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Estimation of Botanical Composition in Mixed Clover-Grass Fields Using Machine Learning-Based Image Analysis

Sun, Sashuang and Liang, Ning and Zuo, Zhiyu and Parsons, David and Morel, Julien and Shi, Jiang and Wang, Zhao and Luo, Letan and Zhao, Lin and Fang, Hui and He, Yong and Zhou, Zhenjiang (2021). Estimation of Botanical Composition in Mixed Clover-Grass Fields Using Machine Learning-Based Image Analysis. Frontiers in Plant Science. 12 , 622429
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Abstract

This study aims to provide an effective image analysis method for clover detection and botanical composition (BC) estimation in clover-grass mixture fields. Three transfer learning methods, namely, fine-tuned DeepLab V3+, SegNet, and fully convolutional network-8s (FCN-8s), were utilized to detect clover fractions (on an area basis). The detected clover fraction (CFdetected), together with auxiliary variables, viz., measured clover height (H-clover) and grass height (H-grass), were used to build multiple linear regression (MLR) and back propagation neural network (BPNN) models for BC estimation. A total of 347 clover-grass images were used to build the estimation model on clover fraction and BC. Of the 347 samples, 226 images were augmented to 904 images for training, 25 were selected for validation, and the remaining 96 samples were used as an independent dataset for testing. Testing results showed that the intersection-over-union (IoU) values based on the DeepLab V3+, SegNet, and FCN-8s were 0.73, 0.57, and 0.60, respectively. The root mean square error (RMSE) values for the three transfer learning methods were 8.5, 10.6, and 10.0%. Subsequently, models based on BPNN and MLR were built to estimate BC, by using either CFdetected only or CFdetected, grass height, and clover height all together. Results showed that BPNN was generally superior to MLR in terms of estimating BC. The BPNN model only using CFdetected had a RMSE of 8.7%. In contrast, the BPNN model using all three variables (CFdetected, H-clover, and H-grass) as inputs had an RMSE of 6.6%, implying that DeepLab V3+ together with BPNN can provide good estimation of BC and can offer a promising method for improving forage management.

Authors/Creators:Sun, Sashuang and Liang, Ning and Zuo, Zhiyu and Parsons, David and Morel, Julien and Shi, Jiang and Wang, Zhao and Luo, Letan and Zhao, Lin and Fang, Hui and He, Yong and Zhou, Zhenjiang
Title:Estimation of Botanical Composition in Mixed Clover-Grass Fields Using Machine Learning-Based Image Analysis
Series Name/Journal:Frontiers in Plant Science
Year of publishing :2021
Volume:12
Article number:622429
Number of Pages:12
Publisher:FRONTIERS MEDIA SA
ISSN:1664-462X
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 106 Biological Sciences (Medical to be 3 and Agricultural to be 4) > Botany
Keywords:crop species classification, forage crop, transfer learning, DeepLab V3+, back propagation neural network
URN:NBN:urn:nbn:se:slu:epsilon-p-111124
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-111124
Additional ID:
Type of IDID
DOI10.3389/fpls.2021.622429
Web of Science (WoS)000621376700001
ID Code:22811
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Agricultural Research for Northern Sweden
(VH) > Dept. of Agricultural Research for Northern Sweden
Deposited By: SLUpub Connector
Deposited On:22 Mar 2021 07:43
Metadata Last Modified:22 Mar 2021 07:51

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