with Multi-modal Data and Variable Patch Training Size
Authors
Patricia O’Byrne
Patrick Jackman
Damon Berry
Thomas Lee
Michael French
Robert J. Ross
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-79150-6_20

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