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Carlier A, Dandrifosse S, Dumont B, Mercatoris B. Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing. Front Plant Sci 2023; 14:1204791. [PMID: 38053768 PMCID: PMC10694231 DOI: 10.3389/fpls.2023.1204791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/30/2023] [Indexed: 12/07/2023]
Abstract
Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential.
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Affiliation(s)
- Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Sébastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Benoit Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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David E, Serouart M, Smith D, Madec S, Velumani K, Liu S, Wang X, Pinto F, Shafiee S, Tahir ISA, Tsujimoto H, Nasuda S, Zheng B, Kirchgessner N, Aasen H, Hund A, Sadhegi-Tehran P, Nagasawa K, Ishikawa G, Dandrifosse S, Carlier A, Dumont B, Mercatoris B, Evers B, Kuroki K, Wang H, Ishii M, Badhon MA, Pozniak C, LeBauer DS, Lillemo M, Poland J, Chapman S, de Solan B, Baret F, Stavness I, Guo W. Global Wheat Head Detection 2021: An Improved Dataset for Benchmarking Wheat Head Detection Methods. Plant Phenomics 2021; 2021:9846158. [PMID: 34778804 PMCID: PMC8548052 DOI: 10.34133/2021/9846158] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/11/2021] [Indexed: 05/03/2023]
Abstract
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.
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Affiliation(s)
- Etienne David
- Arvalis, Institut du Végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
| | - Mario Serouart
- Arvalis, Institut du Végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
| | - Daniel Smith
- School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
| | - Simon Madec
- Arvalis, Institut du Végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
- School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
| | - Kaaviya Velumani
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
- Hiphen SAS, 120 Rue Jean Dausset, Agroparc, Bâtiment Technicité, 84140 Avignon, France
| | - Shouyang Liu
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
| | - Xu Wang
- Wheat Genetics Resource Center, Dep. of Plant Pathology, Kansas State Univ., 4024 Throckmorton Plant Sciences Center, Manhattan, Kansas, USA
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT), Mexico, D.F., Mexico
| | - Shahameh Shafiee
- Faculty of Biosciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
| | - Izzat S. A. Tahir
- Agricultural Research Corporation, Wheat Research Program, P.O. Box 126, Wad Medani, Sudan
| | - Hisashi Tsujimoto
- Arid Land Research Center, Tottori University, Tottori 680-0001, Japan
| | - Shuhei Nasuda
- Laboratories of Plant Genetics and Plant Breeding, Graduate School of Agriculture, Kyoto University, Japan
| | - Bangyou Zheng
- CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, 4067 QLD, Australia
| | - Norbert Kirchgessner
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Helge Aasen
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zurich, Switzerland
| | | | - Koichi Nagasawa
- Institute of Crop Science, National Agriculture and Food Research Organization, Japan
| | - Goro Ishikawa
- Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization, Japan
| | - Sébastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Benoit Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Byron Evers
- Wheat Genetics Resource Center, Dep. of Plant Pathology, Kansas State Univ., 4024 Throckmorton Plant Sciences Center, Manhattan, Kansas, USA
| | - Ken Kuroki
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
| | - Haozhou Wang
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
| | - Masanori Ishii
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
| | | | - Curtis Pozniak
- Department of Plant Sciences, University of Saskatchewan, Canada
| | - David Shaner LeBauer
- College of Agriculture and Life Sciences, University of Arizona, Tucson, Arizona, USA
| | - Morten Lillemo
- Faculty of Biosciences, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
| | - Jesse Poland
- Wheat Genetics Resource Center, Dep. of Plant Pathology, Kansas State Univ., 4024 Throckmorton Plant Sciences Center, Manhattan, Kansas, USA
| | - Scott Chapman
- School of Food and Agricultural Sciences, The University of Queensland, Gatton, 4343 QLD, Australia
- CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, 4067 QLD, Australia
| | - Benoit de Solan
- Arvalis, Institut du Végétal, 3 Rue Joseph et Marie Hackin, 75116 Paris, France
| | - Frédéric Baret
- UMR1114 EMMAH, INRAE, Centre PACA, Bâtiment Climat, Domaine Saint-Paul, 228 Route de l'Aérodrome, CS 40509, 84914 Avignon Cedex, France
| | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Canada
| | - Wei Guo
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
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