The Detection of Soybean Bacterial Blight Based on Polarization Spectral Imaging Techniques

Soybean bacterial blight, caused by Pseudomonas savastanoi pv. glycine, which is one of the common diseases of soybeans, has a strong harm and a great impact on the yield of soybeans. If the disease is not diagnosed in time and no solution comes up, it will lead to the serious loss of yield after the disease becomes serious. Therefore, this paper proposes the detection of the soybean bacterial blight with the polarization spectroscopic imaging method, derived from the detection principle and mathematical model of polarization bidirectional reflection distribution function on the basis of the Stokes vector analysis method. By synthesizing the spectral lines of the four polarization states and the non-polarization states, it was found that the physical parameters of I (135°, 90°) polarization state were the most suitable for identifying soybean bacterial blight disease, and other polarization states could also supplement the characteristic information. The results show that the polarization spectral image can effectively identify the polarization characteristics of healthy soybean leaves and early bacterial blight in the field, and can distinguish the healthy leaves and the diseased leaves by obtaining the relative polarization reflectance of different areas in soybean leaves. Finally, the soybean disease species can be accurately diagnosed. This paper provides an optical method for the detection of crop diseases and insect pests, which makes up for the deficiency of the traditional detection technology and can provide a scientific basis for the safe non-destructive detection of crop diseases and pests.

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Publication Details

Journal
Agronomy
Published
2024-12-28
DOI
https://doi.org/10.3390/agronomy15010050
Citations
4
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
0.94

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article

The Detection of Soybean Bacterial Blight Based on Polarization Spectral Imaging Techniques

Jia Yi, Yong Tan, Jiang Huilin
4 citations
Agronomy
Spectroscopy and Chemometric Analyses
0.94
article

The Detection of Soybean Bacterial Blight Based on Polarization Spectral Imaging Techniques

Jia Yi, Yong Tan, Jiang Huilin
article en
4 citations

Abstract

Soybean bacterial blight, caused by Pseudomonas savastanoi pv. glycine, which is one of the common diseases of soybeans, has a strong harm and a great impact on the yield of soybeans. If the disease is not diagnosed in time and no solution comes up, it will lead to the serious loss of yield after the disease becomes serious. Therefore, this paper proposes the detection of the soybean bacterial blight with the polarization spectroscopic imaging method, derived from the detection principle and mathematical model of polarization bidirectional reflection distribution function on the basis of the Stokes vector analysis method. By synthesizing the spectral lines of the four polarization states and the non-polarization states, it was found that the physical parameters of I (135°, 90°) polarization state were the most suitable for identifying soybean bacterial blight disease, and other polarization states could also supplement the characteristic information. The results show that the polarization spectral image can effectively identify the polarization characteristics of healthy soybean leaves and early bacterial blight in the field, and can distinguish the healthy leaves and the diseased leaves by obtaining the relative polarization reflectance of different areas in soybean leaves. Finally, the soybean disease species can be accurately diagnosed. This paper provides an optical method for the detection of crop diseases and insect pests, which makes up for the deficiency of the traditional detection technology and can provide a scientific basis for the safe non-destructive detection of crop diseases and pests.

AgronomyVol. 15(1)
Changchun University of Science and Technology (CN)
Natural Science Foundation of Jilin Province, People's Government of Jilin Province
Openalex Percentile: Top 28%
Spectroscopy and Chemometric Analyses
0.94
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