A method for predicting cotton defoliation rate based on cotton canopy

Abstract

The invention discloses a method for predicting cotton defoliation rate based on cotton canopy, and belongs to the technical field of crop growth detection. A method for predicting cotton defoliation rate based on cotton canopy, comprising the following steps: before defoliation treatment, testing the cotton canopy in the test area to obtain cotton canopy data before defoliation treatment; after defoliation treatment, testing the cotton canopy in the test area to obtain cotton canopy data after defoliation treatment; calculating the cotton canopy data change rate of the test area according to the cotton canopy data of the test area before defoliation treatment and the cotton canopy data of the test area after defoliation treatment; inputting the cotton canopy data change rate of the test area into a linear regression model, and outputting the cotton defoliation rate of the test area. The present invention can accurately predict the cotton defoliation rate of the test area based on the cotton canopy data change rate.

Description

Technical Field

[0001] The present invention relates to the technical field of crop growth detection, and more specifically to a method for predicting cotton defoliation rate based on cotton canopy. Background Art

[0002] Cotton harvesting requires a lot of manual labor. In the context of the increasing shortage of agricultural labor, mechanized harvesting has become an inevitable trend. In actual agricultural production, the method of spraying defoliants and ripening agents is usually used to make the leaves of cotton plants fall off as soon as possible, so as to improve the efficiency of mechanical harvesting and reduce the impurity rate of seed cotton. The defoliation rate is used to evaluate the defoliation process of cotton.

[0003] Traditional methods of investigating defoliation rates mostly use ground surveys to investigate the number of cotton leaves before and after spraying defoliants and ripening agents, and then calculate the difference in leaves before and after the defoliants and ripening agents to calculate the defoliation rate of cotton. This method has cumbersome and complicated investigation steps. In response to this problem, Chinese invention patent CN103766145A discloses a method for rapid identification of the defoliation effect of cotton before harvest. The instrument used is a canopy analyzer. The LAI-2200 canopy analyzer uses a uniquely designed "fisheye" optical sensor with a viewing angle of 148° to measure the changes in light intensity above and below (or inside and outside) the canopy from 5 different zenith angles, and calculates relevant data of the canopy by preparing a radiation propagation model in the canopy. When investigating the defoliation rate of cotton, the invention reads the leaf area coefficient through the canopy analyzer, and then establishes the relationship between the leaf area coefficient and the defoliation rate of cotton. However, the leaf area coefficient is a direct indicator. Assuming that the cotton defoliation rates of multiple areas to be predicted are consistent, but due to factors such as soil fertility and variety, the cotton canopy population size is inconsistent, and different leaf area coefficients will lead to different predicted defoliation rate results, thereby failing to accurately judge the defoliation process of cotton. Summary of the invention

[0004] In view of the above problems, the present invention provides a method for predicting cotton defoliation rate based on cotton canopy. The present invention can accurately predict the cotton defoliation rate of a test area based on the change rate of cotton canopy data.

[0005] The object of the present invention is to provide a method for predicting cotton defoliation rate based on cotton canopy, comprising the following steps:

[0006] Before defoliation, the cotton canopy in the tested area is tested to obtain cotton canopy data before defoliation.

[0007] After the defoliation treatment, the cotton canopy in the test area is tested to obtain the cotton canopy data after the defoliation treatment.

[0008] The cotton canopy data change rate of the tested area is calculated based on the cotton canopy data of the tested area before and after defoliation.

[0009] The cotton canopy data change rate of the area to be tested is input into the linear regression model, and the cotton defoliation rate of the area to be tested is output.

[0010] In a preferred embodiment of the present invention, the method for obtaining the linear regression model is:

[0011] Before the defoliation treatment, the cotton canopy in the test area was tested to obtain the cotton canopy data before the defoliation treatment in the test area.

[0012] After defoliation, the cotton canopy in the test area was tested to obtain the cotton canopy data after defoliation in the test area.

[0013] The cotton canopy data change rate of the test area was calculated based on the cotton canopy data before and after defoliation treatment in the test area.

[0014] The cotton canopy data change rate in the experimental area was taken as the independent variable, and the defoliation rate in the experimental area was taken as the dependent variable. A univariate linear regression equation was established through fitting model screening to obtain a linear regression model.

[0015] In a preferred embodiment of the present invention, the cotton canopy data is light transmittance or leaf area index.

[0016] In a preferred embodiment of the present invention, the fitting model is an exponential function, a logarithmic function or a linear function.

[0017] In a preferred embodiment of the present invention, the screening method is to establish a univariate linear regression equation by screening through the determination coefficient of the fitting model.

[0018] In a preferred embodiment of the present invention, the cotton density in the test area is 3000 plants / mu, and the cotton canopy data is the leaf area index, and the univariate linear regression equation is y=2.8462x 0.7364 .

[0019] In a preferred embodiment of the present invention, when the cotton density in the area to be tested is 3000 plants / mu and the cotton canopy data is light transmittance, the univariate linear regression equation is y=-0.1138x-0.2413.

[0020] In a preferred embodiment of the present invention, the cotton density in the test area is 6000 plants / mu, and the cotton canopy data is the leaf area index, and the univariate linear regression equation is y=3.6253x 0.7064 .

[0021] In a preferred embodiment of the present invention, when the cotton density in the area to be tested is 6000 plants / mu and the cotton canopy data is light transmittance, the univariate linear regression equation is y=-0.0697x+0.0747.

[0022] In a preferred embodiment of the present invention, the number of days for defoliation treatment is 5 to 20 days after spraying the pesticide.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention adopts a canopy analyzer to measure the cotton canopy data before and after defoliation, then calculates the cotton canopy data change rate, and estimates the cotton defoliation rate through the cotton canopy data change rate, thereby avoiding the error in the prediction result of the defoliation rate caused by the different leaf area coefficients in different measured areas, and can accurately judge the defoliation process of the cotton.

[0025] The present invention estimates the cotton defoliation rate based on the cotton canopy data change rate, provides a reference for dynamic monitoring of cotton field defoliation and timely harvesting, and provides an important theoretical basis and technical support for regional-scale precision agricultural management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is the regression model of leaf area index change rate and defoliation rate under the condition of density of 3000 plants / mu.

[0027] Figure 2 This is the regression model of light transmittance change rate and leaf loss rate under the condition of density of 3000 plants / mu.

[0028] Figure 3 This is the regression model for leaf area index change rate and defoliation rate under the condition of density of 6000 plants / mu.

[0029] Figure 4 This is the regression model of light transmittance change rate and leaf loss rate under the condition of density of 6000 plants / mu. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The model of the canopy analyzer used in the present invention is LAI-2200.

[0032] Example 1

[0033] This embodiment provides a method for predicting cotton defoliation rate based on cotton canopy, which specifically includes the following steps:

[0034] Step 1: Establishment of linear regression model

[0035] Step 1.1. Data Collection

[0036] Each plot in the sample area is 9 meters long, 8 meters wide, and has a row spacing of 1 meter. In order to increase the amount of data and consider the consistency of the cotton plant population status, each plot is divided into two parts.

[0037] The experiment was of split-plot design, with density as the main factor and hinseli concentration as the secondary factor.

[0038] The density is designed to be 3,000 plants per mu.

[0039] When spraying Hinseli, a control group was designed, denoted as ck, i.e. 0 g / mu, and the same amount of water was sprayed. Then Hinseli of different concentrations was sprayed in 4 areas, namely: 50 ml / mu, i.e. 71.5 g / mu, denoted as T1; 100 ml / mu, i.e. 143 g / mu, denoted as T2; 200 ml / mu, i.e. 286 g / mu, denoted as T3; 300 ml / mu, i.e. 429 g / mu, denoted as T4.

[0040] (1) Record the data of cotton before treatment

[0041] The leaf data of the day before defoliation were investigated and recorded as the leaf data before drug removal. The LAI of the day before defoliation was measured based on LAI-2200 between 7 and 10 a.m.

[0042] (2) Record the data of cotton after treatment

[0043] The leaf data after defoliation were investigated 5, 10, 15 and 20 days after the application of the drug, recorded as the leaf data after the application of the drug on different days, and the defoliation rate on different days after the application of the drug was calculated according to the formula.

[0044] Defoliation rate = (number of leaves before medication - number of leaves on a certain day after medication) / number of leaves before medication

[0045] At 7:00-10:00 in the morning, based on LAI-2200, the post-drug LAI data of different days were measured at 5, 10, 15, and 20 days after medication, and the LAI change rate on different days after medication was calculated according to the formula.

[0046] It should be noted that the present invention performs data measurement based on the observation method for measuring row crops in the LAI-2200 user manual, and measures a reading on the canopy (A value). The method for obtaining the reading on the canopy (B value) is: usually a diagonal line located between two ridges is used to evenly distribute the B value sampling points, and 4 B values ​​are collected on each diagonal line. The first B value is taken on the ridge, the second B value is taken at 1 / 4 between the two ridges, the third B value is taken in the middle of the two ridges, and the fourth B value is taken at 3 / 4 from the ridge. This diagonal method can better reflect the uniformity of spatial distribution than the vertical line method, and a 270° cover cap is used. After obtaining the A value and the B value, the LAI value is obtained by testing using the LAI-2200 test method.

[0047] LAI change rate = (LAI before medication - LAI after medication) / LAI before medication

[0048] Step 1.2: Establish a linear regression model based on LAI change rate and defoliation rate.

[0049] Specifically, with the defoliation rate as the dependent variable and the leaf area index change rate as the independent variable, three univariate linear regression equations were obtained by establishing three fitting models: exponential function, logarithmic function and linear function. Figure 1 As shown, by comparing the determination coefficients R of the three univariate linear regression equations 2 The linear regression model was obtained by screening.

[0050] like Figure 1 As shown, the linear regression model obtained by screening in this embodiment is y=2.8462x 0.7364 , R 2 =0.7876.

[0051] Step 2: Prediction of defoliation rate

[0052] Before defoliation, the cotton canopy in the test area was tested using a canopy analyzer to obtain cotton canopy data before defoliation.

[0053] After defoliation, the cotton canopy in the test area was tested using a canopy analyzer to obtain the cotton canopy data after defoliation.

[0054] The cotton canopy data change rate of the tested area is calculated based on the cotton canopy data of the tested area before and after defoliation.

[0055] The cotton canopy data change rate of the area to be tested is input into the linear regression model, and the cotton defoliation rate of the area to be tested is output.

[0056] Example 2

[0057] This embodiment provides a method for predicting cotton defoliation rate based on cotton canopy, which specifically includes the following steps:

[0058] Step 1: Establishment of linear regression model

[0059] Step 1.1. Data Collection

[0060] Each plot in the sample area is 9 meters long, 8 meters wide, and has a row spacing of 1 meter. In order to increase the amount of data and consider the consistency of the cotton plant population status, each plot is divided into two parts.

[0061] The experiment was of split-plot design, with density as the main factor and hinseli concentration as the secondary factor.

[0062] The density is designed to be 3,000 plants per mu.

[0063] When spraying Hinseli, a control group was designed, denoted as ck, i.e. 0 g / mu, and the same amount of water was sprayed. Then Hinseli of different concentrations was sprayed in 4 areas, namely: 50 ml / mu, i.e. 71.5 g / mu, denoted as T1; 100 ml / mu, i.e. 143 g / mu, denoted as T2; 200 ml / mu, i.e. 286 g / mu, denoted as T3; 300 ml / mu, i.e. 429 g / mu, denoted as T4.

[0064] (1) Record the data of cotton before treatment

[0065] The leaf data of one day before defoliation were investigated and recorded as the leaf data before drug removal. The DIFN of one day before defoliation was measured based on LAI-2200 at 7:00-10:00 in the morning.

[0066] (2) Record the data of cotton after treatment

[0067] The leaf data after defoliation were investigated 5, 10, 15 and 20 days after the application of the drug, recorded as the leaf data after the application of the drug on different days, and the defoliation rate on different days after the application of the drug was calculated according to the formula.

[0068] Defoliation rate = (number of leaves before medication - number of leaves on a certain day after medication) / number of leaves before medication

[0069] At 7:00-10:00 in the morning, based on LAI-2200, the DIFN data of different days after medication were measured at 5 days, 10 days, 15 days, and 20 days after medication, and the DIFN change rate of different days after medication was calculated according to the formula.

[0070] It should be noted that the present invention performs data measurement based on the observation method for measuring row crops in the LAI-2200 user manual, and measures a reading on the canopy (A value). The method for obtaining the reading on the canopy (B value) is: usually a diagonal line located between two ridges is used to evenly distribute the B value sampling points, and 4 B values ​​are collected on each diagonal line. The first B value is taken on the ridge, the second B value is taken at 1 / 4 between the two ridges, the third B value is taken in the middle of the two ridges, and the fourth B value is taken at 3 / 4 from the ridge. This diagonal method can better reflect the uniformity of spatial distribution than the vertical line method, and a 270° cover cap is used. After obtaining the A value and the B value, the DIFN value is obtained by testing using the LAI-2200 test method.

[0071] DIFN change rate = (DIFN before drug - DIFN after drug) / DIFN before drug

[0072] Step 1.2: Establish a linear regression model based on the DIFN change rate and defoliation rate.

[0073] Specifically, with the defoliation rate as the dependent variable and the leaf area index change rate as the independent variable, three univariate linear regression equations were obtained by establishing three fitting models: exponential function, logarithmic function and linear function. Figure 2 As shown, by comparing the determination coefficients R of the three univariate linear regression equations 2 The linear regression model was obtained by screening.

[0074] like Figure 2 As shown, the linear regression model obtained by screening in this embodiment is y=-0.1138x-0.2413, R 2 =0.6137.

[0075] Step 2: Prediction of defoliation rate

[0076] Before defoliation, the cotton canopy in the test area was tested using a canopy analyzer to obtain cotton canopy data before defoliation.

[0077] After defoliation, the cotton canopy in the test area was tested using a canopy analyzer to obtain the cotton canopy data after defoliation.

[0078] The cotton canopy data change rate of the tested area is calculated based on the cotton canopy data of the tested area before and after defoliation.

[0079] The cotton canopy data change rate of the area to be tested is input into the linear regression model, and the cotton defoliation rate of the area to be tested is output.

[0080] Example 3

[0081] This embodiment provides a method for predicting cotton defoliation rate based on cotton canopy, which specifically includes the following steps:

[0082] Step 1: Establishment of linear regression model

[0083] Step 1.1. Data Collection

[0084] Each plot in the sample area is 9 meters long, 8 meters wide, and has a row spacing of 1 meter. In order to increase the amount of data and consider the consistency of the cotton plant population status, each plot is divided into two parts.

[0085] The experiment was of split-plot design, with density as the main factor and hinseli concentration as the secondary factor.

[0086] The density is designed to be 6,000 plants per mu.

[0087] When spraying Hinseli, a control group was designed, denoted as ck, i.e. 0 g / mu, and the same amount of water was sprayed. Then Hinseli of different concentrations was sprayed in 4 areas, namely: 50 ml / mu, i.e. 71.5 g / mu, denoted as T1; 100 ml / mu, i.e. 143 g / mu, denoted as T2; 200 ml / mu, i.e. 286 g / mu, denoted as T3; 300 ml / mu, i.e. 429 g / mu, denoted as T4.

[0088] (1) Record the data of cotton before treatment

[0089] The leaf data of the day before defoliation were investigated and recorded as the leaf data before drug removal. The LAI of the day before defoliation was measured based on LAI-2200 between 7 and 10 a.m.

[0090] (2) Record the data of cotton after treatment

[0091] The leaf data after defoliation were investigated 5, 10, 15 and 20 days after the application of the drug, recorded as the leaf data after the application of the drug on different days, and the defoliation rate on different days after the application of the drug was calculated according to the formula.

[0092] Defoliation rate = (number of leaves before medication - number of leaves on a certain day after medication) / number of leaves before medication

[0093] At 7:00-10:00 in the morning, based on LAI-2200, the post-drug LAI data of different days were measured at 5, 10, 15, and 20 days after medication, and the LAI change rate on different days after medication was calculated according to the formula.

[0094] It should be noted that the present invention performs data measurement based on the observation method for measuring row crops in the LAI-2200 user manual, and measures a reading on the canopy (A value). The method for obtaining the reading on the canopy (B value) is: usually a diagonal line located between two ridges is used to evenly distribute the B value sampling points, and 4 B values ​​are collected on each diagonal line. The first B value is taken on the ridge, the second B value is taken at 1 / 4 between the two ridges, the third B value is taken in the middle of the two ridges, and the fourth B value is taken at 3 / 4 from the ridge. This diagonal method can better reflect the uniformity of spatial distribution than the vertical line method, and a 270° cover cap is used. After obtaining the A value and the B value, the LAI value is obtained by testing using the LAI-2200 test method.

[0095] LAI change rate = (LAI before medication - LAI after medication) / LAI before medication

[0096] Step 1.2: Establish a linear regression model based on LAI change rate and defoliation rate.

[0097] Specifically, with the defoliation rate as the dependent variable and the leaf area index change rate as the independent variable, three univariate linear regression equations were obtained by establishing three fitting models: exponential function, logarithmic function and linear function. Figure 3 As shown, by comparing the determination coefficients R of the three univariate linear regression equations 2 The linear regression model was obtained by screening.

[0098] like Figure 3 As shown, the linear regression model obtained by screening in this embodiment is y=3.6253x 0.7064 , R 2 =0.7475.

[0099] Step 2: Prediction of defoliation rate

[0100] Before defoliation, the cotton canopy in the test area was tested using a canopy analyzer to obtain cotton canopy data before defoliation.

[0101] After defoliation, the cotton canopy in the test area was tested using a canopy analyzer to obtain the cotton canopy data after defoliation.

[0102] The cotton canopy data change rate of the tested area is calculated based on the cotton canopy data of the tested area before and after defoliation.

[0103] The cotton canopy data change rate of the area to be tested is input into the linear regression model, and the cotton defoliation rate of the area to be tested is output.

[0104] Example 4

[0105] This embodiment provides a method for predicting cotton defoliation rate based on cotton canopy, which specifically includes the following steps:

[0106] Step 1: Establishment of linear regression model

[0107] Step 1.1. Data Collection

[0108] Each plot in the sample area is 9 meters long, 8 meters wide, and has a row spacing of 1 meter. In order to increase the amount of data and consider the consistency of the cotton plant population status, each plot is divided into two parts.

[0109] The experiment was of split-plot design, with density as the main factor and hinseli concentration as the secondary factor.

[0110] The density is designed to be 6,000 plants per mu.

[0111] When spraying Hinseli, a control group was designed, denoted as ck, i.e. 0 g / mu, and the same amount of water was sprayed. Then Hinseli of different concentrations was sprayed in 4 areas, namely: 50 ml / mu, i.e. 71.5 g / mu, denoted as T1; 100 ml / mu, i.e. 143 g / mu, denoted as T2; 200 ml / mu, i.e. 286 g / mu, denoted as T3; 300 ml / mu, i.e. 429 g / mu, denoted as T4.

[0112] (1) Record the data of cotton before treatment

[0113] The leaf data of one day before defoliation were investigated and recorded as the leaf data before drug removal. The DIFN of one day before defoliation was measured based on LAI-2200 at 7:00-10:00 in the morning.

[0114] (2) Record the data of cotton after treatment

[0115] The leaf data after defoliation were investigated 5, 10, 15 and 20 days after the application of the drug, recorded as the leaf data after the application of the drug on different days, and the defoliation rate on different days after the application of the drug was calculated according to the formula.

[0116] Defoliation rate = (number of leaves before medication - number of leaves on a certain day after medication) / number of leaves before medication

[0117] At 7:00-10:00 in the morning, based on LAI-2200, the DIFN data of different days after medication were measured at 5 days, 10 days, 15 days, and 20 days after medication, and the DIFN change rate of different days after medication was calculated according to the formula.

[0118] It should be noted that the present invention performs data measurement based on the observation method for measuring row crops in the LAI-2200 user manual, and measures a reading on the canopy (A value). The method for obtaining the reading on the canopy (B value) is: usually a diagonal line located between two ridges is used to evenly distribute the B value sampling points, and 4 B values ​​are collected on each diagonal line. The first B value is taken on the ridge, the second B value is taken at 1 / 4 between the two ridges, the third B value is taken in the middle of the two ridges, and the fourth B value is taken at 3 / 4 from the ridge. This diagonal method can better reflect the uniformity of spatial distribution than the vertical line method, and a 270° cover cap is used. After obtaining the A value and the B value, the DIFN value is obtained by testing using the LAI-2200 test method.

[0119] DIFN change rate = (DIFN before drug - DIFN after drug) / DIFN before drug

[0120] Step 1.2: Establish a linear regression model based on the DIFN change rate and defoliation rate.

[0121] Specifically, with the defoliation rate as the dependent variable and the leaf area index change rate as the independent variable, three univariate linear regression equations were obtained by establishing three fitting models: exponential function, logarithmic function and linear function. Figure 4 As shown, by comparing the determination coefficients R of the three univariate linear regression equations 2 The linear regression model was obtained by screening.

[0122] like Figure 4 As shown, the linear regression model obtained by screening in this embodiment is y=-0.0697x+0.0747, R 2 =0.7285.

[0123] Step 2: Prediction of defoliation rate

[0124] Before defoliation, the cotton canopy in the test area was tested using a canopy analyzer to obtain cotton canopy data before defoliation.

[0125] After defoliation, the cotton canopy in the test area was tested using a canopy analyzer to obtain the cotton canopy data after defoliation.

[0126] The cotton canopy data change rate of the tested area is calculated based on the cotton canopy data of the tested area before and after defoliation.

[0127] The cotton canopy data change rate of the area to be tested is input into the linear regression model, and the cotton defoliation rate of the area to be tested is output.

[0128] The DIFN change rate and LAI change rate obtained based on the DIFN and LAI measured by LAI-2200 can estimate the cotton defoliation rate, providing a reference for dynamic monitoring of defoliation in cotton fields and timely harvesting, and providing an important theoretical basis and technical support for precision agricultural management and decision-making at the regional scale.

[0129] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0130] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for predicting cotton defoliation rate based on cotton canopy, characterized in that: The following steps are involved: Before defoliation, the cotton canopy in the tested area is tested to obtain cotton canopy data before defoliation; After defoliation treatment by spraying pesticides, the cotton canopy in the tested area is tested to obtain cotton canopy data after defoliation treatment; The cotton canopy data change rate of the tested area is calculated based on the cotton canopy data of the tested area before defoliation treatment and the cotton canopy data of the tested area after defoliation treatment; Input the cotton canopy data change rate of the tested area into the linear regression model, and output the cotton defoliation rate of the tested area; The linear regression model is obtained as follows: Before defoliation treatment, the cotton canopy in the test area was tested to obtain the cotton canopy data before defoliation treatment in the test area; After defoliation treatment by spraying pesticides, the cotton canopy in the test area was tested to obtain cotton canopy data after defoliation treatment in the test area; The cotton canopy data change rate of the test area is calculated based on the cotton canopy data before and after defoliation in the test area; The cotton canopy data change rate in the test area was taken as the independent variable, and the defoliation rate in the test area was taken as the dependent variable. A univariate linear regression equation was established through fitting model screening to obtain a linear regression model. Cotton canopy data are light transmittance or leaf area index.

2. The method for predicting cotton defoliation rate based on cotton canopy according to claim 1, characterized in that: The fitted model is an exponential, logarithmic, or linear function.

3. The method for predicting cotton defoliation rate based on cotton canopy according to claim 2, characterized in that: The screening method is to screen through the determination coefficient of the fitting model and establish a univariate linear regression equation.

4. The method for predicting cotton defoliation rate based on cotton canopy according to claim 3, characterized in that: The cotton density in the test area is 3000 plants / mu. When the cotton canopy data is leaf area index, the linear regression equation is y=2.8462x 0.7364 .

5. The method for predicting cotton defoliation rate based on cotton canopy according to claim 3, characterized in that: The cotton density in the tested area is 3000 plants / mu. When the cotton canopy data is light transmittance, the univariate linear regression equation is y=-0.1138x-0.2413.

6. The method for predicting cotton defoliation rate based on cotton canopy according to claim 3, characterized in that: The cotton density in the test area is 6000 plants / mu. When the cotton canopy data is leaf area index, the linear regression equation is y=3.6253x 0.7064 .

7. The method for predicting cotton defoliation rate based on cotton canopy according to claim 3, characterized in that: The cotton density in the tested area is 6000 plants / mu. When the cotton canopy data is light transmittance, the univariate linear regression equation is y=-0.0697x+0.0747.

8. The method for predicting cotton defoliation rate based on cotton canopy according to claim 1, characterized in that: The days for defoliation treatment are 5 to 20 days after spraying the agent.

Images