Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features
A non-destructive, convenient, and low-cost yield estimation at the field scale is vital for precision farming. Significant progress has been made in using UAV-based canopy features to predict crop yield during the mid-growth stages. However, there has been limited effort to explore yield estimation...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223003187 |
| _version_ | 1797633305120604160 |
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| author | Jinbang Peng Dongliang Wang Wanxue Zhu Ting Yang Zhen Liu Ehsan Eyshi Rezaei Jing Li Zhigang Sun Xiaoping Xin |
| author_facet | Jinbang Peng Dongliang Wang Wanxue Zhu Ting Yang Zhen Liu Ehsan Eyshi Rezaei Jing Li Zhigang Sun Xiaoping Xin |
| author_sort | Jinbang Peng |
| collection | DOAJ |
| description | A non-destructive, convenient, and low-cost yield estimation at the field scale is vital for precision farming. Significant progress has been made in using UAV-based canopy features to predict crop yield during the mid-growth stages. However, there has been limited effort to explore yield estimation specifically after crop maturity. Researching the effectiveness of artificial intelligence for estimating wheat yield utilizing phenotypic features extracted from UAV images, this study applied a deep learning algorithm (Mask R-CNN) to extract three wheat ear phenotypic features at ripening stage, including ear count, ear size, and ear anomaly index. Subsequently, machine learning algorithms (i.e., multiple linear regression, support vector regression, and random forest regression) driven by ear features were intercompared to obtain the optimal grain yield estimation. Based on the findings, (1) field observed ear count which was linearly associated with grain yield (R2 = 0.93), can be largely detected by UAV images (81 %); (2) Mask R-CNN demonstrated satisfactory performance in ear segmentation, achieving an F1 score of 0.87; (3) random forest regression resulted in the most accurate yield estimation (R2 = 0.86 and rRMSE = 17.53 %), when all three ear phenotypic features were combined. Overall, this study demonstrates that utilizing ear phenotypic features is an alternative approach for estimating wheat grain yield at ripening stage, showing potential as a viable substitute to tedious field sampling methods. |
| first_indexed | 2024-03-11T11:52:13Z |
| format | Article |
| id | doaj.art-bb2d3e6050cc43f3bdfa30ab72238327 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-11T11:52:13Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-bb2d3e6050cc43f3bdfa30ab722383272023-11-09T04:11:31ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-11-01124103494Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic featuresJinbang Peng0Dongliang Wang1Wanxue Zhu2Ting Yang3Zhen Liu4Ehsan Eyshi Rezaei5Jing Li6Zhigang Sun7Xiaoping Xin8Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100190, China; State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaDepartment of Crop Sciences, University of Göttingen, Von-Siebold-Str. 8, 37075 Göttingen, GermanyInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaLeibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, GermanyInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100190, China; Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China; Corresponding author at: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China (Z. Sun).State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Corresponding author at: State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China (X. Xin).A non-destructive, convenient, and low-cost yield estimation at the field scale is vital for precision farming. Significant progress has been made in using UAV-based canopy features to predict crop yield during the mid-growth stages. However, there has been limited effort to explore yield estimation specifically after crop maturity. Researching the effectiveness of artificial intelligence for estimating wheat yield utilizing phenotypic features extracted from UAV images, this study applied a deep learning algorithm (Mask R-CNN) to extract three wheat ear phenotypic features at ripening stage, including ear count, ear size, and ear anomaly index. Subsequently, machine learning algorithms (i.e., multiple linear regression, support vector regression, and random forest regression) driven by ear features were intercompared to obtain the optimal grain yield estimation. Based on the findings, (1) field observed ear count which was linearly associated with grain yield (R2 = 0.93), can be largely detected by UAV images (81 %); (2) Mask R-CNN demonstrated satisfactory performance in ear segmentation, achieving an F1 score of 0.87; (3) random forest regression resulted in the most accurate yield estimation (R2 = 0.86 and rRMSE = 17.53 %), when all three ear phenotypic features were combined. Overall, this study demonstrates that utilizing ear phenotypic features is an alternative approach for estimating wheat grain yield at ripening stage, showing potential as a viable substitute to tedious field sampling methods.http://www.sciencedirect.com/science/article/pii/S1569843223003187YieldPhenotypic featuresRemote sensingDeep/machine learningUnmanned Aerial Vehicle (UAV) |
| spellingShingle | Jinbang Peng Dongliang Wang Wanxue Zhu Ting Yang Zhen Liu Ehsan Eyshi Rezaei Jing Li Zhigang Sun Xiaoping Xin Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features International Journal of Applied Earth Observations and Geoinformation Yield Phenotypic features Remote sensing Deep/machine learning Unmanned Aerial Vehicle (UAV) |
| title | Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features |
| title_full | Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features |
| title_fullStr | Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features |
| title_full_unstemmed | Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features |
| title_short | Combination of UAV and deep learning to estimate wheat yield at ripening stage: The potential of phenotypic features |
| title_sort | combination of uav and deep learning to estimate wheat yield at ripening stage the potential of phenotypic features |
| topic | Yield Phenotypic features Remote sensing Deep/machine learning Unmanned Aerial Vehicle (UAV) |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223003187 |
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