Water-saving agricultural irrigation system and method

Abstract

The invention discloses a water-saving agricultural irrigation system and method, and relates to the technical field of agricultural irrigation, the system comprises an irrigation device, a support frame, an insect situation monitoring device, a control center and a server; the irrigation device is arranged at the bottom of the supporting frame. The insect situation monitoring device is far away from the irrigation device and fixed to the supporting frame. The server is used for operating the water-saving agricultural irrigation method according to the received crop image and generating an irrigation control instruction and a deinsectization instruction corresponding to the target field area; the control center is further used for receiving the irrigation control instruction of the server and controlling the operation state of the irrigation device by controlling opening and closing of the irrigation valve so as to irrigate the target field area through the irrigation device; the control center is further used for receiving the insect killing instruction of the server and controlling the running state of the insect situation monitoring device by controlling running of the purple light lamp so as to trap and kill insects through the insect situation monitoring device. The cost and the equipment management difficulty of the irrigation device and the insect situation monitoring device are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural irrigation, in particular to a water-saving agricultural irrigation system and method. BACKGROUND

[0002] In the process of agricultural production, pests and diseases are one of the important factors affecting the yield, quality and comprehensive economic benefits of crops, and have a large overall harmfulness, so it is necessary to prevent and control pests and diseases. At the same time, in order to protect the agricultural production environment and improve the quality of agricultural products, the application of green pest control technology should be strengthened. Green control refers to the use of green plant protection methods. In related research, green control is also called pest green control method. According to the definition of plant protection in China, the concept of green control is to ensure that plant protection work can ensure that man and nature live in harmony, and highlight the protection and support of high yield, high quality, high efficiency, ecology and safety. In the process of agricultural pest control, the traditional control method is chemical pesticide control, which has a relatively prominent effect, but in the application process, pests and diseases will develop drug resistance.

[0003] The existing agricultural irrigation device and pest monitoring device are often separately arranged and independently operated, resulting in high equipment cost and difficult equipment management.

[0004] Therefore, how to reduce the cost and equipment management difficulty of the irrigation device and the pest monitoring device has become a technical problem to be solved. SUMMARY

[0005] The technical problem solved by the present application is that the existing irrigation device and pest monitoring device have high cost and difficult equipment management.

[0006] To solve the above technical problems, the present application provides the following technical scheme: a water-saving agricultural irrigation system, the system comprises: an irrigation device, a support frame, a pest monitoring device, a control center and a server; the irrigation device is arranged at the bottom of the support frame, the pest monitoring device is fixed to the support frame away from the irrigation device, and the control center is fixed to the support frame; the irrigation device is provided with a purple light and a camera device, and the irrigation device is provided with an irrigation valve; the control center is used for sending the crop image shot by the camera device to the server; the server is used for running a water-saving agricultural irrigation method according to the received crop image, and generating irrigation control instructions and pest control instructions corresponding to the target field area; the control center is also used for receiving the irrigation control instructions of the server, controlling the opening and closing of the irrigation valve to control the running state of the irrigation device, so as to irrigate the target field area through the irrigation device; the control center is also used for receiving the pest control instructions of the server, controlling the running state of the pest monitoring device by controlling the operation of the purple light, so as to trap pests and perform pest control through the pest monitoring device.

[0007] The application also provides a water-saving agricultural irrigation method, which comprises the following steps: acquiring a plurality of crop images of a plurality of target farmlands; judging the growth stage of crops in the corresponding target farmland according to the crop images; identifying insect pests according to the crop images to determine the current pest situation data of each target farmland; determining the social structure of each type of insect pest according to the current pest situation data; determining the pesticide resistance corresponding to the insect pest from a plurality of preset pesticide resistance structure comparison tables according to the social structure of each type of insect pest; and determining the pesticide ratio and the pesticide water ratio during irrigation of each target farmland according to the pesticide resistance of each type of insect pest and the growth stage of crops.

[0008] As a preferred scheme of the method, before determining the pesticide ratio and the pesticide water ratio during irrigation of each target farmland according to the pesticide resistance of each type of insect pest and the growth stage of crops, the method further comprises the following steps: predicting the future pest situation data of each target farmland according to the current pest situation data and the historical pest situation data of the plurality of target farmlands; and determining the pesticide ratio and the pesticide water ratio during irrigation of each target farmland according to the pesticide resistance of each type of insect pest, the growth stage of crops and the future pest situation data of each target farmland.

[0009] As a preferred scheme of the method, the step of predicting the future pest situation data of each target farmland according to the current pest situation data and the historical pest situation data of the plurality of target farmlands comprises the following steps: constructing a random variable X=(X1, X2, …, Xn) according to the current pest situation data and the historical pest situation data of the plurality of target farmlands, wherein Xi is a three-dimensional matrix, i is the data at the i-th moment, i and n are positive integers, and i≤n; extracting a feature vector Zi from each Xi in the random variable X to obtain a feature variable Z=(Z1, Z2, …, Zn); performing vector autoregression analysis on the feature variable Z to obtain a predicted feature vector Zn+1 corresponding to the (n+1)-th moment; and mapping the feature vector Zn+1 back to a three-dimensional matrix to obtain a three-dimensional matrix Xn+1 corresponding to the (n+1)-th moment, wherein the three-dimensional matrix Xn+1 is used to represent the pest situation data corresponding to the (n+1)-th moment.

[0010] As a preferred scheme of the method, the step of extracting a feature vector Zi from each Xi in the random variable X to obtain a feature variable Z=(Z1, Z2, …, Zn) comprises the following steps: extracting a feature vector Zi from each Xi in the random variable X according to the principal component analysis method to obtain a feature variable Z=(Z1, Z2, …, Zn).

[0011] As a preferred scheme of the method, two dimensions of the three-dimensional matrix Xi are used to represent the position coordinates between each target field area, one dimension of the three-dimensional matrix Xi is used to represent the pest category, and the element xpjk in the three-dimensional matrix Xi is used to represent the quantity data of the kth pest at the (p, j) coordinate.

[0012] As a preferred scheme of the method, the pest situation data includes the pest type, quantity, and development stage; and the quantity data of the kth pest in the element xpjk is determined according to the quantity and development stage of the kth pest at the (p, j) coordinate.

[0013] As a preferred scheme of the method, the method further includes: determining a quantity coefficient of the kth pest at the (p, j) coordinate according to the proportion of each development stage in the social structure of the kth pest at the (p, j) coordinate; and determining the quantity data of the kth pest at the (p, j) coordinate according to the quantity and data coefficient of the kth pest at the (p, j) coordinate.

[0014] As a preferred scheme of the method, the resistance structure control table includes the control relationship between the resistance multiple and the social structure of the pest, and the difference between adjacent resistance multiples increases when the resistance multiple of the resistance structure control table increases from low to high.

[0015] As a preferred scheme of the method, the determination of the pesticide proportion and the pesticide water ratio during the irrigation of each target field area according to the resistance of each pest and the growth stage of the crop includes: determining the effective degree of the effective component in the pesticide according to the resistance of each pest; and determining the pesticide proportion and the pesticide water ratio during the irrigation of each target field area according to the effective degree of each effective component.

[0016] The present application has the beneficial effect that the irrigation device is arranged at the bottom of the support frame, the pest situation monitoring device is fixed to the support frame away from the irrigation device, and the control center is fixed to the support frame, so as to reduce the equipment cost and equipment management difficulty of the irrigation device. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A basic structure block diagram of a water-saving agricultural irrigation system provided by an embodiment of the present application is provided. Figure 2 A basic flowchart of a water-saving agricultural irrigation method provided by an embodiment of the present application is provided.

[0018] The reference signs in the detailed description are as follows: 1, agricultural irrigation system; 11, control center; 12, support frame; 13, ultraviolet light; 14, camera device. DETAILED DESCRIPTION

[0019] In order to make the above objectives, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all embodiments.

[0020] Embodiment 1, Reference Figure 1 For an embodiment of the present application, a water-saving agricultural irrigation system is provided, which comprises an irrigation device, a support frame 12, a pest monitoring device, a control center 11 and a server; the irrigation device is arranged at the bottom of the support frame 12, the pest monitoring device is fixed to the support frame 12 away from the irrigation device, and the control center 11 is fixed to the support frame 12; the irrigation device is equipped with a purple light 13 and a camera device 14, and the irrigation device is equipped with an irrigation valve; the control center 11 is used to send the images of crops taken by the camera device 14 to the server; the server is used to run a water-saving agricultural irrigation method according to the received images of crops, and generate irrigation control instructions and pest control instructions corresponding to the target field area; the control center 11 is also used to receive the irrigation control instructions of the server, control the running state of the irrigation device by controlling the opening and closing of the irrigation valve, so as to irrigate the target field area through the irrigation device; the control center 11 is also used to receive the pest control instructions of the server, control the running state of the pest monitoring device by controlling the operation of the purple light 13, so as to trap pests and control pests through the pest monitoring device.

[0021] Figure 1 The schematic agricultural irrigation system 1 is a physical structure with a separately installed irrigation device. If the pest monitoring device is further installed, the top of the folded support frame 12 can be pulled out, and then the pest monitoring device is fixed to the support frame 12. Further, the irrigation device and the pest monitoring device can be integrated.

[0022] The server and the control center 11 can be remotely communicated and connected to transmit and receive data with the control center 11.

[0023] In the prior art, when studying the resistance of pests, the migration characteristics of pests are usually ignored. For example, a cotton aphid resistance dynamic monitoring and pesticide recommendation method and system is disclosed in Chinese Patent No. CN115358883B, which characterizes the resistance information of cotton aphids through changes in pest index.

[0024] However, the method of characterizing the resistance information of cotton aphids through changes in pest index is prone to misjudgment of the resistance of pests due to the neglect of the mobility of pests migrating between multiple field areas in actual use, resulting in incorrect pesticide allocation for field areas and inability to effectively defend against pests. Especially in relatively dry areas, multiple field areas are concentrated near water sources, and pests are prone to migrate between multiple field areas.

[0025] Based on this, the application provides a water-saving agricultural irrigation method. After the server obtains image data collected by the pest monitoring device, the water-saving agricultural irrigation method is run. The method includes S110-S170.

[0026] S110, obtaining multiple crop images of multiple target farmland areas.

[0027] S120, determining the growth stage of crops in the corresponding target farmland area according to the crop images.

[0028] According to the hyperspectral image information of crops, image information and spectral information are extracted, the spectral information is processed by a continuous projection algorithm to select a characteristic wavelength set, and a spectral feature is generated by the characteristic wavelength set. The continuous projection method selects the variable with the least redundant information from the information variable, extracts the variable with the least collinearity, and establishes a subset for each wavelength combination. Each subset is calculated by a multiple linear regression method to obtain a root mean square error value, and the subset corresponding to the smoothest and smallest root mean square error value is selected as the characteristic wavelength subset. The texture features and contour features in the image information are obtained by a gray level co-occurrence matrix. Generally, the texture features and contour features are reflected by the characteristic values such as energy, entropy, contrast, uniformity, correlation, variance, average, and variance. The typical features of the leaf area corresponding to each growth stage are obtained by the type information of the crop plants. The deviation degree of the texture features and contour features and the typical features of each growth stage is calculated, the growth stage with the lowest deviation degree is obtained as the growth stage of the crop plants, and the average growth stage of the crops in the target area is determined according to the growth stage of the crop plants. For more specific implementation of S110, refer to the Chinese patent with publication number “CN115358883A”.

[0029] S130, identifying pests according to the crop images to determine the current pest data of each target farmland area.

[0030] The pest data includes the type, number, and developmental stage of the pests.

[0031] S140, determining the social structure of each type of pest according to the current pest data.

[0032] The deep learning model (YOLO or UNet++) is used to segment the pest damage area, extract morphological features (tunnel area, pest cover length, and other parameters), and associate the age of the pests to quantify the corresponding relationship between the feeding characteristics of pests at different ages and the leaf damage morphology.

[0033] In a specific experiment, images of leaves infected by target pests (such as rice leaf roller and diamondback moth) are collected, covering 1-6 instars, with ≥50 leaf images per instar group. The following key features are recorded: tunnel morphology (size, shape, edge features), fecal state (granular or powdery, moisture), and cocoon structure (length, opening method, silk winding density). The instar identification criteria for rice leaf roller can be set as follows: 1st instar, needle-sized semi-transparent white dots, no cocoon; 2nd instar, cocoon length 2-4.5 cm, leaf white stripe wet; 5th instar, cocoon dry and cracked, feces in dry powder form.

[0034] For example, by fixing the 8 million pixel camera (30-50 cm from the leaf, and constant light source), sample collection is performed, and images of leaves infected by different instar pests (such as Spodoptera frugiperda larvae) are collected, covering 1-6 instars, with ≥200 samples per instar, and the insect damage area (such as tunnel and feeding edge) and corresponding instar label are labeled; the image is flipped, rotated, and added with Gaussian noise, and Mixup and Mosaic mixed enhancement are used; the improved UNet++ (such as MRES-UNet++) is used to segment the insect damage area, and the insect body length, head width, and tunnel area are calculated; based on the YOLOv5 or YOLOv11 model, the segmented insect damage feature map is input, and the instar classification is output; the precision (Precision), recall (Recall), and F1 score (target F1≥0.86 when the confidence threshold is 0.475) are used as the evaluation indicators of the model.

[0035] Please refer to Table 1, which shows the morphological criteria for Spodoptera frugiperda larvae.

[0036] Table 1

[0037] S150, according to the social structure of each type of pest, determine the resistance corresponding to the pest from the pre-set multiple resistance structure comparison tables.

[0038] Because the metabolic enzyme activity of older insects is high, and the epidermis is thick, it can be known that the higher the proportion of older pests, the stronger the resistance of the population.

[0039] In the experiment of constructing multiple resistance structure comparison tables, a control group and an experimental group can be set. The control group is a natural population evenly distributed in each instar, and the experimental group can be set as: high-age dominant group, with ≥70% of 5-6 instar individuals; low-age dominant group, with ≥70% of 1-2 instar individuals. Resistance detection is performed on the samples, CYP450 enzyme gene expression (core resistance gene) in the high-age group is detected at the genetic level by PCR, and resistance detection can also be combined with the phenotype level to calculate the median lethal concentration , and the resistance ratio (RR) = experimental group / control group and define high resistance as RR > 106.

[0040] Then, the proportion of old pests is associated with the population resistance: the correlation between the proportion of the old group and the RR value (expected positive correlation, r≥0.6) is calculated, and the correlation between the age structure and the resistance is found as follows: the thickening of the epidermis of old larvae hinders the penetration of pesticides (such as the wax layer of scale insects), and the expression of CYP450 enzymes in 5-6 age larvae can reach 3-5 times that of young larvae, representing the increase of detoxification enzyme activity.

[0041] Therefore, the higher the proportion of old pests, the stronger the resistance of the population. The specific correlation can be further quantified through experiments to obtain a resistance structure control table.

[0042] The resistance structure control table includes the control relationship between the resistance multiple and the social structure of the pests, and the difference between adjacent resistance multiples increases when the resistance multiple of the resistance structure control table increases from low to high.

[0043] S160, predicting the future pest data of each target field area according to the current pest data and the historical pest data of the multiple target field areas.

[0044] Based on the migration of pests, pests can be prevented and controlled in advance to further improve the pest control effect. Specifically, S160 includes S161-S164.

[0045] S161, constructing a random variable X=(X1, X2, …, Xn) according to the current pest data and the historical pest data of the multiple target field areas, wherein Xi is a three-dimensional matrix, i-time data, i and n are positive integers, and i≤n; S162, extracting a feature vector Zi from each Xi in the random variable X according to the principal component analysis method to obtain a feature variable Z=(Z1, Z2, …, Zn); S163, performing vector autoregression analysis on the feature variable Z to obtain a predicted feature vector Zn+1 corresponding to the n+1 time.

[0046] S164, mapping the feature vector Zn+1 back to a three-dimensional matrix to obtain a three-dimensional matrix Xn+1 corresponding to the n+1 time, which is used to represent the n+1 time pest data.

[0047] Preferably, two dimensions of the three-dimensional matrix Xi are used to represent the position coordinates between each target field area, and one dimension of the three-dimensional matrix Xi is used to represent the pest category, and the element xpjk in the three-dimensional matrix Xi is used to represent the number data of the kth pest category at the (p, j) coordinate.

[0048] The quantity data of the kth type of pest in the element xpjk in the three-dimensional matrix Xi is determined according to the quantity and developmental stage of the kth type of pest at the (p, j) coordinate, specifically, the quantity coefficient of the kth type of pest at the (p, j) coordinate is determined according to the proportion of each developmental stage in the social structure of the kth type of pest at the (p, j) coordinate; the quantity data of the kth type of pest at the (p, j) coordinate is determined according to the quantity and data coefficient of the kth type of pest at the (p, j) coordinate, that is, different weights are given to pests according to the migration ability of different age stages of pests to improve the accuracy of prediction, and the specific weight can be set according to the actual situation.

[0049] S170, determine the pesticide ratio and the pesticide water ratio of each target field area at irrigation time according to the resistance of each type of pest, the growth stage of the crop and the future pest situation data of each target field area.

[0050] Specifically, the effective degree of the effective component in the pesticide is determined according to the resistance of each type of pest, and the pesticide ratio and the pesticide water ratio of each target field area at irrigation time are determined according to the effective degree of each effective component.

[0051] The embodiment of the present application determines the current pest situation data of each target field area through the crop image, determines the social structure of each type of pest according to the pest situation data, and determines the resistance corresponding to the pest from the preset multiple resistance structure comparison tables according to the social structure of each type of pest. The social structure of the pest can ignore the influence of pest migration to truly reflect the resistance of the pest, so as to more accurately determine the resistance of the pest and more effectively prevent the pest.

[0052] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (or computer- readable storage media) having computer-usable program code embodied in the medium. The medium can be any available storage media that can be accessed by a computer. By way of example, and not limitation, such computer-usable storage media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other storage medium(s) that can be used to carry or store desired computer program code in the form of instructions or data structures and that can be accessed by a computer. Also, the present application can be embodied in a computer program product that can be traded as goods or merchandise, through the storage medium described above or any other suitable medium. Accordingly, the present application can be embodied in a computer program product that can be traded as goods or merchandise, through the storage medium described above or any other suitable medium. Computer program code embodied in a storage medium is said (referring to a program or code) to "cause a computer" (or Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks

[0053] It should be noted that the above-mentioned embodiments are only used to illustrate but not to limit the technical solutions of the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A water saving agricultural irrigation system characterized in that, The system comprises an irrigation device, a support frame, a pest monitoring device, a control center and a server; the irrigation device is arranged at the bottom of the support frame, the pest monitoring device is fixed to the support frame away from the irrigation device, and the control center is fixed to the support frame; the irrigation device is equipped with a purple light and a camera device, and the irrigation device is equipped with an irrigation valve; The control center is used to send the crop image captured by the camera device to the server; The server is used to run a water-saving agricultural irrigation method according to the received crop image, and generate irrigation control instructions and pest control instructions corresponding to the target field area; The control center is also used to receive the irrigation control instructions of the server, control the opening and closing of the irrigation valve to control the running state of the irrigation device, so as to irrigate the target field area through the irrigation device; The control center is also used to receive the pest control instructions of the server, control the running state of the pest monitoring device through the control of the purple light, so as to capture and control pests through the pest monitoring device.

2. A water saving method for agricultural irrigation, characterized by, The method runs in the server of claim 1, and the method comprises: Obtaining a plurality of crop images of a plurality of target field areas; Judging the growth stage of crops in the corresponding target field area according to the crop image; Identifying pests according to the crop image to determine the current pest data of each target field area; Determining the social structure of each type of pest according to the current pest data; Determining the pesticide resistance corresponding to the pest from a plurality of preset pesticide resistance structure comparison tables according to the social structure of each type of pest; Determining the pesticide ratio and the pesticide water ratio during irrigation of each target field area according to the pesticide resistance of each type of pest and the growth stage of crops.

3. The method of claim 2, wherein, Before determining the pesticide ratio and the pesticide water ratio during irrigation of each target field area according to the pesticide resistance of each type of pest and the growth stage of crops, the method further comprises: Predicting the future pest data of each target field area according to the current pest data and the historical pest data of the plurality of target field areas; Determining the pesticide ratio and the pesticide water ratio during irrigation of each target field area according to the pesticide resistance of each type of pest, the growth stage of crops and the future pest data of each target field area. The prediction of the future pest data of each target field area according to the current pest data and the historical pest data of the plurality of target field areas comprises:

4. The method of claim 3, wherein, Constructing a random variable X=(X1, X2, …, Xn) according to the current pest data and the historical pest data of the plurality of target field areas, wherein Xi is a three-dimensional matrix, i is the data at time i, i and n are positive integers, and i≤n; Extracting a feature vector Zi from each Xi in the random variable X to obtain a feature variable Z=(Z1, Z2, …, Zn); Performing vector autoregressive analysis on the feature variable Z to obtain a predicted feature vector Zn+1 corresponding to time n+1. ​ Map the feature vector Zn+1 back to the three-dimensional matrix to obtain the three-dimensional matrix Xn+1 corresponding to the n+1 moment, and the Xn+1 is used for representing the pest situation data corresponding to the n+1 moment.

5. The method of claim 4, wherein, The feature vector Zi of each Xi in the random variable X is extracted to obtain the feature variable Z=(Z1, Z2,..., Zn). The feature vector Zi of each Xi in the random variable X is extracted according to the principal component analysis method to obtain the feature variable Z=(Z1, Z2,..., Zn).

6. The method of claim 5, wherein, Two dimensions of the three-dimensional matrix Xi are used for representing the position coordinates between each target field area, and one dimension of the three-dimensional matrix Xi is used for representing the pest category, and the element xpjk in the three-dimensional matrix Xi is used for representing the number data of the kth pest at the (p, j) coordinate.

7. The method of claim 6, wherein, The pest situation data includes the pest type, the number and the development stage; and the number data of the kth pest in the element xpjk is determined according to the number and the development stage of the kth pest at the (p, j) coordinate.

8. The method of claim 7, wherein, The method further comprises: The number coefficient of the kth pest at the (p, j) coordinate is determined according to the proportion of each development stage in the social structure of the kth pest at the (p, j) coordinate; The number data of the kth pest at the (p, j) coordinate is determined according to the number and the data coefficient of the kth pest at the (p, j) coordinate.

9. The method of claim 8, wherein, The resistance structure control table includes the control relationship between the resistance multiple and the social structure of the pest, and when the resistance multiple of the resistance structure control table increases from low to high, the difference between adjacent resistance multiples increases.

10. The method of claim 9, wherein, The pesticide proportion and the pesticide water ratio at the irrigation time of each target field area are determined according to the resistance of each pest and the growth stage of the crop, and the method comprises: The effective degree of the effective component in the pesticide is determined according to the resistance of each pest; The pesticide proportion and the pesticide water ratio at the irrigation time of each target field area are determined according to the effective degree of each effective component. The effective degree of the effective component in the pesticide is determined according to the resistance of each pest; The pesticide proportion and the pesticide water ratio at the irrigation time of each target field area are determined according to the effective degree of each effective component.

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