Author ORCID Identifier

https://orcid.org/ 0000-0001-6488-0201

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences, 2. ENGINEERING AND TECHNOLOGY, Remote sensing

Publication Details

Copyright 2021 IEEE. Published in the IEEE 2021 International Geoscience & Remote Sensing Symposium (IGARSS 2021), scheduled for July 11 - 16, 2021 in Brussels, Belgium. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966.

Abstract

In precision agriculture, having knowledge of pastureland forage biomass and moisture content prior to an ensiling process enables pastoralists to enhance silage production. While traditional trait measurement estimation methods relied on hand-crafted vegetation indices, manual measurements, or even destructive methods, remote sensing technology coupled with state-of-the-art deep learning algorithms can enable estimation using a broader spectrum of data, but generally require large volumes of labelled data, which is lacking in this domain. This work investigates the performance of a range of deep learning algorithms on a small dataset for biomass and moisture estimation that was collected with a compact remote sensing system designed to work in real time. Our results showed that applying transfer learning to Inception ResNet improved minimum mean average percentage error from 45.58% on a basic CNN, to 28.07% on biomass, and from 29.33% to 8.03% on moisture content. From scratch models and models optimised for mobile remote sensing applications (MobileNet) failed to produce the same level of improvement.

DOI

https://doi.org/10.1109/IGARSS47720.2021.9553222

Funder

Enterprise Ireland Innovation Partnership IP 2018 0728 and Tanco Autowrap Ltd.


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