دورية أكاديمية
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Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.

التفاصيل البيبلوغرافية
العنوان: Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.
المؤلفون: Gao W; Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, China., Zeng W; College of Agronomy, Xinjiang Agricultural University, Urumqi, China., Li S; Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, China., Zhang L; Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi, China., Wang W; Anyang Institute of Technology, AnYang, China., Song J; Cotton Research Institute, Chinese Academy of Agricultural Sciences, AnYang, China., Wu H; Anyang Institute of Technology, AnYang, China.
المصدر: PloS one [PLoS One] 2024 Jun 21; Vol. 19 (6), pp. e0300056. Date of Electronic Publication: 2024 Jun 21 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Beta vulgaris* , Remote Sensing Technology*/methods , Remote Sensing Technology*/instrumentation, Unmanned Aerial Devices ; Support Vector Machine ; Soil/chemistry ; Machine Learning ; Crops, Agricultural/growth & development ; Agriculture/methods ; Droughts
مستخلص: Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Gao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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المشرفين على المادة: 0 (Soil)
تواريخ الأحداث: Date Created: 20240621 Date Completed: 20240621 Latest Revision: 20240623
رمز التحديث: 20240623
مُعرف محوري في PubMed: PMC11192409
DOI: 10.1371/journal.pone.0300056
PMID: 38905187
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0300056