Method for identifying early-stage disease spots of sclerotinia sclerotiorum and botrytis of rape

A technology for sclerotinia sclerotiorum and botrytis cinerea of ​​rapeseed, applied in the measurement of color/spectral characteristics, etc., can solve the problems of less research and unsatisfactory accuracy, and achieve the effect of high identification accuracy and accurate classification.

Active Publication Date: 2015-11-18
ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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  • Description
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Problems solved by technology

[0005] However, few studies have used hyperspectral imagery to identify different diseases with unusually similar symptoms on the same plant
T. Rumpf uses hyperspectral information combined with vegetation indices (VegetationIndices) and support vector machines (SupportVectorMachines) to distinguish healthy sugar beet leaves from leaves infected with three types of sugar beet diseases, such as rust, cercospora leaf spot and powdery mildew. The total ide...

Method used

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  • Method for identifying early-stage disease spots of sclerotinia sclerotiorum and botrytis of rape
  • Method for identifying early-stage disease spots of sclerotinia sclerotiorum and botrytis of rape
  • Method for identifying early-stage disease spots of sclerotinia sclerotiorum and botrytis of rape

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Experimental program
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Embodiment 1

[0070] Embodiment 1 band operation and feature band-least squares support vector machine (LS-SVM)

[0071] By observing the spectral difference between sclerotinia and gray mold (such as Figure 5 ), it was found that there is a big difference in the slope of the spectral curves of Sclerotinia sclerotiorum and Botrytis cinerea in the 675-750nm region, and this difference can be used to distinguish them. At the same time, the maximum value of the spectral difference between sclerotinia and botrytis cinerea appears at 748.23nm, and the minimum value appears at 681.95nm. Therefore, the band ratio calculation and band difference calculation for these two bands can effectively distinguish the two diseases. spot.

[0072] Select these two bands for calculation, the formula is as follows:

[0073] bandratio=R748.23 / R681.95(2)

[0074] difference=R748.23-R681.95(3)

[0075] In the formula, bandratio is the result of image band ratio calculation, difference is the result of image d...

Embodiment 2

[0081] Embodiment 2 Band operation-receiver operating characteristic curve (BandMath-ROC)

[0082] In order to facilitate subsequent image analysis, the result of the region of interest extraction is made into mask data, and the region of interest is set to 1, and other regions are set to 0.

[0083] This embodiment proposes a BandMath-ROC algorithm to identify two kinds of lesions. The band calculation is divided into a band ratio algorithm and a band difference algorithm. Only two bands are required to participate in the calculation, which can greatly reduce the calculation quantity.

[0084] Apply a mask to the band ratio and band difference calculation results for analysis, such as Image 6 shown. This embodiment tries to perform threshold segmentation on the results of band ratio and band difference calculation to distinguish two kinds of lesions. The selection of the threshold is an important step. If the selected threshold is close to the first category, then the proba...

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Abstract

The invention discloses a method for identifying early-stage disease spots of sclerotinia sclerotiorum and botrytis of rape. The method comprises steps as follows: (1), a high-spectrum image of a to-be-detected leaf of the rape is taken, and the pixel of a local disease spot of the high-spectrum image is picked; (2), a band ratio operation value and a band differential operation value of the pixel at the band of 681.95 nm-748.23 nm are calculated and obtained; (3), a feature vector composed of a spectrum value of the pixel at the band of 555.29 nm, the band ratio operation value and the band differential operation value is input into a trained least square support vector machine model, and the type of the disease spot is judged according to an output result. A band with a characteristic difference is obtained from the high-spectrum image information with the early-stage disease spots of sclerotinia sclerotiorum and botrytis through analysis, the early-stage disease spots of sclerotinia sclerotiorum and botrytis are accurately classified by combination of a characteristic wavelength, the band ratio operation value, the band differential operation value and a least square support vector machine, and the identification accuracy is higher.

Description

technical field [0001] The invention relates to the technical field of digital agriculture, in particular to a method for distinguishing early lesions of rape sclerotinia and botrytis cinerea. Background technique [0002] Rapeseed is an important oil crop in my country. It is often threatened by various diseases during the planting process, resulting in reduced yield. The main diseases of rapeseed are sclerotinia and gray mold. Sclerotinia is caused by S. sclerotiorum infection, and gray mold is caused by Botrytis cinerea infection. When the two diseases infect rapeseed leaves in the early stage, water-soaked lesions appear on the rapeseed leaves, and it is difficult to distinguish the two diseases, which is easy to cause misjudgment and miss the best control time. If these two diseases are accurately identified in a fast and non-destructive way, farmers can be guided to apply pesticides for specific diseases, improving efficiency and protecting the environment to the gre...

Claims

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Application Information

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IPC IPC(8): G01N21/25
Inventor 赵芸徐兴
Owner ZHEJIANG UNIVERSITY OF SCIENCE AND TECHNOLOGY
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