Academic Journal
Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass
| Title: | Legume content estimation from UAV image in grass-legume meadows: comparison methods based on the UAV coverage vs. field biomass |
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| Authors: | Kensuke Kawamura, Tsuneki Tanaka, Taisuke Yasuda, Shoji Okoshi, Masaaki Hanada, Kazuya Doi, Toshiya Saigusa, Takanori Yagi, Kenji Sudo, Kenji Okumura, Jihyun Lim |
| Source: | Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024) |
| Publisher Information: | Nature Portfolio, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Medicine, Science |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-82055-w |
| Access URL: | https://doaj.org/article/920be330c7af4e8795db5cbf8bb80d56 |
| Accession Number: | edsdoj.920be330c7af4e8795db5cbf8bb80d56 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20452322 |
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| DOI: | 10.1038/s41598-024-82055-w |
| Published in: | Scientific Reports |
| Language: | English |