CN114740004A - Method for detecting root rot and positioning based on collection of hyperspectral corn by unmanned aerial vehicle - Google Patents
Method for detecting root rot and positioning based on collection of hyperspectral corn by unmanned aerial vehicle Download PDFInfo
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Abstract
A method for detecting root rot and positioning based on corn hyperspectrum acquired by an unmanned aerial vehicle belongs to the technical field of agricultural disease early prediction, and can timely and efficiently monitor corn root rot possibly occurring in the whole corn production management process. According to the method, a root rot identification model is established based on the difference of chlorophyll changes of leaves of corn plants under the conditions of root rot of different periods and different degrees, correct diagnosis can be made about 14 days before the corn diseased plants have no apparent symptoms through the root rot identification model, diseased regions can be positioned in a farmland at the same time, a decision is provided for early prevention and timely treatment of the corn root rot diseases, and the corn yield is improved. The invention has the characteristics of accuracy and rapidness, can improve the agricultural production level, reduce the agricultural production management cost and improve the yield, quality and benefit of agricultural products.
Description
Technical Field
The invention belongs to the technical field of early prediction of agricultural diseases, and particularly relates to a method for detecting root rot and positioning by collecting corn hyperspectrum based on an unmanned aerial vehicle.
Background
Corn is one of the main grain crops in China, and the planting area and the total yield of the corn are second to those of wheat and rice. The application of the corn is very wide, the corn is one of important raw materials of light industry and medical industry except for eating and preparing high-quality livestock feed, so the corn is in an important position in the development of national economy, but frequent corn diseases are one of important factors for restricting the development of the corn due to the change of climate, the change of cultivation system and the change of types, and therefore, the prevention of the corn diseases becomes a key link for ensuring the sustainable yield increase of the corn at present. The corn root rot is one of serious fungal diseases in a corn seedling stage, generally infected in a corn leaf stage 2, the root system is browned in a corn leaf stage 4, and the corn leaves are gradually yellowed and withered from bottom to top when the corn leaves show symptoms in a corn leaf stage 8. Because the corn root rot has strong concealment at the initial stage of disease occurrence and late appearance of the apparent symptoms, the disease rate in parts of domestic areas reaches 80 percent, and the influence on the corn yield is large.
At present, the traditional disease and insect pest monitoring mainly adopts manual field investigation, diagnoses are carried out through the forms and symptoms shown by disease occurrence and development, the diagnosis depends on human sense judgment, the efficiency is low, the accuracy is poor, the difficulty is high, detection personnel have strong professional knowledge or experience, and the large-scale popularization is difficult. However, the detection method has high requirements on the precision of the detected sample and the operation technique of a detector, has high cost and long time consumption, generates more damages to the sample, and is easy to cause environmental pollution.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for detecting root rot and positioning by collecting corn hyperspectrum based on an unmanned aerial vehicle, which can timely and efficiently monitor the corn root rot possibly occurring in the whole process of corn production management.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a method for detecting root rot and positioning based on unmanned aerial vehicle corn hyperspectral acquisition comprises the following steps:
the method comprises the following steps: inducing the corn root rot in the 2-leaf stage, and collecting the high spectral reflectivity of the corn roots of the patient and the healthy corn plants;
step two: performing first-order differential calculation on the hyperspectral reflectivities of the diseased corn plant and the healthy corn plant in the step one, and selecting a wave band with obvious difference under different disease conditions as a characteristic wave band;
step three: calculating a hyperspectral reflectance normalized value of the healthy corn plant and the diseased corn root according to the characteristic wave band in the step two, and establishing a root rot identification model according to the hyperspectral reflectance normalized value and the characteristic wave band;
step four: collecting the hyperspectral reflectivities of corns in different leaf periods of a region to be detected by using an unmanned aerial vehicle;
step five: and predicting the maize disease condition of the area to be detected according to the maize high spectral reflectivity in the step four and the root rot identification model in the step three, drawing a diseased area bitmap, and determining the disease occurrence degree through manual reinspection.
Preferably, the induction of the corn root rot in the 2-leaf stage in the step one is realized by utilizing a mode of artificially inoculating pathogenic bacteria.
Preferably, the diseased corn roots are mild 4-leaf stage disease and moderate 6-leaf stage disease.
Preferably, in the step one, the hyperspectral reflectivities of the healthy corn plants and the corn roots of the patients are collected through a portable spectrometer.
Preferably, the wavelength of the characteristic spectrum band is 550nm to 740nm as the characteristic band.
Preferably, the root rot identification model comprises areas of mild illness, moderate illness and severe illness.
Preferably, the fourth step comprises the following specific steps: and carrying out orthoscopic image shooting on the area to be measured by utilizing the unmanned aerial vehicle carrying the hyperspectral camera, and acquiring data.
Preferably, the fourth step further comprises: the unmanned aerial vehicle is set with a flight line before taking off and placed on the ground for radiation calibration and reflection panels.
The invention has the beneficial effects that: the invention establishes a root rot identification model based on the difference of chlorophyll changes of leaves of corn plants under the conditions of root rot of different periods and different degrees, can make correct diagnosis about 14 days before the corn diseased plants have no apparent symptoms through the root rot identification model, can position diseased regions in a farmland, provides a decision for early prevention and timely treatment of the corn root rot diseases, and improves the corn yield. The method has the characteristics of accuracy and rapidness, and can improve the agricultural production level, reduce the agricultural production management cost and improve the yield, quality and benefit of agricultural products.
Drawings
FIG. 1 is a schematic flow diagram of a method for detecting root rot and positioning by collecting corn hyperspectrum based on an unmanned aerial vehicle.
FIG. 2 is a graph showing the relationship between the hyperspectral reflectivities of corn and the hyperspectral wavelengths in the corn root rot disease in the method for detecting the root rot and positioning the hyperspectral of the corn based on the unmanned aerial vehicle acquisition.
FIG. 3 is a graph showing the relationship between the hyperspectral first-order differential of the maize hyperspectral and the hyperspectral wavelength in the maize root rot, in the method for detecting the root rot and positioning by collecting the maize hyperspectral by the unmanned aerial vehicle.
FIG. 4 is a corn root rot identification model of the method for detecting and locating root rot based on unmanned aerial vehicle corn hyperspectral collection.
FIG. 5 shows the diseased positions of corns in the research area of the experimental field in the method for detecting root rot and positioning by collecting hyperspectrum of corns by the unmanned aerial vehicle.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in figure 1, the method for detecting rot and positioning based on the hyperspectral collection of the corn roots by the unmanned aerial vehicle comprises the following steps:
the method comprises the following steps: the root rot of corn in the 2-leaf stage is induced by artificial inoculation of pathogenic bacteria, and the hyperspectral reflectivities of diseased corn in the 4-leaf stage and the 6-leaf stage and healthy corn plants are collected by a portable spectrometer, as shown in fig. 2.
Step two: performing first-order differential calculation on the high spectral reflectivity of the patient corn plant and the healthy corn plant,
in the formula: p' (i) is the first order differential at the band i, p (i +1) is the reflectivity of the next sampling band, p (i-1) is the reflectivity of the previous sampling band, Δ p is the wavelength sampling interval, and as a result, as shown in fig. 3, the 550 nm-740 nm band with significant differences at mild, moderate and severe degrees is selected as the characteristic band.
Step three: calculating a normalized value of the hyperspectral reflectivity,
in the formula: z (i) is a normalized value of the hyperspectral reflectivity at the wave band i, p (i) is the hyperspectral reflectivity at the wave band i, and m (i) is an envelope curve value at the wave band i; establishing a root rot identification model according to the hyperspectral reflectance normalized value and the characteristic wave bands in the step two, drawing hyperspectral reflectance normalized value curves of 4-leaf mild disease corns, 6-leaf moderate disease corns and healthy corn plants, and determining regions of mild disease (I), moderate disease (II) and severe disease (III) in the characteristic wave bands, as shown in fig. 4.
Step four: the hyperspectral unmanned aerial vehicle is used for collecting the hyperspectral reflectivity of the corn in the area to be measured, the unmanned aerial vehicle carrying the hyperspectral camera is used for shooting the orthoscopic image of the area to be measured, data are collected, a course line and flight parameters are set before the unmanned aerial vehicle takes off, and a calibration reflection panel is placed on the ground for radiation calibration.
Step five: the disease condition of the corn in the area to be detected is predicted according to the rot identification model, the disease occurrence position is determined, the spectrum data of the area to be detected collected by the unmanned aerial vehicle is compared with the root rot identification model, whether the corn is diseased or not is judged, a diseased area bitmap is drawn, and the disease occurrence degree is determined through manual reinspection.
Example 1: selecting an experimental field of Jilin agriculture science and technology institute of Jilin province as an observation object, collecting hyperspectral data of corn in a 4-leaf stage by using an unmanned aerial vehicle, comparing the hyperspectral data with a corn root rot identification model after analysis and calculation, determining that a hyperspectral reflectance curve of the corn in 2 areas is in a mild disease area, and determining the occurrence condition of diseases through manual review. As shown in Table 1, the 2 regions were determined to be "diseased" by the artificial retest, and the diseased regions are shown in FIG. 5 in accordance with the results of the spectral detection.
TABLE 1 disease status in different areas
While there have been shown and described what are at present considered the fundamental principles and essential features of the invention and its advantages, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (8)
1. A method for detecting root rot and positioning based on unmanned aerial vehicle corn hyperspectral acquisition is characterized by comprising the following steps:
the method comprises the following steps: inducing the corn root rot in the 2-leaf stage, and collecting the high spectral reflectivity of the corn roots of the patient and the healthy corn plants;
step two: performing first-order differential calculation on the hyperspectral reflectivities of the diseased corn plant and the healthy corn plant in the step one, and selecting a wave band with obvious difference under different disease conditions as a characteristic wave band;
step three: calculating a hyperspectral reflectance normalized value of the healthy corn plant and the diseased corn root according to the characteristic wave band in the step two, and establishing a root rot identification model according to the hyperspectral reflectance normalized value and the characteristic wave band;
step four: collecting the hyperspectral reflectances of corns in different leaf periods of a region to be detected by using an unmanned aerial vehicle;
step five: and predicting the maize disease condition of the area to be detected according to the maize high spectral reflectivity in the step four and the root rot identification model in the step three, drawing a diseased area bitmap, and determining the disease occurrence degree through manual reinspection.
2. The method for detecting root rot and positioning based on unmanned aerial vehicle corn hyperspectral acquisition according to claim 1, wherein the induction of the 2-leaf corn root rot in the first step is realized by means of artificial germ inoculation.
3. The unmanned aerial vehicle-based corn hyperspectral collection method for detecting root rot and positioning according to claim 1 is characterized in that the corn roots of the patients are mild patients in 4-leaf stage and moderate patients in 6-leaf stage.
4. The method for detecting root rot and positioning based on unmanned aerial vehicle corn hyperspectral collection according to claim 1, wherein in step one, hyperspectral reflectivity of healthy corn plants and diseased corn roots is collected by a portable spectrometer.
5. The method for achieving root rot detection and location based on unmanned aerial vehicle corn hyperspectral acquisition according to claim 1, wherein the wavelength of the characteristic spectrum segment is 550nm to 740 nm.
6. The unmanned aerial vehicle-based corn hyperspectral collection method for detecting root rot and positioning according to claim 1, wherein the root rot identification model comprises areas of mild illness, moderate illness and severe illness.
7. The method for detecting root rot and positioning based on unmanned aerial vehicle corn hyperspectral acquisition according to claim 1 is characterized in that the fourth step is specifically: and carrying out orthoscopic image shooting on the area to be measured by utilizing the unmanned aerial vehicle carrying the hyperspectral camera, and acquiring data.
8. The unmanned aerial vehicle-based method for detecting root rot and positioning by collecting corn hyperspectrum, according to claim 7, wherein the fourth step further comprises: and a flight path is set before the unmanned aerial vehicle takes off, and a reflection panel for radiation calibration is placed on the ground of the area to be measured.
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| CN116087118A (en) * | 2023-04-06 | 2023-05-09 | 黑龙江省农业科学院农业遥感与信息研究所 | Device and system for identifying corn northern leaf blight by hyperspectral remote sensing |
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| CN106777845A (en) * | 2017-03-22 | 2017-05-31 | 南京农业大学 | The method that sensitive parameter builds wheat leaf blade powdery mildew early monitoring model is extracted based on subwindow rearrangement method |
| CN108375550A (en) * | 2018-01-12 | 2018-08-07 | 河南农业大学 | The construction method of winter wheat full rot disease Index Prediction Model based on spectral index and application |
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