Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis-Reference-Cited by-同舟云学术

Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis

Published:2010-07 Issue:2 Volume:72 Page:99-106
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Liu Zhan-Yu,Wu Hong-Feng,Huang Jing-Feng

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference48 articles.

1. Evaluation of three Brucella soluble antigens used in an indirect Elisa to discriminate S19 vaccinated from naturally infected cattle;Abalos;Veterinary Microbiology,2000

2. Yellowness index: an application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation;Adams;International Journal of Remote Sensing,1999

3. Mean effective optical constants of cotton leaves;Allen;Journal of the Optical Society of America,1970

4. Theory and applications of optical remote sensing;Asrar,1989

5. A review of assessing the accuracy of classification of remotely sensed data;Congalton;Remote Sensing of Environment,1991

Cited by 109 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Image-Based Plant Disease Detection Using IoT and Deep Learning;Green Industrial Applications of Artificial Intelligence and Internet of Things;2024-07-21

2. Mapping invasive noxious weed species in the alpine grassland ecosystems using very high spatial resolution UAV hyperspectral imagery and a novel deep learning model;GIScience & Remote Sensing;2024-03-13

3. Reflectance spectra for identifying stress in different parts of leaf: a case study on oil palm seedlings;International Journal of Remote Sensing;2024-01-28

4. Retrieval of chlorophyll content for vegetation communities under different inundation frequencies using UAV images and field measurements;Ecological Indicators;2024-01

5. Spectral response to early detection of stressed oil palm seedlings using near-infrared reflectance spectra at region 900-1000 nm;Infrared Physics & Technology;2023-12