Formula optimization method of thrip attractant

Abstract

The invention provides a formula optimization method of a thrip attractant, which belongs to the technical field of digital data processing, and comprises the following steps: firstly, arranging a plurality of thrip attractant devices containing different formula parameters in a field, regularly collecting insect samples, and carrying out classified counting; the selectivity index of each formula parameter is calculated by analyzing the trapping conditions of thrips and non-target insects. The selectivity indexes of different formulas are compared, and several parameters with better performance are screened out. And then, carrying out medium-scale field verification test on the screened formula, and further determining the finally recommended thrip attractant formula. In general, the research adopts a systematic experimental method, and comprehensively evaluates the performance of the attractant with different formula parameters from two aspects of thrip trapping effect and non-target insect trapping effect; the problem that in the prior art, an optimized formula of a thrip attractant with a high thrip trapping effect and a low non-target insect trapping effect is difficult to obtain is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of digital data processing, and in particular relates to a method for optimizing the formulation of a thrips attractant. Background Art

[0002] Thrips, as an important agricultural pest, are widely distributed all over the world, causing serious losses to agricultural production. The prevention and control of thrips has always been one of the key issues in agricultural management. Existing methods for thrips control mainly include physical control, chemical control and biological control. Among them, chemical control, as the most important means of prevention and control, eliminates thrips by using chemical pesticides. However, chemical pesticides not only cause great pollution to the environment, but may also lead to increased drug resistance in thrips, thereby reducing the control effect. Therefore, the development of safe and efficient new technologies for thrips control is of great practical significance.

[0003] In the prior art, using chemical substances to attract and capture thrips is an effective control method. This method can attract and capture a large number of adult thrips by placing some attractants with specific volatile compounds in the field. Compared with the direct use of chemical pesticides, this method has the advantages of less impact on the environment, long-lasting control effect, and less resistance to pesticides. At present, there are some commercial thrips attractant products, whose formulas mainly include thrips sex pheromones, plant volatile compounds, etc.

[0004] However, there are still some problems with the existing thrips attractants in practical applications. First, the existing attractants have limited attracting effects on thrips and cannot completely replace chemical pesticides. Second, such attractants tend to attract a large number of non-target insects, causing certain interference to the ecological environment. Furthermore, the formula and preparation process of existing attractants generally lack systematic optimization design, making it difficult to further improve the attracting effect and selectivity.

[0005] Therefore, developing a new type of thrips attractant that can efficiently attract thrips and has less impact on non-target insects is a technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of this, the present invention provides a method for optimizing the formula of a thrips attractant, which can solve the problem in the prior art that it is difficult to obtain an optimized formula of a thrips attractant with both high thrips attracting effect and low non-target insect attracting effect.

[0007] The present invention is achieved in that:

[0008] The present invention provides a method for optimizing the formulation of a thrips attractant, comprising the following steps:

[0009] S10, laying out multiple trapping devices containing thrips attractants with different formulation parameters in the field;

[0010] S20, regularly collecting insect samples in the trapping device;

[0011] S30, classifying and counting the insect samples, and recording the types and quantities of thrips and non-target insects;

[0012] S40, calculating the trapping effect of the thrips attractant with each formulation parameter on thrips, including the number and species diversity of thrips;

[0013] S50. Calculate the attracting effect of thrips attractant with each formulation parameter on non-target insects, including the number and species diversity of non-target insects;

[0014] S60, calculating the selectivity index of each of the thrips attractants according to the thrips attracting effect and the non-target insect attracting effect;

[0015] S70, comparing the selectivity indexes of the different thrips attractants, and screening out the thrips attractant formula parameters with high thrips attracting effect and low non-target insect attracting effect;

[0016] S80, conducting a medium-scale field verification test on the screened thrips attractant formulation parameters, and determining the thrips attractant formulation parameters that are ultimately recommended for use based on the field verification test results.

[0017] The frequency of regular collection of insect samples in the S20 is once every 3-7 days; the area of ​​the medium-scale field verification test in the S80 is 1-5 mu.

[0018] On the basis of the above technical scheme, a formula optimization method of a thrips attractant of the present invention can also be improved as follows:

[0019] Wherein, the thrips attractant is provided in the form of an attractant microcapsule, and the attractant microcapsule is composed of a wall material and a core material, wherein the wall material is a chitosan-polylactic acid graft polymer, and the core material is an active ingredient of the thrips attractant.

[0020] The mass ratio of the wall material to the core material is 1:1 to 5:1.

[0021] Furthermore, the basic formula of the active ingredients of the thrips attractant specifically includes: 15-25 parts by weight of jasmone; 10-20 parts by weight of salicylaldehyde; 8-15 parts by weight of eugenol; 5-10 parts by weight of limonene; 3-8 parts by weight of benzyl acetate; 2-6 parts by weight of benzaldehyde; 1-5 parts by weight of lauryl aldehyde; 1-4 parts by weight of geraniol; and 0.5-3 parts by weight of linalool.

[0022] Optionally, 0.1-1 parts by weight of p-methoxybenzaldehyde is also included.

[0023] Furthermore, the trapping device comprises a support, a blue trapping plate and an attractant releasing device, wherein the attractant releasing device is fixed on the support, the blue trapping plate is suspended on the support, and the attractant microcapsule is contained in the attractant releasing device.

[0024] The color of the blue trapping plate is sky blue, and its reflection wavelength range is 450-495nm.

[0025] Furthermore, the preparation steps of the thrips attractant specifically include:

[0026] Step S11, using chitosan as a raw material and DL-lactide to prepare an amphiphilic chitosan-polylactic acid graft polymer CS-co-PLA by a ring-opening polymerization method;

[0027] Step S12, characterizing the structure of the CS-co-PLA by infrared spectroscopy, nuclear magnetic resonance and thermogravimetric analysis;

[0028] Step S13, preparing the active ingredient solution of the thrips attractant;

[0029] Step S14, dissolving the CS-co-PLA in an organic solvent to prepare a wall material solution;

[0030] Step S15, mixing the thrips attractant active ingredient solution with the wall material solution, and preparing a thrips attractant microcapsule emulsion by an emulsified solvent volatilization method;

[0031] Step S16, centrifuging, washing and drying the thrips attractant microcapsule emulsion to obtain thrips attractant microcapsules.

[0032] The emulsification temperature in step S15 is 20-30° C., the emulsification time is 10-30 minutes, and the emulsification speed is 800-1200 rpm.

[0033] Furthermore, the formula parameters include the type and proportion of active ingredients of the thrips attractant, the mass ratio of the wall material to the core material, and process parameters, and the process parameters include emulsification temperature, emulsification time, emulsification speed, solvent volatilization temperature and solvent volatilization time.

[0034] Furthermore, the selectivity index is calculated as follows: selectivity index = (number of thrips trapped / total number of trapped insects) × (number of thrips species / total number of trapped insect species).

[0035] In step S40, the formula for calculating the trapping effect of the thrips attractant of each formula parameter on thrips is specifically expressed as follows:

[0036]

[0037] In the formula, Et is the thrips trapping effect index; Nt is the number of thrips trapped under the formula parameters to be evaluated; S t is the number of thrips species trapped under the formulation parameters to be evaluated; n is the number of all test formulations; T is the trapping time (days); α, β, γ, λ are weight coefficients obtained through multiple experimental fitting; ε is a random error term that follows a normal distribution N(0, σ 2 ).

[0038] Parameter acquisition method: Nt and S t Obtained by classification and counting in step S30. The initial values ​​of α, β, γ, and λ can be set to α=0.5, β=0.3, γ=0.2, and λ=0.05, and then optimized through multiple experimental data.

[0039] In step S50, the formula for calculating the trapping effect of the thrips attractant of each formulation parameter on non-target insects is specifically expressed as follows:

[0040]

[0041] In the formula, E n N is the non-target insect trapping effect index; n is the number of non-target insects trapped under the formulation parameters to be evaluated; S n is the number of non-target insect species trapped under the formulation parameters to be evaluated; n is the number of all test formulations; T is the trapping time (days); ω1, ω2, ω3 are weight coefficients obtained by fitting multiple experiments; δ is the system error, which is assumed to obey the uniform distribution U(-0.05, 0.05).

[0042] Parameter acquisition method: N n and S n Obtained by classification and counting in step S30. The initial values ​​of ω1, ω2, and ω3 can be set to ω1 = 0.4, ω2 = 0.4, and ω3 = 0.2, and then optimized through multiple experimental data.

[0043] In step S60, the formula for calculating the selectivity index of each thrips attractant is specifically expressed as follows:

[0044]

[0045] Where SI is the selectivity index; E t E is the thrips trapping effect index; n is the non-target insect trapping effect index; and E t To N t and E n To N nThe partial derivative of; i is the imaginary unit; θ is the phase angle, reflecting the periodic changes in the trapping effect, d is the number of days after the start of the experiment; ξ is the composite error term, ξ=ε+iδ.

[0046] Parameter acquisition method: E t and E n are obtained from steps S40 and S50 respectively. The partial derivatives are calculated by numerical methods, such as the forward difference method: Where ΔN t Take a smaller value, such as 1.

[0047] In step S70, the criteria for selecting the optimal thrips attractant formulation parameters can be expressed as:

[0048]

[0049] Where F(SI) is the screening function; SI k is the selectivity index of the kth formulation; m is the number of formulations to be screened; is the average value of the selectivity index of all recipes; p is an adjustment parameter used to control the strictness of screening, and its default value is 2.

[0050] Parameter acquisition method: SI k Obtained from step S60. By calculating all SI k p can be set through expert experience or optimized through historical data.

[0051] Compared with the prior art, the formula optimization method of a thrips attractant provided by the present invention has the following beneficial effects: 1. By arranging a plurality of trapping devices with different formula parameters in the field, the trapping data of thrips and non-target insects are systematically collected and analyzed, providing a scientific basis for the optimization of the attractant formula. Compared with the existing empirical formula design, the method of the present invention is more systematic and comprehensive. 2. By adopting a mathematical modeling method, the trapping effect index of thrips and non-target insects under each formula parameter is calculated respectively, and on this basis, the selectivity index of each attractant is obtained. This quantitative evaluation method can more objectively identify the optimal formula parameters with high thrips trapping effect and low non-target insect trapping effect. 3. After screening out the optimal formula, a medium-scale field verification is also carried out to further ensure the trapping effect of the selected formula in actual production applications. This verification mechanism ensures that the recommended attractant formula parameters have good practicality. 4. The attractant microcapsule technology adopted by the present invention can achieve the sustained release and protection of the active ingredients of the attractant, thereby improving the stability and durability of the attractant. The application of this microencapsulation technology also laid the foundation for the subsequent industrial production of attractants.

[0052] In general, the thrips attractant formula optimization method proposed in the present invention can more scientifically and comprehensively identify the optimal attractant formula with high efficiency in attracting thrips and less impact on non-target insects. This not only helps to improve the control effect of thrips and reduce the use of chemical agents, but also can protect the ecological environment to the maximum extent, solving the problem that it is difficult to obtain an optimized thrips attractant formula with both high thrips attracting effect and low non-target insect attracting effect in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0055] like Figure 1 FIG. 1 is a flow chart of a method for optimizing a formula of a thrips attractant provided by the present invention, and the method comprises the following steps:

[0056] S10, laying out multiple trapping devices containing thrips attractants with different formulation parameters in the field;

[0057] S20. Collect insect samples from traps regularly;

[0058] S30. Classify and count insect samples, and record the types and numbers of thrips and non-target insects;

[0059] S40, calculating the trapping effect of the thrips attractant with each formulation parameter on thrips, including the number and species diversity of thrips;

[0060] S50. Calculate the attracting effect of thrips attractant with each formulation parameter on non-target insects, including the number and species diversity of non-target insects;

[0061] S60. Calculate the selectivity index of each thrips attractant based on the thrips attracting effect and the non-target insect attracting effect;

[0062] S70. Compare the selectivity indexes of different thrips attractants and screen out the thrips attractant formula parameters with high thrips attracting effect and low non-target insect attracting effect;

[0063] S80. Conduct a medium-scale field verification test on the selected thrips attractant formulation parameters, and determine the final recommended thrips attractant formulation parameters based on the field verification test results.

[0064] The specific implementation methods of the above steps are described in detail below:

[0065] The specific implementation of step S10 is: multiple trapping devices are arranged in the field of the test area, and each trapping device contains a thrips attractant with different formulation parameters. These formulation parameters include the type and proportion of the active ingredients of the attractant, the mass ratio of the wall material to the core material, and process parameters, such as emulsification temperature, emulsification time, emulsification speed, solvent evaporation temperature, and solvent evaporation time. By setting a variety of different formulation parameters, the optimal thrips attractant formulation can be screened out.

[0066] The specific implementation of step S20 is: regularly collect insect samples captured in each trapping device. The recommended collection frequency is once every 3-7 days. Frequent collection can better reflect the dynamic changes of insects in different time periods.

[0067] The specific implementation of step S30 is: classify and count the collected insect samples. First, the captured insect samples are divided into two categories: thrips and other non-target insects according to morphological characteristics. Then, species identification and quantity statistics are performed for each type of insect, and the number and type of thrips and the number and type of non-target insects are recorded. These data provide a basis for the subsequent evaluation of the trapping effect.

[0068] The specific implementation of step S40 is: according to the data collected in step S30, the trapping effect E of the thrips attractant of each formula parameter on thrips is calculated. t The calculation formula is: Among them, N t is the number of thrips trapped under the formulation parameters to be evaluated, S t is the number of thrips species trapped under the formulation parameters to be evaluated, n is the number of all test formulations, T is the trapping time (days), α, β, γ, λ are empirically determined weight coefficients, and ε is the random error term. This formula can comprehensively consider factors such as the number of thrips and species diversity to evaluate the trapping effect of thrips attractants on thrips.

[0069] The specific implementation of step S50 is: according to the data collected in step S30, the trapping effect E of the thrips attractant of each formula parameter on non-target insects is calculated. n The calculation formula is: Among them, N n is the number of non-target insects trapped under the formulation parameters to be evaluated, S n is the number of non-target insect species trapped under the formulation parameters to be evaluated, ω1, ω2, ω3 are empirically determined weight coefficients, and δ is the systematic error term. This formula takes into account factors such as the number of non-target insects, species diversity, and trapping time, and reflects the degree of attraction of thrips attractants to non-target insects.

[0070] The specific implementation of step S60 is: according to the E calculated in steps S40 and S50 t and E n , and further calculate the selectivity index SI of each thrips attractant. The calculation formula is: in, and E t To N t and E n To N n The partial derivative of θ is the phase angle, which reflects the periodic change of the attractant effect, and ξ is the composite error term. This formula comprehensively considers the attractant effect of thrips and non-target insects, and introduces some dynamic factors to obtain the selectivity index of each thrips attractant.

[0071] The specific implementation of step S70 is: according to the selectivity index SI of various thrips attractants calculated in step S60 k , design a screening function F(SI) to select the optimal formulation parameters: Where m is the number of formulas to be screened, is the average value of the selectivity index of all formulas, and p is the adjustment parameter. This function comprehensively considers the maximum value of the selectivity index and the degree of deviation from the average value, and can screen out the optimal formula parameters with both high thrips attracting effect and low non-target insect attracting effect.

[0072] The specific implementation method of step S80 is: a medium-scale field verification test is conducted on the optimal thrips attractant formula parameters screened out in step S70. The test area is recommended to be 1-5 mu. Through actual field tests, the trapping effect of the screened optimal formula in actual production applications is further verified, providing a basis for the final recommended thrips attractant formula parameters.

[0073] In summary, the patented method achieves non-target trapping screening of thrips attractant formula parameters by setting trapping devices with multiple formula parameters, regularly collecting insect samples, calculating the trapping effect index of thrips and non-target insects, screening the formula parameters with the highest selectivity index, and medium-scale field verification. This method can effectively identify the optimal thrips attractant formula parameters that have both high thrips trapping effect and low non-target insect trapping effect.

[0074] Specifically, the principle of the present invention is to identify the optimal attractant formula parameters with high thrips attracting effect and low non-target insect attracting effect by non-target insect attracting screening. The key to this idea is that thrips, as a specific agricultural pest, have certain differences in their reaction mechanism to chemical attractants and non-target insects. Therefore, when optimizing the attractant formula, it is necessary not only to consider the attracting effect on thrips themselves, but also to take into account the selectivity for non-target insects.

[0075] Specifically, the method of the present invention firstly arranges a plurality of trapping devices with different formula parameters in the field, and regularly collects captured insect samples. By classifying and counting these samples, the number and species information of thrips and non-target insects trapped under each formula parameter can be obtained. On this basis, the trapping effect index of thrips and non-target insects is calculated respectively by mathematical modeling.

[0076] Thrips trapping effect index E t The number and species diversity of thrips trapped, as well as the trapping time and other factors are comprehensively considered to reflect the ability of the attractant to attract thrips. n The two indices take into account factors such as the number of non-target insects trapped, species diversity, and trapping time, reflecting the degree of attraction of the attractant to non-target insects. The calculation formulas for these two indices are as follows:

[0077] Among them, N t and S t are the number and species of thrips trapped, N n and S n are the number and species of non-target insects trapped, T is the trapping time, α, β, γ, λ, ω1, ω2, ω3 are empirically determined weight coefficients, and ε and δ are error terms. These parameters can be fitted through multiple experimental data.

[0078] After obtaining the trapping effect index of thrips and non-target insects, the selectivity index SI of each attractant was further calculated:

[0079] This formula combines the attracting effect of thrips and non-target insects, and takes into account some dynamic factors, such as the periodic changes in the attracting effect. The higher the selectivity index SI, the better the attracting effect of the attractant formula on thrips and the smaller the impact on non-target insects. Therefore, by comparing the selectivity index under different formula parameters, the optimal thrips attractant formula can be screened out.

[0080] Finally, the optimal attractant formula screened out was verified on a medium-scale field basis to further ensure that the recommended formula parameters had a good attractant effect in actual production applications.

[0081] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows: The specific implementation of step S10 is: a plurality of trapping devices are arranged in the field of the test area, and each trapping device contains a thrips attractant with different formulation parameters. These formulation parameters include the type and ratio of the active ingredients of the attractant x i，j , the mass ratio of wall material to core material k and process parameters, such as emulsification temperature T e , emulsification time t e , emulsification speed v e , solvent evaporation temperature T s and solvent evaporation time t s By setting m different recipe parameters x = {x i，j} i＝1，...，n；j＝1，...，p , r={r k} k＝1，...，q , T e , t e , v e , T s , t s , the optimal thrips attractant formula can be screened out.

[0082] The specific implementation of step S20 is: regularly collect insect samples captured in each trapping device. The collection frequency is recommended to be once every Δt days. Frequent collection can better reflect the dynamic changes of insects in different time periods.

[0083] The specific implementation of step S30 is: classify and count the collected insect samples. First, the captured insect samples are classified into thrips according to their morphological characteristics. and other non-target insects Then, the number of thrips N was obtained by species identification and quantity statistics of each type of insect. t and the number of species S t and the number of non-target insects N n and the number of species S n These data provide the basis for subsequent evaluation of trapping effectiveness.

[0084] The specific implementation of step S40 is: according to the data collected in step S30, the trapping effect E of the thrips attractant of each formula parameter on thrips is calculated. t The calculation formula is: Among them, N t is the number of thrips trapped under the formulation parameters to be evaluated, S tis the number of thrips species trapped under the formulation parameters to be evaluated, m is the number of all test formulations, T is the trapping time (days), α, β, γ, λ are empirically determined weight coefficients, and ε is the random error term. This formula takes into account factors such as the number and species diversity of thrips, and evaluates the trapping effect of thrips attractants on thrips by weighted summation. The weight coefficients α, β, γ, λ can be obtained by fitting multiple experimental data.

[0085] The specific implementation of step S50 is: according to the data collected in step S30, the trapping effect E of the thrips attractant of each formula parameter on non-target insects is calculated. n The calculation formula is: Among them, N n is the number of non-target insects trapped under the formulation parameters to be evaluated, S n is the number of non-target insect species trapped under the formulation parameters to be evaluated, ω1, ω2, ω3 are empirically determined weight coefficients, and δ is the systematic error term. This formula adopts the form of a Sigmoid function, comprehensively considering factors such as the number of non-target insects, species diversity, and trapping time, and reflects the degree of attraction of thrips attractants to non-target insects. The weight coefficients ω1, ω2, ω3 can be obtained by fitting multiple experimental data.

[0086] The specific implementation of step S60 is: according to the E calculated in steps S40 and S50 t and E n , and further calculate the selectivity index SI of each thrips attractant. The calculation formula is: in, and E t To N t and E n To N n The partial derivative of is the phase angle, reflecting the periodic changes in the attractant effect, d is the number of days after the start of the experiment, and ξ=ε+iδ is the composite error term. This formula comprehensively considers the attractant effect of thrips and non-target insects, and introduces some dynamic factors to obtain the selectivity index of each thrips attractant. Partial derivatives can be calculated by numerical methods, such as the forward difference method: Where ΔN t Take a smaller value, such as 1.

[0087] The specific implementation of step S70 is: according to the selectivity index SI of various thrips attractants calculated in step S60 k , design a screening function F(SI) to select the optimal formulation parameters: Where m is the number of formulas to be screened, is the average value of the selectivity index of all formulas, and p is the adjustment parameter. This function comprehensively considers the maximum value of the selectivity index and the degree of deviation from the average value, and can screen out the optimal formula parameters with both high thrips attracting effect and low non-target insect attracting effect. The adjustment parameter p can be set by expert experience or optimized by historical data.

[0088] The specific implementation method of step S80 is: conducting a medium-scale field verification test on the optimal thrips attractant formula parameters screened out in step S70. The test area is recommended to be A t Through actual field tests, the trapping effect of the selected optimal formula in actual production applications was further verified, providing a basis for the final recommended thrips attractant formula parameters.

[0089] In summary, the patented method achieves non-target trapping screening of thrips attractant formula parameters by setting trapping devices with multiple formula parameters, regularly collecting insect samples, calculating the trapping effect index of thrips and non-target insects, screening the formula parameters with the highest selectivity index, and medium-scale field verification. This method can effectively identify the optimal thrips attractant formula parameters that have both high thrips trapping effect and low non-target insect trapping effect. The mathematical modeling and algorithm design of the entire method fully consider various factors in practical applications, providing a scientific basis for the efficient prevention and control of thrips.

[0090] In order to better understand and implement the present invention, the following is an example 2 of a specific application scenario of the present invention: A vegetable base is located in a tropical area and has been seriously damaged by thrips for many years. The base covers an area of ​​about 100 mu and mainly grows crops such as tomatoes and peppers. In order to improve the prevention and control effect of thrips, the base manager decides to use the thrips attractant formula optimization method proposed in the present invention to screen and verify the best attractant formula.

[0091] First, they set up 10 traps in 10 different areas of the base. Each trap contained thrips attractant microcapsules with different formula parameters. Specifically, the formula parameters of the attractant microcapsules included:

[0092] Types and proportions of active ingredients of the attractant: Formula 1: 20 parts by weight of jasmone, 15 parts by weight of salicylaldehyde, 12 parts by weight of eugenol, 8 parts by weight of limonene, 5 parts by weight of benzyl acetate, 4 parts by weight of benzaldehyde, 3 parts by weight of lauryl aldehyde, 2 parts by weight of geraniol, and 1 part by weight of linalool; Formula 2: 18 parts by weight of jasmone, 13 parts by weight of salicylaldehyde, 10 parts by weight of eugenol, 6 parts by weight of limonene, 4 parts by weight of benzyl acetate, 3 parts by weight of benzaldehyde, 2 parts by weight of lauryl aldehyde, 1.5 parts by weight of geraniol, and 0.8 parts by weight of linalool; Formula 3: 22 parts by weight of jasmone, 18 parts by weight of salicylaldehyde, 15 parts by weight of eugenol, 10 parts by weight of limonene, 6 parts by weight of benzyl acetate, 5 parts by weight of benzaldehyde, 4 parts by weight of lauryl aldehyde, 3 parts by weight of geraniol, and 2 parts by weight of linalool; ... (a total of 10 different formulas)

[0093] The mass ratio of wall material to core material: 1:1; 2:1; 3:1; 4:1; 5:1;

[0094] Process parameters: emulsification temperature: 25°C; emulsification time: 20 minutes; emulsification speed: 1000rpm; solvent evaporation temperature: 35°C; solvent evaporation time: 3 hours;

[0095] The attractant microcapsules in each trap consist of a wall material and a core material. The wall material is a biodegradable chitosan-polylactic acid graft polymer (CS-co-PLA), which can achieve sustained release and protection of the attractant active ingredients. The core material is the above-mentioned thrips attractant active ingredients of different formulations. These attractant microcapsules were prepared by mixing the wall material solution and the attractant active ingredient solution using the emulsified solvent evaporation method.

[0096] After the traps were set up, the base managers collected samples of insects captured in each device every four days. By classifying and counting these samples, the number and species of thrips and non-target insects trapped under each formula parameter can be obtained, as shown in Table 1:

[0097] Table 1 The trapping of thrips and non-target insects under different attractant formulation parameters

[0098]

[0099]

[0100] According to the data in Table 1, the trapping effect index of thrips and non-target insects for each formulation parameter was calculated.

[0101] Thrips trapping effect index E t The calculation formula is:

[0102] Among them, Nt is the number of thrips trapped, S t is the number of thrips species trapped, t is the trapping time (4 days), α, β, γ, λ are empirically determined weight coefficients, with values ​​of 0.5, 0.3, 0.2, 0.05, respectively, and ε is the random error term. According to the data in Table 1, the E of each formula can be calculated. t The values ​​are shown in Table 2:

[0103] Table 2 Thrips trapping effect index E under different attractant formulation parameters t ;

[0104] Recipe parameters <![CDATA[Thrips trapping effect index E t > Recipe 1 0.643 Recipe 2 0.696 Recipe 3 0.803 Recipe 4 0.728 Recipe 5 0.597 Recipe 6 0.651 Recipe 7 0.719 Recipe 8 0.670 Recipe 9 0.736 Recipe 10 0.685

[0105] Similarly, the non-target insect trapping effect index E n The calculation formula is:

[0106] Among them, N n is the number of non-target insects trapped, S n is the number of non-target insect species trapped, ω1, ω2, ω3 are empirically determined weight coefficients, with values ​​of 0.4, 0.4, 0.2, respectively, and δ is the systematic error term. According to the data in Table 1, the E of each formula can be calculated. n The values ​​are shown in Table 3:

[0107] Table 3 Non-target insect trapping effect index E under different attractant formulation parameters n ;

[0108] Recipe parameters <![CDATA[Non-target insect trapping effect index E n > Recipe 1 0.730 Recipe 2 0.619 Recipe 3 0.488 Recipe 4 0.545 Recipe 5 0.806 Recipe 6 0.685 Recipe 7 0.525 Recipe 8 0.641 Recipe 9 0.445 Recipe 10 0.567

[0109] With the trapping effect index of thrips and non-target insects, the selectivity index SI of each formulation can be further calculated:

[0110] in, and E t To N t and E n To N n The partial derivatives of are calculated by numerical methods; is the phase angle, d is the number of days after the start of the experiment (taken as 16 days), and ξ is the composite error term. The calculation results are shown in Table 4:

[0111] Table 4 Selectivity index SI under different attractant formulation parameters;

[0112] Recipe parameters Selectivity Index SI Recipe 1 0.864 Recipe 2 1.098 Recipe 3 1.588 Recipe 4 1.297 Recipe 5 0.728 Recipe 6 0.942 Recipe 7 1.330 Recipe 8 1.014 Recipe 9 1.606 Recipe 10 1.157

[0113] By comparing the selectivity index under different formulation parameters, it can be seen that formulation 3 and formulation 9 have the highest selectivity index, which are 1.588 and 1.606 respectively, indicating that these two formulations have both high thrips attracting effect and little effect on attracting non-target insects. Therefore, these two formulations can be preliminarily screened as the optimal formulations.

[0114] To further verify the effects of these two formulas in actual production applications, the base management staff set up 5 trapping devices equipped with attractant microcapsules of formula 3 and formula 9 in a 5-mu test area and observed them for 2 months. The results showed that these two formulas significantly increased the number and species diversity of thrips, while having little effect on the trapping of non-target insects. The specific data are shown in Table 5:

[0115] Table 5 Results of medium-scale field validation test

[0116]

[0117] From the data in Table 5, it can be seen that compared with the previous test, in actual production applications, the trapping effect of Formula 3 and Formula 9 on thrips was further improved, while the impact on non-target insects was reduced. This further verifies the superiority of these two formulas in practical applications.

[0118] Taking into account the preliminary experimental data and the results of medium-scale field verification, the base management personnel finally determined Formula 9 as the recommended formula parameters for the thrips attractant. This attractant microcapsule is made of chitosan-polylactic acid grafted polymer (CS-co-PLA) as the wall material, and the core material includes: 22 parts by weight of jasmone, 18 parts by weight of salicylaldehyde, 15 parts by weight of eugenol, 10 parts by weight of limonene, 6 parts by weight of benzyl acetate, 5 parts by weight of benzaldehyde, 4 parts by weight of lauryl aldehyde, 3 parts by weight of geraniol, and 2 parts by weight of linalool. The process parameters are: emulsification temperature 25°C, emulsification time 20 minutes, emulsification speed 1000rpm, solvent evaporation temperature 35°C, and solvent evaporation time 3 hours. The mass ratio of wall material to core material is 3:1.

[0119] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the formulation of a thrips attractant, characterized in that: The following steps are involved: S10, laying out multiple trapping devices containing thrips attractants with different formulation parameters in the field; S20, regularly collecting insect samples in the trapping device; S30, classifying and counting the insect samples, and recording the types and quantities of thrips and non-target insects; S40, calculating the trapping effect of the thrips attractant with each formulation parameter on thrips, including the number and species diversity of thrips; S50. Calculate the attracting effect of thrips attractant with each formulation parameter on non-target insects, including the number and species diversity of non-target insects; S60, calculating the selectivity index of each of the thrips attractants according to the thrips attracting effect and the non-target insect attracting effect; S70, comparing the selectivity indexes of the different thrips attractants, and screening out the thrips attractant formula parameters with high thrips attracting effect and low non-target insect attracting effect; S80, conducting a medium-scale field verification test on the screened thrips attractant formulation parameters, and determining the thrips attractant formulation parameters that are ultimately recommended for use based on the field verification test results.

2. The method for optimizing the formulation of a thrips attractant according to claim 1, wherein: The thrips attractant is in the form of an attractant microcapsule, which is composed of a wall material and a core material, wherein the wall material is a chitosan-polylactic acid graft polymer, and the core material is an active ingredient of the thrips attractant.

3. A method for optimizing the formulation of a thrips attractant according to claim 2, characterized in that: The basic formula of the active ingredients of the thrips attractant specifically includes: 15-25 parts by weight of jasmone; 10-20 parts by weight of salicylaldehyde; 8-15 parts by weight of eugenol; 5-10 parts by weight of limonene; 3-8 parts by weight of benzyl acetate; 2-6 parts by weight of benzaldehyde; 1-5 parts by weight of lauryl aldehyde; 1-4 parts by weight of geraniol; and 0.5-3 parts by weight of linalool.

4. A method for optimizing the formulation of a thrips attractant according to claim 3, characterized in that: The trapping device comprises a support, a blue trapping plate and an attractant releasing device, wherein the attractant releasing device is fixed on the support, the blue trapping plate is suspended on the support, and the attractant microcapsule is arranged in the attractant releasing device.

5. A method for optimizing the formulation of a thrips attractant according to claim 4, characterized in that: The preparation steps of the thrips attractant specifically include: Step S11, using chitosan as a raw material and DL-lactide to prepare an amphiphilic chitosan-polylactic acid graft polymer CS-co-PLA by a ring-opening polymerization method; Step S12, characterizing the structure of the CS-co-PLA by infrared spectroscopy, nuclear magnetic resonance and thermogravimetric analysis; Step S13, preparing the active ingredient solution of the thrips attractant; Step S14, dissolving the CS-co-PLA in an organic solvent to prepare a wall material solution; Step S15, mixing the thrips attractant active ingredient solution with the wall material solution, and preparing a thrips attractant microcapsule emulsion by an emulsified solvent volatilization method; Step S16, centrifuging, washing and drying the thrips attractant microcapsule emulsion to obtain thrips attractant microcapsules.

6. The method for optimizing the formulation of a thrips attractant according to claim 5, wherein: The formula parameters include the type and proportion of active ingredients of the thrips attractant, the mass ratio of the wall material to the core material, and process parameters, and the process parameters include emulsification temperature, emulsification time, emulsification speed, solvent volatilization temperature and solvent volatilization time.

7. The method for optimizing the formula of a thrips attractant according to claim 6, characterized in that: The calculation formula of the selectivity index is: selectivity index=(number of thrips trapped / total number of trapped insects)×(number of thrips species / total number of trapped insect species).

8. The method for optimizing the formulation of a thrips attractant according to claim 1, wherein: The formula for calculating the trapping effect of thrips attractant with each formula parameter on thrips is specifically expressed as follows: In the formula, E t N is the thrips trapping effect index; t is the number of thrips trapped under the formulation parameters to be evaluated; S t is the number of thrips species trapped under the formulation parameters to be evaluated; n is the number of all test formulations; T is the trapping time; α, β, γ, λ are weight coefficients obtained by fitting multiple experiments; ε is the random error term.

9. The method for optimizing the formula of a thrips attractant according to claim 8, characterized in that: The formula for calculating the trapping effect of thrips attractant of each formulation parameter on non-target insects is specifically expressed as follows: In the formula, E n N is the non-target insect trapping effect index; n is the number of non-target insects trapped under the formulation parameters to be evaluated; S n is the number of non-target insect species trapped under the formulation parameters to be evaluated; n is the number of all test formulations; T is the trapping time; ω1, ω2, ω3 are weight coefficients obtained by fitting multiple experiments; δ is the system error.

10. The method for optimizing the formula of a thrips attractant according to claim 9, characterized in that: The formula for calculating the selectivity index of each thrips attractant is specifically expressed as follows: Where SI is the selectivity index; E t E is the thrips trapping effect index; n is the non-target insect trapping effect index; and E t To N t and E n To N n The partial derivative of; i is the imaginary unit; θ is the phase angle, reflecting the periodic changes in the trapping effect, d is the number of days after the start of the experiment; ξ is the composite error term, ξ=ε+iδ.

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