Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass
Abstract Legume content (LC) in grass-legume mixtures is important for assessing forage quality and optimizing fertilizer application in meadow fields. This study focuses on differences in LC measurements obtained from unmanned aerial vehicle (UAV) images and ground surveys based on dry matter asses...
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| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-82055-w |
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| author | Kensuke Kawamura Tsuneki Tanaka Taisuke Yasuda Shoji Okoshi Masaaki Hanada Kazuya Doi Toshiya Saigusa Takanori Yagi Kenji Sudo Kenji Okumura Jihyun Lim |
| author_facet | Kensuke Kawamura Tsuneki Tanaka Taisuke Yasuda Shoji Okoshi Masaaki Hanada Kazuya Doi Toshiya Saigusa Takanori Yagi Kenji Sudo Kenji Okumura Jihyun Lim |
| author_sort | Kensuke Kawamura |
| collection | DOAJ |
| description | Abstract Legume content (LC) in grass-legume mixtures is important for assessing forage quality and optimizing fertilizer application in meadow fields. This study focuses on differences in LC measurements obtained from unmanned aerial vehicle (UAV) images and ground surveys based on dry matter assessments in seven meadow fields in Hokkaido, Japan. We propose a UAV-based LC (LCUAV) estimation and mapping method using a land cover map from a simple linear iterative clustering (SLIC) algorithm and a random forest (RF) classifier. The SLIC-RF classification achieved a high accuracy level for four different ground cover types (grasses, legumes, weeds, and background) in seven distinct meadows with an overall accuracy of 91.4% and an F score of 91.5%. By applying SLIC-RF to eliminate plots with low classification accuracy, we demonstrate the necessity of achieving a minimum classification accuracy of 0.82 for precise LC estimation. A non-linear relationship was revealed between the LCUAV and LCBM influenced by surface sward height (SSH, height of plant canopy). The results indicate a higher accuracy of the LCBM estimation when SSH levels were lower, particularly when recommending SSH levels below 40 cm for optimal LCBM estimation. This highlights the effectiveness of UAV-based remote sensing for assessing early growth or grazing in pastures with low SSH. |
| format | Article |
| id | doaj-art-920be330c7af4e8795db5cbf8bb80d56 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-920be330c7af4e8795db5cbf8bb80d562025-08-20T01:47:58ZengNature PortfolioScientific Reports2045-23222024-12-0114111310.1038/s41598-024-82055-wLegume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomassKensuke Kawamura0Tsuneki Tanaka1Taisuke Yasuda2Shoji Okoshi3Masaaki Hanada4Kazuya Doi5Toshiya Saigusa6Takanori Yagi7Kenji Sudo8Kenji Okumura9Jihyun Lim10Obihiro University of Agriculture and Veterinary MedicineDairy Research Center, Hokkaido Research Organization (HRO)Mount Fuji Research Institute, Yamanashi Prefectural GovernmentObihiro University of Agriculture and Veterinary MedicineObihiro University of Agriculture and Veterinary MedicineRakuno Gakuen UniversityRakuno Gakuen UniversityHokkaido Agricultural Research Center, National Agriculture and Food Research Organization (NARO)Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization (NARO)Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization (NARO)Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization (NARO)Abstract Legume content (LC) in grass-legume mixtures is important for assessing forage quality and optimizing fertilizer application in meadow fields. This study focuses on differences in LC measurements obtained from unmanned aerial vehicle (UAV) images and ground surveys based on dry matter assessments in seven meadow fields in Hokkaido, Japan. We propose a UAV-based LC (LCUAV) estimation and mapping method using a land cover map from a simple linear iterative clustering (SLIC) algorithm and a random forest (RF) classifier. The SLIC-RF classification achieved a high accuracy level for four different ground cover types (grasses, legumes, weeds, and background) in seven distinct meadows with an overall accuracy of 91.4% and an F score of 91.5%. By applying SLIC-RF to eliminate plots with low classification accuracy, we demonstrate the necessity of achieving a minimum classification accuracy of 0.82 for precise LC estimation. A non-linear relationship was revealed between the LCUAV and LCBM influenced by surface sward height (SSH, height of plant canopy). The results indicate a higher accuracy of the LCBM estimation when SSH levels were lower, particularly when recommending SSH levels below 40 cm for optimal LCBM estimation. This highlights the effectiveness of UAV-based remote sensing for assessing early growth or grazing in pastures with low SSH.https://doi.org/10.1038/s41598-024-82055-w |
| spellingShingle | Kensuke Kawamura Tsuneki Tanaka Taisuke Yasuda Shoji Okoshi Masaaki Hanada Kazuya Doi Toshiya Saigusa Takanori Yagi Kenji Sudo Kenji Okumura Jihyun Lim Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass Scientific Reports |
| title | Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass |
| title_full | Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass |
| title_fullStr | Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass |
| title_full_unstemmed | Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass |
| title_short | Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass |
| title_sort | legume content estimation from uav image in grass legume meadows comparison methods based on the uav coverage vs field biomass |
| url | https://doi.org/10.1038/s41598-024-82055-w |
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