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Research article - Peer-reviewed, 2021

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

Sun, Sashuang; Liang, Ning; Zuo, Zhiyu; Parsons, David; Morel, Julien; Shi, Jiang; Wang, Zhao; Luo, Letan; Zhao, Lin; Fang, Hui; He, Yong; Zhou, Zhenjiang

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.

Keywords

crop species classification; forage crop; transfer learning; DeepLab V3+; back propagation neural network

Published in

Frontiers in Plant Science
2021, volume: 12, article number: 622429
Publisher: FRONTIERS MEDIA SA

Authors' information

Sun, Sashuang
Zhejiang University
Liang, Ning
Zhejiang University
Zuo, Zhiyu
Jiangsu University
Swedish University of Agricultural Sciences, Department of Agricultural Research for Northern Sweden
Swedish University of Agricultural Sciences, Department of Agricultural Research for Northern Sweden
Shi, Jiang
Zhejiang Academy of Agricultural Sciences (ZAAS)
Wang, Zhao
Zhejiang University
Luo, Letan
Zhejiang Academy of Agricultural Sciences (ZAAS)
Zhao, Lin
Zhejiang Academy of Agricultural Sciences (ZAAS)
Fang, Hui
Zhejiang University
He, Yong
Zhejiang University
Zhou, Zhenjiang
Zhejiang University
Zhou, Zhenjiang
Swedish University of Agricultural Sciences, Department of Agricultural Research for Northern Sweden

UKÄ Subject classification

Botany

Publication Identifiers

DOI: https://doi.org/10.3389/fpls.2021.622429

URI (permanent link to this page)

https://res.slu.se/id/publ/111124