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Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.
Li, Tiansheng; Wang, Haijiang; Cui, Jing; Wang, Weiju; Li, Wenruiyu; Jiang, Menghao; Shi, Xiaoyan; Song, Jianghui; Wang, Jingang; Lv, Xin; Zhang, Lifu.
Afiliación
  • Li T; College of Agriculture, Shihezi University, Shihezi, China.
  • Wang H; College of Agriculture, Shihezi University, Shihezi, China.
  • Cui J; College of Agriculture, Shihezi University, Shihezi, China.
  • Wang W; College of Agriculture, Shihezi University, Shihezi, China.
  • Li W; College of Agriculture, Shihezi University, Shihezi, China.
  • Jiang M; College of Agriculture, Shihezi University, Shihezi, China.
  • Shi X; College of Agriculture, Shihezi University, Shihezi, China.
  • Song J; College of Agriculture, Shihezi University, Shihezi, China.
  • Wang J; College of Agriculture, Shihezi University, Shihezi, China.
  • Lv X; College of Agriculture, Shihezi University, Shihezi, China.
  • Zhang L; College of Agriculture, Shihezi University, Shihezi, China.
Front Plant Sci ; 15: 1333089, 2024.
Article en En | MEDLINE | ID: mdl-38601301
ABSTRACT
Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones. The acquired images included cotton seedlings, bare soil, mulching films, and PE drip tapes. After constructing 17 visible VIs and 14 multispectral VIs, three strategies were used to separate cotton seedlings from the images (1) Otsu's thresholding was performed on each vegetation index (VI); (2) Key VIs were extracted based on results of (1), and the Otsu-intersection method and three machine learning methods were used to classify cotton seedlings, bare soil, mulching films, and PE drip tapes in the images; (3) Machine learning models were constructed using all VIs and validated. Finally, the models constructed based on two modeling strategies [Otsu-intersection (OI) and machine learning (Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)] showed a higher accuracy. Therefore, these models were selected to estimate cotton seedling emergence rate, and the estimates were compared with the manually measured emergence rate. The results showed that multispectral VIs, especially NDVI, RVI, SAVI, EVI2, OSAVI, and MCARI, had higher crop seedling extraction accuracy than visible VIs. After fusing all VIs or key VIs extracted based on Otsu's thresholding, the binary image purity was greatly improved. Among the fusion methods, the Key VIs-OI and All VIs-KNN methods yielded less noises and small errors, with a RMSE (root mean squared error) as low as 2.69% and a MAE (mean absolute error) as low as 2.15%. Therefore, fusing multiple VIs can increase crop image segmentation accuracy. This study provides a new method for rapidly monitoring crop seedling emergence rate in the field, which is of great significance for the development of modern agriculture.
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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2024 Tipo del documento: Article País de afiliación: China