Hyperspectral discrimination of foliar biotic damages in rice using principal component analysis and probabilistic neural network-Reference-Cited by-同舟云学术

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Hyperspectral discrimination of foliar biotic damages in rice using principal component analysis and probabilistic neural network

Published:2018-04-25 Issue:6 Volume:19 Page:973-991
ISSN:1385-2256
Container-title:Precision Agriculture
language:en
Short-container-title:Precision Agric

Author:

Liu Zhan-Yu,Qi Jia-Guo,Wang Nan-Nan,Zhu Zeng-Rong,Luo Ju,Liu Li-Juan,Tang Jian,Cheng Jia-An

Funder

the National Natural Science Foundation of China

the National Basic Research Program (973) of China

the Agro-Industry R&D Special Fund of China

the China Scholarship Council Foundation

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences

Link

http://link.springer.com/article/10.1007/s11119-018-9567-4/fulltext.html

Reference53 articles.

1. Abalos, P., Daffner, J., & Pinochet, L. (2000). Evaluation of three Brucella soluble antigens used in an indirect Elisa to discriminate S19 vaccinated from naturally infected cattle. Veterinary Microbiology, 71, 161–167.

2. Albacete, A., Ghanem, M. E., Dodd, I. C., & Pérez-Alfocea, F. (2010). Principal component analysis of hormone profiling data suggests an important role for cytokinins in regulating leaf growth and senescence of salinized tomato. Plant Signal Behavior, 5, 45–48.

3. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.

4. Demetriades-Shah, T. H., Steven, M. D., & Clark, J. A. (1990). High-resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33, 55–64.

5. Fraenkel, G., & Fallil, F. (1981). The spinning (stitching) behavior of the rice leaf folder, Cnaphalocrocis Medinalis. Entomologia Experimentalis et Applicata, 29, 138–146.

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