Abstract

Understanding the amount of Soil Organic Carbon (SOC) at farm and field scale is a necessary precursor to effective management, important for both agricultural productivity and to reduce CO2 emissions. To avoid the prohibitive cost of measurement, SOC can be estimated by using multispectral images. In this study, we propose a novel Physics-Informed Convolutional Neural Network (CNN) to model well-known but noisy relationship between a soil index and SOC using the network’s loss function. This study is also conducted by resampling the European Land Use/Classification Area Survey (LUCAS) dataset to Sentinel-2 bands. Our experimental results show that our proposed network converges more quickly, has a lower root mean squared error (RMSE) and is more robust (as measured by the standard deviation of RMSE over multiple trials) than a compatible standard CNN. The operation of the novel Physics-Informed CNN is explained in terms of the components of the loss function.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2024 - 17th Asian Conference on Computer Vision, Proceedings
EditorsMinsu Cho, Ivan Laptev, Du Tran, Angela Yao, Hongbin Zha
PublisherSpringer Science and Business Media Deutschland GmbH
Pages366-383
Number of pages18
ISBN (Print)9789819609628
DOIs
Publication statusPublished - 2025
Event17th Asian Conference on Computer Vision, ACCV 2024 - Hanoi, Viet Nam
Duration: 08 Dec 202412 Dec 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15478 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Asian Conference on Computer Vision, ACCV 2024
Country/TerritoryViet Nam
CityHanoi
Period08/12/2412/12/24

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