Rapid identification method for infected sweet potato small weevil

Abstract

The invention relates to the technical field of agricultural detection and plant disease and insect pest recognition, in particular to a rapid identification method for infected sweet potato small weevils, which is technically characterized by comprising the following steps: collecting suspected infected sweet potato samples and hermetically enriching volatile organic gases; acquiring a response signal containing a plurality of gas sensitive elements through electronic nose equipment; performing normalization processing on the original response value, and extracting the maximum response value of each sensor to construct a sample odor feature vector; carrying out dimensionality reduction on the multi-dimensional response data by utilizing principal component analysis to obtain a sample principal component vector; and performing similarity comparison on the main component template and a preset main component template of the ipomoea batatas infection in a database, calculating a similarity index S by adopting cosine similarity, and judging that the infection is positive when S is greater than a set threshold value. The identification process does not need DNA extraction, does not need microdissection, is high in response speed, is suitable for field on-site rapid deployment, and has the advantages of being simple and convenient to operate, sensitive in detection, good in repeatability and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural detection and plant pest identification, and in particular, to a rapid identification method for infected sweet potato weevils. BACKGROUND

[0002] The sweet potato weevil (Cylas formicarius) is a common and highly destructive storage and field dual-hazard pest in sweet potato producing areas, belonging to the family Curculionidae of Coleoptera. The larvae of the weevil bore into the internal tissues of sweet potato tubers, and the adults gnaw the epidermis, forming holes, feces and brown spots, which not only leads to yield reduction, shortens storage period, but also seriously affects commodity quality and export trade safety. In recent years, with the development of large-scale sweet potato industry and the extension of storage and transportation chain, the occurrence area and damage degree of sweet potato weevils have shown an increasing trend year by year, and it is urgent to establish an efficient and sensitive rapid detection technology to meet the needs of early warning in the field and market supervision.

[0003] Currently, the detection of sweet potato weevils mainly relies on two technical paths: one is manual visual inspection method, which identifies the infection of pests by recognizing the appearance abnormalities such as holes, brown spots or feces traces, but this method is highly subjective, prone to misjudgment, and difficult to identify early or mild infection samples; the other is molecular biology detection method, such as PCR amplification of mitochondrial COI gene, LAMP constant temperature amplification and other technologies, which have high specificity and sensitivity, but often rely on laboratory equipment, DNA extraction operation and reagent support, which is not suitable for rapid deployment in the field or circulation link.

[0004] In recent years, with the development of electronic nose and gas sensing technology, pest detection based on volatile organic compounds (VOCs) profile recognition has become a research hotspot. Different pests can cause metabolic changes in plants after affecting them, releasing specific small molecule gases such as aldehydes, ketones and alcohols. These VOCs characteristics can be used as "odor fingerprints" for early infection identification. Electronic nose integrates multiple gas sensitive elements and combines principal component analysis (PCA), discriminant analysis, neural network and other algorithms to extract features and recognize patterns from complex gas mixtures, which has shown good application potential in fruit and vegetable maturity assessment, agricultural product quality identification and other directions.

[0005] However, there is no modeling analysis and rapid determination method for the gas characteristics released by infected sweet potato weevils, and related researches are mostly focused on experimental verification or simulated environment, lacking practical and standardized rapid detection procedures, especially in the field, storage logistics and market circulation links, there are still problems such as "detection means lagging behind, low efficiency and inability to trace".

[0006] Therefore, it is urgent to develop a rapid identification method for infected sweet potato weevils to solve the above problems. SUMMARY

[0007] The present application aims to solve the technical problems raised in the background art, and provides a rapid identification method for sweet potato weevils.

[0008] The above-mentioned object of the present application is achieved in that:

[0009] A rapid identification method for sweet potato weevils, comprising the following steps:

[0010] (1) Sample collection: select a suspected infected sweet potato tuber with surface insect holes or discolored areas, cut a sample block with a mass of 50-100 grams, and place it in a sealed detection container;

[0011] (2) Gas enrichment: let the sealed container stand for a time T1=20-30 minutes, so that the sample naturally releases volatile organic gases and enriches them in the sealed space;

[0012] (3) Sensing detection: extract and introduce a gas sample in the container into an electronic nose sensing array containing n gas sensing elements, with a sampling frequency f=2-5 Hz per second, a continuous sampling time T2=180-300 s, and obtain the response value R i,j of the i-th sensor in the j-th sampling;

[0013] (4) Normalized response processing: calculate the normalized response value S i,j according to the following formula:

[0014]

[0015] wherein R i,j is the response value of the i-th sensor in the j-th sampling; R i,0 is the baseline response value of the i-th sensor; S i,j is the normalized response value of the i-th sensor in the j-th sampling; i=1,2,…,n, j=1,2,…,m; wherein n is the number of sensors, and m=f×T2 is the number of samplings;

[0016] (5) Principal component analysis: input the normalized response values S i,j of all sensors into a PCA model, extract the first k principal components PC1, PC2,…, PC k , and construct a sample principal component vector:

[0017] P=[PC1, PC2,…, PC k ];

[0018] wherein PC iScore of the i-th principal component; P is the projection vector of the sample to be tested in the principal component space, representing its odor profile characteristics; k is the number of principal components selected, which satisfies the cumulative contribution rate ≥ 90%;

[0019] (6) Similarity determination: calculate the similarity index S of P and the standard infection template vector P s = [PC 1,a ,PC 2,a ,…,PC k,s ]

[0020]

[0021] Wherein, is the vector length of the sample to be tested; is the vector length of the standard sample; S is the similarity index, the value range is [0, 1], no unit;

[0022] (7) Determination rule: when S ≥ S0, it is judged that the sample is infected with sweet potato weevil; otherwise, it is negative, wherein S0 is the threshold value, the value range is 0.80-0.90, and the recommended value is 0.85 by default.

[0023] Further, the electronic nose array comprises at least 6 types of sensors, respectively responding to the following gas components: aldehydes, ketones, alcohols, esters, amines and mercaptans.

[0024] Further, the closed container has a volume V0 of 1.0-1.5 liters, the sealing material is glass or inert polymer, and the top is provided with a standard sampling port and a one-way valve.

[0025] Further, the number of principal components k is selected to satisfy the following formula:

[0026]

[0027] Wherein, λ i is the eigenvalue corresponding to the i-th principal component; n is the number of gas sensors.

[0028] Further, the maximum response value of the i-th gas sensor is defined as:

[0029]

[0030] Wherein, S i,max is the maximum normalized response value of sensor i, which is used for further feature selection;

[0031] m = f·T2;

[0032] m is the total number of samples for each sensor.

[0033] Further, the standard principal component vector Ps The following average form:

[0034]

[0035] Among them, P j is the principal component vector of the j-th infected sample; N is the total number of training samples, which must be no less than 100.

[0036] Furthermore, all sensors should be zero-calibrated with pure air for no less than 2 minutes before testing, and the baseline drift after calibration should be controlled within the range of ±2%.

[0037] Furthermore, the electronic nose device has a built-in data processing unit that can automatically calculate i ,j , extract the principal component PC i , calculate the similarity index S, and display positive / negative results.

[0038] Furthermore, the test environment control conditions are as follows:

[0039] Temperature T = 20-30°C;

[0040] Relative humidity H = 40% to 70%;

[0041] The temperature fluctuation during the test does not exceed ±1°C.

[0042] Furthermore, the electronic nose device adopts a portable electronic nose.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The method provided by the present invention for rapid detection of sweet potato weevil infection based on volatile gases combines electronic nose gas sensing technology with sweet potato pest diagnosis for the first time. By constructing a standardized sample response template and combining normalization algorithm, principal component analysis and cosine similarity judgment, a rapid identification process is established that does not require molecular manipulation, does not require sample dissection, is non-invasive, and provides real-time feedback. This effectively solves the problems of high misjudgment rate in existing manual inspections, complex PCR detection operations, and unavailability in the field.

[0045] 2. The electronic nose device used in this invention boasts a compact structure and easy operation. Its fully automated detection process requires no specialized training, making it particularly suitable for deployment in environments lacking laboratory facilities, such as major sweet potato producing areas, processing plants, warehousing and logistics sites, and agricultural technology promotion frontlines. The entire detection process, from sampling, enrichment, detection, determination, and result export, can be completed in just 40 minutes, more than three times the efficiency of traditional laboratory testing. It is suitable for high-throughput screening and rapid early warning.

[0046] 3、The identification method has good scalability and reusability, and can be extended to other potato, rhizome crop pest or mildew identification by modifying the training template and odor feature database, has wide universality and industrial popularization prospect, and provides key technical support for establishing sweet potato pest intelligent prevention and control system and "pest traceability + quality tracking" system. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a flowchart of a rapid identification method for sweet potato small elephant beetle infection in the embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0049] The implementation of the present application is described in detail below in combination with specific examples.

[0050] Embodiment: Referring to Figure 1 The embodiment of the present application provides a method for rapidly identifying sweet potato small elephant beetle infection by using volatile gas characteristics, which is suitable for batch rapid detection in sweet potato production sites, processing plants or laboratory field environments. The method is based on the response analysis of volatile organic gases (VOCs) released by sweet potato samples by an electronic nose sensor array, combined with principal component dimensionality reduction and similarity matching algorithm, so as to realize intelligent judgment of pest infection state.

[0051] In the implementation process, first, select suspicious sweet potato tubers with insect holes, discoloration or soft rot on the surface, use sterile knives to cut samples of 50-100 grams, and place them in a sealed detection cavity. The internal volume of the cavity is controlled at about 1.2 liters, and the standing time is 25 minutes, so that the sample releases volatile components such as aldehydes, ketones, alcohols and esters to form measurable gas samples in a closed environment.

[0052] After enrichment, use the built-in air pump of the portable electronic nose device to extract 150mL of sample gas and introduce it into the gas sensitive sensor array module. The electronic nose array used in this embodiment contains 6 different types of gas sensitive elements, each having response capability to the target VOCs category, such as TGS2602 responding to aldehydes, MQ135 responding to ammonia and ketones, TGS813 responding to organic amines, etc.

[0053] After the gas enters the sensing channel, the sampling frequency is set to 4 times per second, and the total sampling time is 240 seconds, so each sensor collects 960 sets of response data, denoted as R i,jwhere i represents the sensor number, j is the time sampling point. Before sampling, all sensors are connected to pure air to establish a stable initial baseline value R i,0 To eliminate the unit difference of different sensors and the interference of fluctuation, the original response data is processed by normalization, and the normalized response value S i,j is calculated for each group of data, and the calculation formula is as follows:

[0054]

[0055] The maximum value in the normalized response sequence of each sensor is extracted and recorded as S i,max , which constitutes the preliminary response feature vector X = [S 1,max , S 2,max , …, S 6,max ]. Based on the feature vector, principal component analysis (PCA) is carried out, and the first k = 3 principal component scores are extracted to constitute the sample principal component vector: P = [PC1, PC2, PC3];

[0056] The PCA model obtains the standard infection feature vector P s = [PC 1,s , PC 2,s , PC 3,s ] through the library samples, and the template is the average feature vector of 100 batches of typical sweet potato small weevil infected samples.

[0057] Then, the cosine similarity algorithm is used to calculate the similarity index S between the to-be-tested sample and the infection template, and the calculation formula is as follows:

[0058]

[0059] Where, the dot product P·P s represents the consistency degree of the sample and the template feature direction, and the vector module length is given by the following formula:

[0060] The present application sets the threshold value S0 = 0.85, when the similarity index S ≥ S0, the system automatically determines that the sweet potato sample is infected with sweet potato small weevil; when S < S0, it is determined to be negative.

[0061] Taking actual detection as an example, the maximum normalized response of a suspicious sample is [0.82, 0.67, 0.31, 0.41, 0.52, 0.22], and the principal component vector after PCA analysis is |0.91, 0.54, 0.38|, compared with the infection template vector |0.89, 0.56, 0.36| in the database, the calculation is:

[0062]

[0063] Significantly higher than the threshold 0.85, determine the infection positive.

[0064] All test results are displayed in real time through the built-in screen of the electronic nose device, and sampling data and analysis results can be exported in CSV format. After the test is completed, the system enters an automatic cleaning process, pure air is introduced for 2 minutes to remove residual gas, and the sensor is restored to within ±2% of the initial baseline to ensure the accuracy and repeatability of the next round of detection.

[0065] The method of the application has simple operation process, short detection time, does not require molecular biology reagents or complex equipment, can be directly deployed on the spot in the field and production place, realizes high-throughput preliminary screening of sweet potato small elephant beetle infection, and has good popularization value and industrial application prospect.

[0066] In September 2024, suspected insect pests occurred in a main sweet potato production area in Yizhou City, Guangxi. The surface of part of the harvested sweet potatoes appeared brown and microporous insect pest traces. In order to determine whether the sweet potato small elephant beetle was infected, the technical personnel carried the portable electronic nose rapid detection system described in the application and conducted on-site detection at the field head.

[0067] Firstly, 5 suspicious sweet potato tubers were randomly collected. After cleaning the surface with a 75% ethanol cotton ball, a sterile knife was used to cut the tissue within a 2 cm range around the insect hole, and the sampling mass of each tuber was about 80 g. The tuber was placed in a 1.2 L sealed gas sampling container and sealed for enrichment for 25 minutes, and the room temperature was kept at 27°C.

[0068] Subsequently, 150 mL of gas samples in the container were extracted using a gas pump on the electronic nose instrument and introduced into the electronic nose sensor array channel. The electronic nose includes 6 gas sensitive elements (MQ135, MQ3, TGS822, TGS2602, TGS813, TGS825), which respond to target VOCs categories respectively. The sampling frequency was set to 4 Hz, the total sampling time was 240 seconds, and 6 sensors x 960 groups of response data were collected. Pure air was introduced for 2 minutes before detection to calibrate the sensor baseline response value R i,0 . Each response value R i,j was automatically normalized by the built-in system of the device, and the formula was as follows:

[0069] After normalization, the maximum normalized response value of each sensor was extracted to form a response vector: X = [0.81, 0.64, 0.36, 0.44, 0.49, 0.27];

[0070] It was input into the built-in PCA dimension reduction module, and the system extracted the first three principal component scores to obtain the sample principal component vector: P = [0.87, 0.53, 0.41];

[0071] The standard infection principal component template vector of sweet potato weevil in the system database is P s = [0.84, 0.55, 0.39];

[0072] The similarity index S is calculated by the following cosine similarity formula:

[0073] It is calculated that P·P s = 0.87*0.84 + 0.53*0.55 + 0.41*0.39 = 0.7308 + 0.2915 + 0.1599 = 1.1822;

[0074]

[0075] Since the similarity index S is approximately equal to 0.9997>0.85, the system judges that the sweet potato sample is infected with sweet potato weevil positively.

[0076] Among the 5 detection samples, the similarity indexes of 4 samples are all greater than 0.90, and they are determined to be positive, and the similarity of 1 sample is 0.42, and it is determined to be negative. The detection time (including sampling, standing, detection and output result) is about 40 minutes, which is significantly improved compared with the traditional laboratory PCR detection (which needs several hours), and no professional reagent and power are needed, so it is suitable for on-site screening in the field.

[0077] After the detection is completed, the device automatically introduces pure air for 2 minutes to clean the channel and restore the sensor response baseline value (within ±2%). All detection results are exported and stored as CSV files for further traceability analysis and pest data management.

[0078] The rapidity, sensitivity and on-site adaptability of the method of the present application are verified through the above embodiment, and the method is particularly suitable for the prediction and rapid response of pest control in the main production area, breeding base and agricultural technology popularization link of sweet potato.

[0079] The above only describes the preferred embodiments of the present application and is not intended to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for rapid identification of sweet potato weevils, characterized in that: The steps include: (1) Sample collection: Select suspected infected sweet potato tubers with insect holes or discolored areas on the surface, cut sample blocks with a mass of 50-100 g, and place them in a sealed test container; (2) Gas enrichment: The sealed container is left to stand for T1 = 20 to 30 minutes to allow the sample to naturally release volatile organic gases and accumulate in the sealed space; (3) Sensing detection: The gas sample in the container is extracted with a volume of V = 100-200 mL and introduced into the electronic nose sensor array containing n gas sensors. The sampling frequency is f = 2-5 Hz per second and the continuous sampling time is T2 = 180-300 s. The response value R of the i-th sensor at the j-th sampling is obtained. i,j ; (4) Normalized response processing: Calculate the normalized response value S according to the following formula: i,j : Among them, R i,j is the response value of the i-th sensor at the j-th sampling; R i,0 is the baseline response value of the i-th sensor; S i,j is the normalized response value of the jth sampling of the i-th sensor; i = 1, 2, ..., n, j = 1, 2, ..., m; where n is the number of sensors and m = f × T2 is the number of sampling times; (5) Principal component analysis: normalized response values ​​S of all sensors i,j Input PCA model and extract the first k principal component scores PC1, PC2, ..., PC k , forming the sample principal component vector: P=[PC1,PC2,…,PC k ]; Among them, PC i is the score of the i-th principal component; P is the projection vector of the sample to be tested in the principal component space, which represents its odor profile characteristics; k is the number of principal components selected, satisfying the cumulative contribution rate ≥ 90%; (6) Similarity determination: Compare P with the standard infection template vector P s =[PC 1,s ,PC 2,S ,…,PC k,s ] Calculate the similarity index S, the formula is: in, is the modulus length of the sample vector to be tested; is the standard sample vector modulus; S is the similarity index, ranging from [0, 1], and has no unit; (7) Judgment rule: When S≥S0, the sample is judged to be infected with the sweet potato weevil; otherwise, it is negative, where S0 is the threshold value, ranging from 0.80 to 0.90, and the default recommended value is 0.

85.

2. A rapid identification method for infecting sweet potato weevils according to claim 1, characterized in that: The electronic nose array comprises at least six types of sensors, which respectively respond to the following gas components: aldehydes, ketones, alcohols, esters, amines and thiols.

3. A rapid identification method for infecting sweet potato weevils according to claim 1, characterized in that: The volume V0 of the sealed container is 1.0-1.5 liters, the sealing material is glass or inert polymer, and a standard sampling port and a one-way valve are provided on the top.

4. A method for rapid identification of sweet potato weevils according to claim 1, characterized in that: The number of principal components k is selected to satisfy the following formula: Among them, λ i is the eigenvalue corresponding to the i-th principal component; n is the number of gas sensors.

5. A rapid identification method for infecting sweet potato weevils according to claim 1, characterized in that: The maximum response value of the i-th gas sensor is defined as: Among them, S i,max is the maximum normalized response value of sensor i, which is used for further feature screening; m=f·T2; m is the total number of sampling times for each sensor.

6. A rapid identification method for infecting sweet potato weevils according to claim 1, characterized in that: Standard principal component vector P s The following average form: Among them, P j is the principal component vector of the jth infected sample; N is the total number of training samples, which must be no less than 100.

7. A rapid identification method for infecting sweet potato weevils according to claim 1, characterized in that: Before testing, all sensors should be zero-calibrated with pure air for no less than 2 minutes, and the baseline drift after calibration should be controlled within the range of ±2%.

8. A method for rapid identification of sweet potato weevils according to claim 1, characterized in that: The electronic nose device has a built-in data processing unit with the function of automatically calculating S i,j , extract the principal component PC i , calculate the similarity index S, and display positive / negative results.

9. A rapid identification method for infecting sweet potato weevils according to claim 1, characterized in that: The test environment control conditions are as follows: Temperature T = 20-30°C; Relative humidity H = 40% to 70%; The temperature fluctuation during the test does not exceed ±1°C.

10. The method for rapid identification of sweet potato weevils according to claim 1, characterized in that: The electronic nose device uses a portable electronic nose.

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