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Feature Selection and Analysis of Powdery Mildew of Winter Wheat based on Multi-Temporal Satellite Imagery

Authors:

Jingcheng Zhang,

Lin YuanAuthors Info & Claims

ICIMCS'16: Proceedings of the International Conference on Internet Multimedia Computing and Service

Pages 251 - 254

https://doi.org/10.1145/3007669.3007701

Published: 19 August 2016 Publication History

ICIMCS'16: Proceedings of the International Conference on Internet Multimedia Computing and Service

Feature Selection and Analysis of Powdery Mildew of Winter Wheat based on Multi-Temporal Satellite Imagery

Pages 251 - 254

Abstract
References

Abstract

The powdery mildew of wheat cause visible and obvious image changes and lead to different spectral reflections, which can be detected by the environment and disaster reduction small satellites. The information of satellites can be transformed into many kinds of indices, some of which could be sensitive and effective in studying and detecting the powdery mildew in wheat, however, it is still unknown whether all of these features are useful in evaluating and classifying the severity of the infected plants. In this paper, we try to study and analyze the relationship and functions of the features to find a more effective combination of features in estimating and evaluating the plants and fields.

We studied the feature selection and analysis in the process to discuss whether they are all necessary in the classification.Based on the multi-temporal moderate resolution images, it is found that SR_T4, NDVI_T4, MSR_T4 carry the same information and that MSR_T4 is more suitable in this study. The two-stage features are reconfirmed to be powerful in the study and enrich the results of the classification. But NDVI_T42 can be eliminated and hardly has an effect on the results. However the universality of the conclusion still needs further study due to the limitation of the data dimensionality and the analysis measure selection

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Cited By

Kumar DKukreja V(2022)Deep learning in wheat diseases classification: A systematic reviewMultimedia Tools and Applications10.1007/s11042-022-12160-381:7(10143-10187)Online publication date: 14-Feb-2022
https://doi.org/10.1007/s11042-022-12160-3
Kumar DKukreja V(2021)N-CNN Based Transfer Learning Method for Classification of Powdery Mildew Wheat Disease2021 International Conference on Emerging Smart Computing and Informatics (ESCI)10.1109/ESCI50559.2021.9396972(707-710)Online publication date: 5-Mar-2021
https://doi.org/10.1109/ESCI50559.2021.9396972

Feature Selection and Analysis of Powdery Mildew of Winter Wheat based on Multi-Temporal Satellite Imagery
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies

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cover image ACM Other conferences

ICIMCS'16: Proceedings of the International Conference on Internet Multimedia Computing and Service

August 2016

360 pages

ISBN:9781450348508

DOI:10.1145/3007669

Copyright © 2016 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Xidian University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 August 2016

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ICIMCS'16

ICIMCS'16: International Conference on Internet Multimedia Computing and Service

August 19 - 21, 2016

Xi'an, China

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ICIMCS'16 Paper Acceptance Rate 77 of 118 submissions, 65%;

Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

Kumar DKukreja V(2022)Deep learning in wheat diseases classification: A systematic reviewMultimedia Tools and Applications10.1007/s11042-022-12160-381:7(10143-10187)Online publication date: 14-Feb-2022
https://doi.org/10.1007/s11042-022-12160-3
Kumar DKukreja V(2021)N-CNN Based Transfer Learning Method for Classification of Powdery Mildew Wheat Disease2021 International Conference on Emerging Smart Computing and Informatics (ESCI)10.1109/ESCI50559.2021.9396972(707-710)Online publication date: 5-Mar-2021
https://doi.org/10.1109/ESCI50559.2021.9396972

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