Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level

BACKGROUND: Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels...

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Autores principales: Jin, Shichao, Su, Yanjun, Song, Shilin, Xu, Kexin, Hu, Tianyu, Yang, Qiuli, Wu, Fangfang, Xu, Guangcai, Ma, Qin, Guan, Hongcan, Pang, Shuxin, Li, Yumei, Guo, Qinghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222476/
https://www.ncbi.nlm.nih.gov/pubmed/32435271
http://dx.doi.org/10.1186/s13007-020-00613-5
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author Jin, Shichao
Su, Yanjun
Song, Shilin
Xu, Kexin
Hu, Tianyu
Yang, Qiuli
Wu, Fangfang
Xu, Guangcai
Ma, Qin
Guan, Hongcan
Pang, Shuxin
Li, Yumei
Guo, Qinghua
author_facet Jin, Shichao
Su, Yanjun
Song, Shilin
Xu, Kexin
Hu, Tianyu
Yang, Qiuli
Wu, Fangfang
Xu, Guangcai
Ma, Qin
Guan, Hongcan
Pang, Shuxin
Li, Yumei
Guo, Qinghua
author_sort Jin, Shichao
collection PubMed
description BACKGROUND: Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. RESULTS: In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R(2) was greater than 0.80), and biomass estimation at leaf group level was the most precise (R(2) = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. CONCLUSION: We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices.
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spelling pubmed-72224762020-05-20 Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level Jin, Shichao Su, Yanjun Song, Shilin Xu, Kexin Hu, Tianyu Yang, Qiuli Wu, Fangfang Xu, Guangcai Ma, Qin Guan, Hongcan Pang, Shuxin Li, Yumei Guo, Qinghua Plant Methods Research BACKGROUND: Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. RESULTS: In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R(2) was greater than 0.80), and biomass estimation at leaf group level was the most precise (R(2) = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. CONCLUSION: We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices. BioMed Central 2020-05-13 /pmc/articles/PMC7222476/ /pubmed/32435271 http://dx.doi.org/10.1186/s13007-020-00613-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Jin, Shichao
Su, Yanjun
Song, Shilin
Xu, Kexin
Hu, Tianyu
Yang, Qiuli
Wu, Fangfang
Xu, Guangcai
Ma, Qin
Guan, Hongcan
Pang, Shuxin
Li, Yumei
Guo, Qinghua
Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level
title Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level
title_full Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level
title_fullStr Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level
title_full_unstemmed Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level
title_short Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level
title_sort non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7222476/
https://www.ncbi.nlm.nih.gov/pubmed/32435271
http://dx.doi.org/10.1186/s13007-020-00613-5
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