Mining Method of Crop Spectral and Image Correlation ModelBased on Spatio-Temporal Information

Authors

Ronghua Gao, 1. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; ;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; ;3. Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097, China; ;4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;
Qifeng Li, 1. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; ;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; ;3. Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097, China; ;4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;
Jingqiu Gu, 1. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; ;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; ;3. Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097, China; ;4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;
Sun Xiang, 1. Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; ;2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; ;3. Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097, China; ;4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China;

Abstract

Abstract: When crop disease occurs, it is often displayed in the leaf, and the appearance and internal structure of the crop are changed, and the growth environment also has a certain influence on the disease. The growth environment, leaf RGB images and spectral images are fused to study the sparse feature recognition method of crop diseases based on information combination of multi spectral images. In this paper, a spatial-temporal information mining method for crop spectral and image correlation models is studied. The correlation between spectral reflectance characteristics of crop diseases and crop development, health status and growth conditions are analyzed from time dimension, space dimension and spectral dimension, and disease characteristics is established. The experimental results show that the fusion method of image processing and spectral imaging technology can achieve fast, accurate and nondestructive diagnosis in the early stage of disease.

Recommended Citation

Gao, Ronghua; Li, Qifeng; Gu, Jingqiu; and Xiang, Sun (2019) "Mining Method of Crop Spectral and Image Correlation ModelBased on Spatio-Temporal Information," Journal of System Simulation: Vol. 30: Iss. 12, Article 3.
DOI: 10.16182/j.issn1004731x.joss.201812003
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss12/3

First Page

4513

Revised Date

2018-07-03

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201812003

Last Page

4519

CLC

TP391

Recommended Citation

Gao Ronghua, Li Qifeng, Gu Jingqiu, Sun Xiang. Mining Method of Crop Spectral and Image Correlation ModelBased on Spatio-Temporal Information[J]. Journal of System Simulation, 2018, 30(12): 4513-4519.

DOI

10.16182/j.issn1004731x.joss.201812003