Method and device for screening stress-resistant excellent plant materials on high and cold steep slopes

Abstract

The invention discloses a high and cold steep slope stress-resistant excellent plant material screening method and device, and the method comprises the steps: obtaining a plant growth image obtained through photographing an investigation sample plot area by an unmanned plane; performing plant identification on the plant growth image to obtain each plant identification area on the plant growth image and a corresponding first prediction plant category; determining regional stress resistance excellent parameters of the to-be-screened plant in the investigation sample plot region based on a plant identification region corresponding to the first predicted plant category of the to-be-screened plant; determining overall stress resistance excellent parameters of the to-be-screened plants based on the area stress resistance excellent parameters of the to-be-screened plants in the plurality of investigation sample plot areas; and determining the to-be-screened plants of which the overall stress-resistant excellent parameters are higher than a preset value as stress-resistant excellent plants in the target high and steep slope area. According to the method, the accuracy of screening the high-cold steep slope stress-resistant excellent plant material can be improved.

Description

Technical Field

[0001] The present application relates to the field of ecological restoration technology, and specifically to a method and device for screening high-altitude, cold, steep slopes and excellent stress-resistant plant materials. Background Art

[0002] Steep slopes are extremely difficult to restore due to their poor soil, difficulty in growing vegetation, and inaccessibility. When restoring steep slopes, it is necessary to first identify stress-resistant plants suitable for these slopes. Existing technologies mostly select survey plots for artificial planting, then screen for stress-resistant plants based on the growth of manually collected vegetation. However, manually collecting data on steep slopes is difficult and can result in large errors, resulting in low accuracy in screening for stress-resistant plant materials suitable for high-altitude, cold, and steep slopes.

[0003] That is, the accuracy of screening high-altitude and steep slope-resistant plant materials in the existing technology is low. Summary of the Invention

[0004] The embodiments of the present application provide a method and device for screening high-altitude, cold and steep slope stress-resistant and excellent plant materials, which can improve the accuracy of screening high-altitude, cold and steep slope stress-resistant and excellent plant materials.

[0005] In the first aspect, the present application provides a method for screening high-altitude and steep slope stress-resistant plant materials, comprising: Acquire multiple survey plots within a target high and steep slope area, wherein multiple categories of plants to be screened grow within the survey plots; Obtaining plant growth images obtained by taking photos of the survey plot area by a drone; Performing plant recognition on the plant growth image to obtain each plant recognition area on the plant growth image and a corresponding first predicted plant category; Determining the regional stress resistance parameters of the plants to be screened within the survey plot based on the plant identification region corresponding to the first predicted plant category of the plants to be screened; Determining the overall stress resistance parameters of the plants to be screened based on the regional stress resistance parameters of the plants to be screened in the plurality of survey plots; The plants to be screened whose overall stress resistance parameters are higher than the preset values ​​are determined as the stress resistance plants in the target steep slope area.

[0006] In an optional embodiment, determining the regional stress resistance parameters of the plants to be screened within the survey plot area based on the plant identification region corresponding to the first predicted plant category of the plants to be screened includes: Obtaining the total area of ​​the plant identification region corresponding to the first predicted plant category of the plants to be screened; The regional stress resistance parameters of the plants to be screened within the survey plot area are determined based on the total area of ​​the plant identification area corresponding to the first predicted plant category of the plants to be screened, wherein the larger the total area, the larger the regional stress resistance parameters.

[0007] In an optional embodiment, the method for screening high-altitude and steep slope stress-resistant plant materials comprises: Obtain a three-dimensional topographic map of the target steep slope area; Generating multiple candidate areas on the three-dimensional topographic map multiple times, and treating the multiple candidate areas generated each time as a region set to obtain multiple region sets; Determining the regional feature uniformity of the region set based on the regional information of the plurality of candidate regions in the region set, to obtain the regional feature uniformity of the plurality of region sets, wherein the regional information includes the regional altitude and the regional slope; The plurality of candidate regions in the region set with the highest regional feature uniformity among the plurality of region sets are determined as the plurality of survey plot regions.

[0008] In an optional embodiment, determining the uniformity of regional features of the region set based on the region information of the plurality of candidate regions in the region set includes: Calculating the regional similarity between the candidate region and other candidate regions in the region set based on the region information of the plurality of candidate regions in the region set, and obtaining a plurality of region similarities corresponding to the candidate region; Sorting the multiple region similarities corresponding to the candidate region from large to small and calculating the similarity difference between any two adjacent region similarities after sorting to obtain multiple similarity differences corresponding to the candidate region; Determine the coefficient of variation of multiple similarity differences corresponding to the candidate regions as a uniformity quantization value of the candidate regions, and obtain a quantitative average of the uniformity quantization values ​​of the multiple candidate regions; The regional feature uniformity of the region set is determined based on the quantized average value, wherein the higher the quantized average value is, the smaller the regional feature uniformity is.

[0009] In an optional embodiment, the method for screening high-altitude and steep slope stress-resistant plant materials comprises: Obtaining a plurality of soil improvement formulas and a plurality of plant seed formulas, wherein the soil improvement formulas include dosages of a plurality of soil materials, and the plant seed formulas include dosages of a plurality of categories of plants to be screened; Allocating a soil improvement formula and a plant seed formula to each survey plot area to obtain candidate seed soil area allocation information, and after multiple allocations, obtaining multiple candidate seed soil area allocation information, wherein the candidate seed soil area allocation information includes multiple seed soil area mapping information, and the seed soil area mapping information includes a soil improvement formula and a plant seed formula corresponding to each survey plot area; determining a distribution uniformity of the candidate seed soil region distribution information based on the seed soil region mapping information in the candidate seed soil region distribution information; determining the candidate seed soil area allocation information with the highest distribution uniformity among the plurality of candidate seed soil area allocation information as the target seed soil area allocation information; Based on the target seed soil area distribution information, multiple categories of plants to be screened are planted in multiple survey plots.

[0010] In an optional embodiment, the step of planting multiple categories of plants to be screened in multiple survey plots based on target seed soil area distribution information includes: Obtaining location information of a plurality of survey plot areas; Planning an action route based on location information of the plurality of survey plot areas; The drone is controlled to arrive at each of the survey plot areas based on the action route, and when in the survey plot area, the drone is controlled to release the soil with the soil improvement formula corresponding to the survey plot area and the plant seeds corresponding to the plant seed formula corresponding to the survey plot area in the target seed soil area allocation information in the survey plot area.

[0011] In a second aspect, the present application provides a device for screening high-altitude, cold, steep slope, and stress-resistant plant materials, comprising: A first acquisition module is used to acquire a plurality of survey plots within a target high and steep slope area, wherein a plurality of categories of plants to be screened grow in the survey plots; The second acquisition module is used to obtain plant growth images obtained by taking photos of the survey sample area by a drone; a plant identification module, configured to perform plant identification on the plant growth image, and obtain each plant identification region on the plant growth image and a corresponding first predicted plant category; A first determination module is used to determine the regional stress resistance parameters of the plants to be screened within the survey plot based on the plant identification region corresponding to the first predicted plant category of the plants to be screened; The second determination module is used to determine the overall stress resistance parameters of the plants to be screened based on the regional stress resistance parameters of the plants to be screened in the plurality of survey plots; The third determination module is used to determine the plants to be screened whose overall stress resistance parameters are higher than preset values ​​as the stress resistance plants in the target steep slope area.

[0012] In an optional embodiment, determining the regional stress resistance parameters of the plants to be screened within the survey plot area based on the plant identification region corresponding to the first predicted plant category of the plants to be screened includes: Obtaining the total area of ​​the plant identification region corresponding to the first predicted plant category of the plants to be screened; The regional stress resistance parameters of the plants to be screened within the survey plot area are determined based on the total area of ​​the plant identification area corresponding to the first predicted plant category of the plants to be screened, wherein the larger the total area, the larger the regional stress resistance parameters.

[0013] In an optional embodiment, a three-dimensional topographic map of the target high and steep slope area is obtained; Generating multiple candidate areas on the three-dimensional topographic map multiple times, and treating the multiple candidate areas generated each time as a region set to obtain multiple region sets; Determining the regional feature uniformity of the region set based on the regional information of the plurality of candidate regions in the region set, to obtain the regional feature uniformity of the plurality of region sets, wherein the regional information includes the regional altitude and the regional slope; The plurality of candidate regions in the region set with the highest regional feature uniformity among the plurality of region sets are determined as the plurality of survey plot regions.

[0014] In an optional embodiment, determining the uniformity of regional features of the region set based on the region information of the plurality of candidate regions in the region set includes: Calculating the regional similarity between the candidate region and other candidate regions in the region set based on the region information of the plurality of candidate regions in the region set, and obtaining a plurality of region similarities corresponding to the candidate region; Sorting the multiple region similarities corresponding to the candidate region from large to small and calculating the similarity difference between any two adjacent region similarities after sorting to obtain multiple similarity differences corresponding to the candidate region; Determine the coefficient of variation of multiple similarity differences corresponding to the candidate regions as a uniformity quantization value of the candidate regions, and obtain a quantitative average of the uniformity quantization values ​​of the multiple candidate regions; The regional feature uniformity of the region set is determined based on the quantized average value, wherein the higher the quantized average value is, the smaller the regional feature uniformity is.

[0015] In an optional embodiment, a plurality of soil improvement formulas and a plurality of plant seed formulas are obtained, wherein the soil improvement formulas include dosages of a plurality of soil materials, and the plant seed formulas include dosages of a plurality of categories of plants to be screened; Allocating a soil improvement formula and a plant seed formula to each survey plot area to obtain candidate seed soil area allocation information, and after multiple allocations, obtaining multiple candidate seed soil area allocation information, wherein the candidate seed soil area allocation information includes multiple seed soil area mapping information, and the seed soil area mapping information includes a soil improvement formula and a plant seed formula corresponding to each survey plot area; determining a distribution uniformity of the candidate seed soil region distribution information based on the seed soil region mapping information in the candidate seed soil region distribution information; determining the candidate seed soil area allocation information with the highest distribution uniformity among the plurality of candidate seed soil area allocation information as the target seed soil area allocation information; Based on the target seed soil area distribution information, multiple categories of plants to be screened are planted in multiple survey plots.

[0016] In an optional embodiment, the step of planting multiple categories of plants to be screened in multiple survey plots based on target seed soil area distribution information includes: Obtaining location information of a plurality of survey plot areas; Planning an action route based on location information of the plurality of survey plot areas; The drone is controlled to arrive at each of the survey plot areas based on the action route, and when in the survey plot area, the drone is controlled to release the soil with the soil improvement formula corresponding to the survey plot area and the plant seeds corresponding to the plant seed formula corresponding to the survey plot area in the target seed soil area allocation information in the survey plot area.

[0017] On the third aspect, the electronic device provided in this application includes a memory and a processor. The memory stores a computer program, and the processor is used to run the computer program in the memory to implement the steps in the method for screening high-altitude steep slope stress-resistant excellent plant materials provided in this application.

[0018] Fourthly, the computer-readable storage medium provided in the present application stores a plurality of instructions, which are suitable for loading by a processor to implement the steps in the method for screening high-altitude and steep slope stress-resistant excellent plant materials provided in the present application.

[0019] In the fifth aspect, the computer program product provided in the present application includes a computer program or instructions, which, when executed by a processor, implements the steps in the method for screening high-altitude, steep slope, and stress-resistant excellent plant materials provided in the present application.

[0020] In the present application, compared to related technologies, multiple survey plot areas are obtained within a target high and steep slope area, wherein multiple categories of plants to be screened grow within the survey plot areas; plant growth images obtained by photographing the survey plot areas with a drone are obtained; plant identification is performed on the plant growth images to obtain each plant identification area and the corresponding first predicted plant category on the plant growth image; regional stress resistance parameters of the plants to be screened within the survey plot area are determined based on the plant identification areas corresponding to the first predicted plant category of the plants to be screened; overall stress resistance parameters of the plants to be screened are determined based on the regional stress resistance parameters of the plants to be screened within multiple survey plot areas; and plants to be screened whose overall stress resistance parameters are higher than preset values ​​are determined as stress-resistant and excellent plants in the target high and steep slope area. The present application can improve the accuracy of screening stress-resistant and excellent plant materials on high-altitude and steep slopes. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a schematic diagram of a system for screening high-altitude, cold, steep slope, and stress-resistant plant materials provided by an embodiment of the present application; Figure 2 This is a flow chart of an embodiment of the method for screening high-altitude, cold, steep slope, and stress-resistant excellent plant materials provided in the embodiments of the present application; Figure 3 This is a schematic diagram of a three-dimensional topographic map in one embodiment of the method for screening high-altitude, cold, steep slope, and stress-resistant excellent plant materials provided in an embodiment of the present application; Figure 4 This is a schematic diagram of various plant identification areas on a plant growth image in one embodiment of the method for screening high-altitude, cold, steep slope stress-resistant and excellent plant materials provided by an embodiment of the present application; Figure 5 This is a structural diagram of an embodiment of a device for screening high-altitude, cold, steep slope, and stress-resistant excellent plant materials provided in an embodiment of the present application; Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] It should be noted that the principles of this application are illustrated by implementing them in an appropriate computing environment. The following description is based on the illustrated specific embodiments of this application and should not be considered as limiting other specific embodiments not described in detail herein.

[0024] In the following description of this application, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

[0025] In the following description of this application, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0027] To improve the effectiveness of screening high-quality, stress-resistant plant materials for high-altitude, cold, and steep slopes, embodiments of the present application provide a method for screening high-quality, stress-resistant plant materials for high-altitude, cold, and steep slopes, a screening device for high-quality, stress-resistant plant materials for high-altitude, cold, and steep slopes, an electronic device, a computer-readable storage medium, and a computer program product. The method for screening high-quality, stress-resistant plant materials for high-altitude, cold, and steep slopes can be performed by the screening device for high-quality, stress-resistant plant materials for high-altitude, cold, and steep slopes, or by an electronic device incorporating the screening device for high-quality, stress-resistant plant materials for high-altitude, cold, and steep slopes.

[0028] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0029] Please refer to Figure 1 This application also provides a screening system for high-altitude cold and steep slope stress-resistant excellent plant materials, such as Figure 1 As shown, the high-altitude cold steep slope stress-resistant excellent plant material screening system includes an electronic device. The electronic device is integrated with the high-altitude cold steep slope stress-resistant excellent plant material screening device provided by the present application.

[0030] Among them, electronic devices can be any devices equipped with a processor and have processing capabilities, such as mobile electronic devices with processors such as smartphones, tablets, PDAs, laptops, smart speakers, or fixed electronic devices with processors such as desktop computers, televisions, servers, and industrial equipment.

[0031] In addition, if Figure 1 As shown, the high-altitude cold steep slope stress-resistant excellent plant material screening system can also include a memory for storing the original data, intermediate data and result data in the audio processing process. In the embodiments of the present application, the memory may be a cloud memory. Cloud storage is a new concept extended and developed from the concept of cloud computing. A distributed cloud storage system (hereinafter referred to as the storage system) refers to a storage system that uses cluster applications, grid technology, and distributed storage file systems to bring together a large number of different types of storage devices (storage devices are also called storage nodes) in the network through application software or application interfaces to work together and provide external data storage and business access functions.

[0032] Currently, storage systems utilize a storage method that creates logical volumes. During the creation of a logical volume, physical storage space is allocated for each logical volume. This physical storage space may consist of disks on a specific storage device or several storage devices. When a client stores data on a logical volume, it stores the data on a file system. The file system divides the data into multiple parts, each of which is an object. An object contains not only the data but also additional information such as the data identifier (ID entity). The file system writes each object to the physical storage space of the logical volume and records the storage location of each object. Therefore, when a client requests access to data, the file system can provide access based on the storage location information of each object.

[0033] The storage system allocates physical storage space to logical volumes by pre-dividing the physical storage space into stripes based on the estimated capacity of the objects to be stored in the logical volume (this estimate often has a large margin relative to the actual capacity of the objects to be stored) and the Redundant Array of Independent Disks (RAID) groupings. A logical volume can be understood as a stripe, thereby allocating physical storage space to the logical volume.

[0034] It should be noted that Figure 1The scene diagram of the high-altitude, cold, steep slope and excellent stress-resistant plant material screening system shown is only an example. The high-altitude, cold, steep slope and excellent stress-resistant plant material screening system and scene described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided by the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the high-altitude, cold, steep slope and excellent stress-resistant plant material screening system and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0035] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0036] Please refer to Figure 2 , Figure 2 This is a flow chart of an embodiment of the method for screening high-altitude, cold, steep slope, and stress-resistant excellent plant materials provided in the embodiment of the present application. Figure 2 As shown, the process of the method for screening high-altitude and steep slope stress-resistant excellent plant materials provided in this application is as follows: 201. Acquire multiple survey plots within the target high and steep slope area.

[0037] Among them, there are multiple categories of plants to be screened growing in the survey sample area. The target high and steep slope area can be selected according to specific circumstances, and multiple survey sample areas within the target high and steep slope area can be selected according to specific circumstances.

[0038] The multiple categories of plants to be screened are different in different target high and steep slope areas.

[0039] For example, the target steep slope area is Demonstration Area 1, and the recommended herbaceous plants include two-headed hair, hairy artemisia, spear-leaved sedge, alfalfa, wormwood, Lancang sticky gland fruit, short-awned dwarf ... ; The local dominant plants include white thorn flower, white thorn flower, small-leaved vitex, gray-haired caltrop, head-flowered sweet rush, small orchid snowflake, concave-leaved pyracantha vine, small-saddle-leaved Bauhinia, Sichuan and Yunnan wild lilac, Yunnan native agarwood, winter bean, sand locust, caragana, fragrant wood, rose, sisal, Tibetan willow, elm, caraway, olive, etc.; the suitable stress-resistant plants mainly include purple-leaved barberry, cypress, golden pot, lilac, caragana, multi-flowered indigo, and halberd.

[0040] The target high and steep slope area is Demonstration Area 2, and the herbaceous plants that can be initially selected include short-stemmed dwarf ...

[0041] In the embodiment of the present application, multiple categories of plants to be screened can be screened according to specific circumstances. For example, multiple categories of plants to be screened can be ryegrass, hollyhock, alfalfa, cosmos, Amorpha fruticosa, Rumex euphorbiae, Caragana korshinskii, and Nitraria suspensa.

[0042] In a specific embodiment, the multiple survey plots within the target high and steep slope area can be determined by manual experience.

[0043] In another specific embodiment, a method for screening high-altitude and steep slope stress-resistant plant materials comprises: (1) Obtain a three-dimensional topographic map of the target steep slope area.

[0044] The three-dimensional topographic map of the target steep slope area is as follows: Figure 3 shown.

[0045] (2) Generate multiple candidate regions on the three-dimensional topographic map multiple times, and regard the multiple candidate regions generated each time as a region set to obtain multiple region sets.

[0046] Specifically, multiple candidate regions can be randomly generated on the three-dimensional topographic map. Figure 3 shown.

[0047] (3) Determine the regional feature uniformity of the region set based on the regional information of the multiple candidate regions in the region set, and obtain the regional feature uniformity of the multiple region sets, wherein the regional information includes the regional altitude and the regional slope.

[0048] (4) Multiple candidate areas in the area set with the highest regional characteristic uniformity among the multiple area sets are determined as multiple survey sample areas.

[0049] Among them, the greater the regional feature uniformity, the more uniform the feature distribution of each candidate area in the regional set, which is a better solution. Multiple candidate areas in the regional set with the highest regional feature uniformity among multiple regional sets are determined as multiple survey sample areas.

[0050] In a specific embodiment, the regional feature uniformity of the region set is determined based on the regional information of multiple candidate regions in the region set, including: obtaining a first coefficient of variation of the regional altitudes of the multiple candidate regions in the region set, obtaining a second coefficient of variation of the regional slopes of the multiple candidate regions in the region set, calculating the coefficient average of the first coefficient of variation and the second coefficient of variation, and determining the coefficient average as the regional feature uniformity.

[0051] In another specific embodiment, determining the uniformity of regional features of the region set based on the regional information of the plurality of candidate regions in the region set includes: (1) Based on the regional information of multiple candidate regions in the region set, the regional similarity between the candidate region in the region set and each other candidate region is calculated to obtain multiple regional similarities corresponding to the candidate region.

[0052] In the embodiment of the present application, the region information of the candidate regions is converted into a vector, and the cosine similarity of the vectors of the region information of two candidate regions is determined as the region similarity.

[0053] (2) Sort the similarities of multiple regions corresponding to the candidate region from large to small and calculate the similarity difference between any two adjacent regions after sorting to obtain multiple similarity differences corresponding to the candidate region.

[0054] (3) The coefficient of variation of the multiple similarity differences corresponding to the candidate regions is determined as the uniformity quantization value of the candidate regions, and the quantitative average value of the uniformity quantization values ​​of the multiple candidate regions is obtained.

[0055] Coefficient of Variation (CV): When comparing the degree of dispersion between two sets of data, if the measurement scales differ significantly or the data dimensions differ, directly using the standard deviation is inappropriate. Instead, the influence of the measurement scale and dimension should be eliminated. The CV can do this. It is the ratio of the standard deviation of the raw data to the mean of the raw data. CV is dimensionless, allowing for objective comparisons. In fact, the CV, like the range, standard deviation, and variance, can be considered an absolute value reflecting the degree of dispersion of the data. Its size is affected not only by the dispersion of the variable values ​​but also by the average level of the variable values.

[0056] (4) Determine the regional feature uniformity of the region set based on the quantitative average value, wherein the higher the quantitative average value, the smaller the regional feature uniformity.

[0057] The smaller the coefficient of variation, the more uniform the regional similarity differences between candidate regions. This means that the characteristics of each candidate region vary evenly. The lower the uniformity quantification value and the lower the quantified average value, the higher the regional characteristic uniformity. This means that the higher the regional characteristic uniformity, the more uniform the characteristics of each candidate region, effectively covering various regional conditions and improving the accuracy of survey plot selection.

[0058] For example, there are four candidate regions, one of which corresponds to three region similarities: 0.5, 0.6, and 0.7, and the similarity differences are 0.1 and 0.1. The coefficient of variation of the similarity differences is 0. A smaller coefficient of variation indicates a lower uniformity quantization value, a lower quantization average, and a higher regional feature uniformity. This indicates that the similarity differences between the candidate regions are relatively uniform, and the features of each candidate region vary evenly.

[0059] Furthermore, the screening method for high-altitude and steep slope stress-resistant plant materials includes: (1) Obtain multiple soil improvement formulas and multiple plant seed formulas. The soil improvement formula includes the dosage of multiple soil materials, and the plant seed formula includes the dosage of multiple categories of plants to be screened.

[0060] In the embodiment of the present application, the number of soil improvement formulas is 4. The first soil improvement formula is 1 cubic meter of sieved original soil, 20 kg of biological organic fertilizer, 250 g of compound fertilizer, 100 g of water-retaining agent, 150 g of microbial agent, and 50 g of granulation promoter. The second soil improvement formula is 1 cubic meter of sieved original soil, 25 kg of biological organic fertilizer, 300 g of compound fertilizer, 150 g of water-retaining agent, 200 g of microbial agent, and 100 g of granulation promoter. The third soil improvement formula is 1 cubic meter of sieved original soil, 30 kg of biological organic fertilizer, 350 g of compound fertilizer, 200 g of water-retaining agent, 250 g of microbial agent, and 150 g of granulation promoter. The fourth soil improvement formula is 1 cubic meter of sieved original soil, 35 kg of biological organic fertilizer, 400 g of compound fertilizer, 250 g of water-retaining agent, 300 g of microbial agent, and 200 g of granulation promoter.

[0061] Plant seed formula: White thorn flower (2g / square meter), Ailanthus altissima (1g / square meter), Indigofera multiflora (2.5g / square meter), Buddaria japonica (1.5g / square meter), Winter sedge (2g / square meter), Sophora japonica (1.5g / square meter), Caragana (2.5g / square meter), Cosmos flower (0.5g / square meter), Elymus brevis (1.5g / square meter), Melilotus officinalis (2g / square meter), Alfalfa (0.5g / square meter), Puccinellia tenuifolia (2g / square meter), Poa frostii (4g / square meter), Festuca australis (4g / square meter).

[0062] (2) A soil improvement formula and a plant seed formula are assigned to each survey plot area to obtain candidate seed soil area assignment information. After multiple assignments, multiple candidate seed soil area assignment information are obtained, wherein the candidate seed soil area assignment information includes multiple seed soil area mapping information, and the seed soil area mapping information includes a soil improvement formula and a plant seed formula corresponding to a survey plot area.

[0063] For example, the multiple soil improvement formulas are soil improvement formula S1, soil improvement formula S2, soil improvement formula S3, and soil improvement formula S4, and the multiple plant seed formulas are plant seed formula Z1 and plant seed formula Z2. The multiple survey plot areas are survey plot area D1 and survey plot area D2. The seed soil area mapping information can be soil improvement formula S1, plant seed formula Z1, and survey plot area D1.

[0064] (3) Determine the distribution uniformity of the candidate seed soil region distribution information based on the seed soil region mapping information in the candidate seed soil region distribution information.

[0065] First, based on the plurality of seed soil region mapping information in the candidate seed soil region allocation information, information similarities between the seed soil region mapping information in the candidate seed soil region allocation information and each of the other seed soil region mapping information are calculated to obtain a plurality of information similarities corresponding to the seed soil region mapping information. In this embodiment of the present application, the region information of the seed soil region mapping information is converted into a vector, and the cosine similarity of the vectors of the region information of the two seed soil region mapping information is determined as the information similarity.

[0066] Then, multiple information similarities corresponding to the seed soil region mapping information are sorted from large to small and the similarity difference between any two adjacent information similarities after sorting is calculated to obtain multiple similarity differences corresponding to the seed soil region mapping information.

[0067] Next, the coefficients of variation of the multiple similarity differences corresponding to the seed soil region mapping information are determined as the uniformity quantization value of the seed soil region mapping information, and the quantitative average value of the uniformity quantization values ​​of the multiple seed soil region mapping information is obtained.

[0068] Finally, the distribution uniformity of the candidate seed soil regional distribution information is determined based on the quantitative average value, where the higher the quantitative average value, the smaller the regional characteristic uniformity.

[0069] (4) Determine the candidate seed soil region allocation information with the highest distribution uniformity among the multiple candidate seed soil region allocation information as the target seed soil region allocation information.

[0070] (5) Plant multiple categories of plants to be screened in multiple survey plots based on the target seed soil area distribution information.

[0071] In an embodiment of the present application, multiple categories of plants to be screened are planted in multiple survey plot areas based on target seed soil area allocation information, including: obtaining location information of multiple survey plot areas; planning an action route based on the location information of multiple survey plot areas; controlling a drone to arrive at each survey plot area based on the action route, and when surveying the plot area, controlling the drone to release soil with a soil improvement formula corresponding to the survey plot area and plant seeds corresponding to the plant seed formula corresponding to the survey plot area in the target seed soil area allocation information in the survey plot area.

[0072] 202. Obtain plant growth images obtained by taking photos of the survey sample area by a drone.

[0073] 203. Perform plant recognition on the plant growth image to obtain each plant recognition area on the plant growth image and a corresponding first predicted plant category.

[0074] In the embodiment of the present application, each plant growth image is input into the trained target detection model for plant recognition, and each plant recognition area on the plant growth image and the corresponding first predicted plant category are obtained. Each plant recognition area is a rectangular detection box of the target detection model, and the first predicted plant category is one of the multiple categories of plants to be screened. Specifically, each plant recognition area on the plant growth image is as follows: Figure 4 As shown in the figure, the target detection model is the yolov8 model.

[0075] 204. Determine the regional stress resistance parameters of the plants to be screened within the survey plot area based on the plant identification region corresponding to the first predicted plant category of the plants to be screened.

[0076] In the embodiment of the present application, determining the regional stress resistance parameters of the plants to be screened within the survey plot area based on the plant identification region corresponding to the first predicted plant category of the plants to be screened includes: (1) Obtain the total area of ​​the plant identification region corresponding to the first predicted plant category of the plants to be screened.

[0077] (2) Determine the regional stress resistance parameters of the plants to be screened within the survey plot area based on the total area of ​​the plant identification area corresponding to the first predicted plant category of the plants to be screened, wherein the larger the total area, the larger the regional stress resistance parameters.

[0078] Furthermore, the regional stress resistance parameters of the plants to be screened in the survey plot area are determined based on the total area of ​​the plant identification areas corresponding to the first predicted plant category of the plants to be screened, including: dividing the plant growth image into multiple array-arranged image areas, calculating the area of ​​the plant identification areas corresponding to each first predicted plant category in each image area, determining the area variance of the area of ​​the plant identification areas of the plants to be screened corresponding to the first predicted plant category in each image area, obtaining the area variance of each plant to be screened, and determining the regional stress resistance parameters of the plants to be screened in the survey plot area based on the total area and the area variance, wherein the larger the total area, the larger the regional stress resistance parameters, and the larger the area variance, the larger the regional stress resistance parameters.

[0079] Furthermore, the regional stress resistance parameters of the plants to be screened within the survey plot are determined based on the total area of ​​the plant identification regions corresponding to the first predicted plant category of the plants to be screened. This method includes: identifying two plants to be screened as a first plant and a second plant, obtaining multiple overlapping regions between the plant identification regions of the first plant and the plant identification regions of the second plant on a plant growth image, determining the plant identification region of the first plant where the overlapping regions are located as a first identification region, and determining the plant identification region of the second plant where the overlapping regions are located as a second identification region, inputting the overlapping regions of the first and second identification regions into a target detection model for target detection, and obtaining a second predicted plant category for the overlapping regions. If the second predicted plant category of the overlapping regions is the same as the first predicted plant category of the first identification region, the overlapping regions are determined to belong to the first plant; if the second predicted plant category of the overlapping regions is the same as the first predicted plant category of the second identification region, the overlapping regions are determined to belong to the second plant. If the number of overlapping regions belonging to the first plant in the multiple overlapping regions is greater than the number of overlapping regions belonging to the second plant in the multiple overlapping regions, the height parameter of the first plant is determined to be greater than the height parameter of the second plant. The two plants to be screened are respectively identified as the first plant and the second plant, and a ranking of the height parameters of the plants to be screened is obtained. The regional stress resistance parameters of the plants to be screened in the surveyed sample area are determined based on the total area, area variance and height parameters. The larger the total area, the larger the regional stress resistance parameters; the larger the area variance, the larger the regional stress resistance parameters; the larger the height parameter, the larger the regional stress resistance parameters.

[0080] 205. Determine the overall stress resistance parameters of the plants to be screened based on the regional stress resistance parameters of the plants to be screened in multiple survey plots.

[0081] In a specific implementation, the average value of the regional stress resistance parameters of the plants to be screened in multiple survey plots is determined as the overall stress resistance parameters of the plants to be screened.

[0082] In another specific implementation, a regional weight of the survey plot area is determined based on the regional information of the survey plot area, wherein the greater the regional altitude, the greater the regional weight, and the greater the regional slope, the greater the regional weight. Based on the regional weight of the survey plot area, the regional stress resistance parameters of the plants to be screened in multiple survey plot areas are weighted and summed to obtain the overall stress resistance parameters of the plants to be screened.

[0083] First, based on the altitude and slope data of the survey plots, a weight calculation formula was established: regional weight = altitude coefficient × altitude + slope coefficient × slope (the altitude coefficient and slope coefficient can be determined through expert evaluation or historical data training). This yielded a weight for each survey plot. Next, the regional stress resistance parameters of the plants to be screened were multiplied by the corresponding regional weights and added together to obtain the overall stress resistance parameters of the plants to be screened. For example, consider three survey plots, A, B, and C. Assuming an altitude coefficient of 0.3 and a slope coefficient of 0.7, Region A, at an altitude of 500 meters and a slope of 15°, has a regional weight of 0.3 × 500 + 0.7 × 15 = 150 + 10.5 = 160.5. Region B, at an altitude of 800 meters and a slope of 20°, has a regional weight of 0.3 × 800 + 0.7 × 20 = 240 + 14 = 254. Region C, at an altitude of 300 meters and a slope of 10°, has a regional weight of 0.3 × 300 + 0.7 × 10 = 90 + 7 = 97. If the regional stress resistance parameters of the plant to be screened in regions A, B, and C are 80, 90, and 75, respectively, then its overall stress resistance parameter = 80×160.5 + 90×254 + 75×97 = 12840 + 22860 + 7275 = 42975.

[0084] 206. The plants to be screened whose overall stress resistance parameters are higher than the preset values ​​are determined as the stress resistance plants in the target steep slope area.

[0085] The preset value can be set according to specific circumstances.

[0086] Furthermore, the regional stress resistance parameters of the plants to be screened in the survey plot area are determined based on the total area, area variance and height parameters, including: (1) Obtain NDVI images taken by drones in the survey sample area.

[0087] Among them, drone NDVI images of the survey plot area can be obtained using drone photography. Drone NDVI images and plant growth images are images of the same surface captured by a drone at the same time. The pixels in the drone NDVI image and the plant growth image correspond one-to-one. Drone NDVI images include the NDVI value corresponding to each pixel. NDVI (Normalized Difference Vegetation Index) values ​​quantify vegetation by measuring the difference between near-infrared (strongly reflected by vegetation) and red light (absorbed by vegetation). NDVI values ​​consistently range from -1 to +1. However, there are no clear boundaries for each type of land cover. For example, a negative value is likely water. On the other hand, an NDVI value close to +1 is likely to indicate lush green foliage. An NDVI value close to zero indicates a lack of green foliage and may even indicate an urban area.

[0088] When light strikes an object's surface, it selectively reflects electromagnetic waves of different wavelengths. Spectral reflectance, the ratio of the light flux reflected by an object within a certain wavelength band to the light flux incident on it, is an essential property of an object's surface. Therefore, different objects have different reflectivities for the same wavelength of electromagnetic waves. Multispectral technology is a spectral detection technique that simultaneously captures multiple optical spectrum bands (usually three or more), extending beyond visible light to infrared and ultraviolet light. A multispectral photograph is a color camera image that, from a spectral perspective, contains information from three bands: red, green, and blue. By adding more bands, such as the sum of the bands, to the camera or detector, a multispectral photograph containing multiple bands can be obtained. A common implementation involves combining various filters or beam splitters with multiple types of photographic films to simultaneously capture light signals radiated or reflected from the same target within different narrow spectral bands, resulting in images of the target in several different spectral bands. Vegetation indices are combinations of spectral values ​​from different bands that have specific biochemical significance. Vegetation indices derived from different band combinations have varying predictive power for different indicators. Plants and soil absorb and reflect sunlight wavelengths based on their composition. Plant leaves have strong absorption characteristics in the visible red band and strong reflection characteristics in the near-infrared band. The vegetation index, derived from combining these two bands, can clearly indicate vegetation coverage. Besides vegetation coverage, it can also indicate the effects of moisture, pests, and other factors.

[0089] (2) Based on the UAV NDVI image, the average NDVI value of each pixel point in the plant identification area corresponding to each plant to be screened is determined.

[0090] Specifically, the NDVI values ​​corresponding to the pixels in the plant identification area corresponding to the plants to be screened are counted and the average NDVI value is calculated.

[0091] In a specific embodiment, the pixel points whose NDVI values ​​on the drone NDVI image are greater than the preset value are determined as suspected vegetation pixel points, and the pixel points whose NDVI values ​​on the drone NDVI image are greater than the preset value are determined as suspected non-vegetation pixel points, wherein the preset value can be 0.4 or other values, which can be set according to the specific situation. Each pixel point is determined as the target pixel point in turn, and the 8 adjacent pixel points around the target pixel point are obtained. The target pixel point and the 8 adjacent pixel points form a nine-square grid, the target pixel point is the center position of the nine-square grid, and the 8 adjacent pixel points are set around the target pixel point. Calculate the proportion of the first pixel point of the suspected vegetation pixel point among the 8 adjacent pixel points around the target pixel point. For example, if the number of suspected vegetation pixel points among the 8 adjacent pixel points is 4, then the proportion of the first pixel point is 4 / 8=0.5. If the proportion of the first pixel is greater than the first preset proportion, the target pixel is determined to be a suspected vegetation pixel. If the proportion of the first pixel is not greater than the second preset proportion, the target pixel is determined to be a suspected non-vegetation pixel, and the average NDVI value of the eight adjacent pixels of the target pixel is determined as the updated NDVI value of the target pixel. The first preset proportion is greater than the second preset proportion. The first preset proportion and the second preset proportion can be set according to the specific situation. For example, the first preset proportion is 75% and the second preset proportion is 25%. After traversing every pixel of the drone NDVI image, a new drone NDVI image is obtained.

[0092] Furthermore, the eight adjacent pixels around the target pixel include four adjacent pixels and four diagonal pixels. The target pixel, the four adjacent pixels, and the four diagonal pixels form a nine-square grid. The target pixel is at the center of the nine-square grid, the four diagonal pixels are located at the four corners of the nine-square grid, and the four adjacent pixels and the four diagonal pixels are arranged in sequence around the target pixel. The second pixel ratio of the four adjacent pixels that are suspected to be vegetation pixels and the third pixel ratio of the four diagonal pixels are obtained. The second pixel ratio and the third pixel ratio are weighted and summed to obtain the fourth pixel ratio, wherein the weight coefficient of the second pixel ratio is greater than the weight coefficient of the third pixel ratio. For example, the weight coefficient of the second pixel ratio is 0.6, and the weight coefficient of the third pixel ratio is 0.4. If the fourth pixel ratio is greater than the first preset ratio, the target pixel is determined to be a suspected vegetation pixel. If the fourth pixel ratio is not greater than the second preset ratio, the target pixel is determined to be a suspected non-vegetation pixel, and the average NDVI value of the eight adjacent pixels of the target pixel is determined as the updated NDVI value of the target pixel. The first preset ratio is greater than the second preset ratio. The first preset ratio and the second preset ratio can be set according to the specific situation. For example, the first preset ratio is 75% and the second preset ratio is 25%. After traversing every pixel of the drone NDVI image, a new drone NDVI image is obtained.

[0093] Based on the new drone NDVI image, the NDVI average value of the NDVI values ​​corresponding to each pixel in the plant identification area corresponding to each plant to be screened is determined. Specifically, the NDVI values ​​corresponding to each pixel in the plant identification area corresponding to the plant to be screened are counted and the NDVI average value is calculated.

[0094] (3) Determine the regional stress resistance parameters of the plants to be screened within the survey plot based on the total area, area variance, height parameter and NDVI average value. The larger the total area, the larger the regional stress resistance parameters; the larger the area variance, the larger the regional stress resistance parameters; the larger the height parameter, the larger the regional stress resistance parameters; the larger the NDVI average value, the larger the regional stress resistance parameters.

[0095] Specifically, the influence coefficient of each indicator can be determined through expert scoring, historical data regression analysis, etc., assuming that the regional total area coefficient is a, the area variance coefficient is b, the height parameter coefficient is c, and the NDVI average coefficient is d. The calculation formula for regional stress resistance excellent parameters is established: Regional stress resistance parameters = a × total area of ​​the region + b × area variance + c × height parameter + d × NDVI average value. Substituting the corresponding data of each survey plot area into the formula, the regional stress resistance parameters of the plants to be screened in the area can be calculated.

[0096] For example, consider a survey plot X. Measurements and calculations reveal a total area of ​​200 hectares, an area variance of 15, an altitude parameter (e.g., mean elevation) of 600 meters, and an average NDVI of 0.6. Assuming expert assessment determines a = 0.2, b = 0.3, c = 0.25, and d = 0.25, then the optimal stress resistance parameter for the region is 0.2 × 200 + 0.3 × 15 + 0.25 × 600 + 0.25 × 0.6 = 40 + 4.5 + 150 + 0.15 = 194.65.

[0097] Compared with related technologies, multiple survey plot areas are obtained within a target high and steep slope area, wherein multiple categories of plants to be screened grow within the survey plot areas; plant growth images are obtained by taking photos of the survey plot areas by a drone; plant identification is performed on the plant growth images to obtain each plant identification area and the corresponding first predicted plant category on the plant growth image; regional stress resistance parameters of the plants to be screened within the survey plot area are determined based on the plant identification areas corresponding to the first predicted plant category of the plants to be screened; overall stress resistance parameters of the plants to be screened are determined based on the regional stress resistance parameters of the plants to be screened within multiple survey plot areas; plants to be screened whose overall stress resistance parameters are higher than preset values ​​are determined as stress-resistant plants in the target high and steep slope area. This application can improve the accuracy of screening stress-resistant plant materials on high-altitude and steep slopes.

[0098] To facilitate the implementation of the method for screening high-quality plant materials resistant to stress on high-cold and steep slopes provided in the embodiments of this application, the embodiments of this application also provide a device for screening high-quality plant materials resistant to stress on high-cold and steep slopes based on the above-mentioned method. The meanings of the terms herein are the same as those in the above-mentioned method for screening high-quality plant materials resistant to stress on high-cold and steep slopes. For specific implementation details, please refer to the description in the above method embodiments.

[0099] Please refer to Figure 5 , Figure 5 70 is a schematic structural diagram of an embodiment of a device for screening high-altitude, cold, steep slope, and stress-resistant plant materials provided in an embodiment of the present application. The device for screening high-altitude, cold, steep slope, and stress-resistant plant materials may include a first acquisition module 701, a second acquisition module 702, a plant identification module 703, a first determination module 704, a second determination module 705, and a third determination module 706, wherein: The first acquisition module 701 is used to acquire a plurality of survey plots in a target high and steep slope area, wherein the survey plots have a plurality of categories of plants to be screened growing therein; The second acquisition module 702 is used to acquire plant growth images obtained by taking photos of the survey plot area by a drone; The plant identification module 703 is configured to perform plant identification on the plant growth image to obtain each plant identification region on the plant growth image and a corresponding first predicted plant category; The first determination module 704 is configured to determine the regional stress resistance parameters of the plants to be screened within the survey plot based on the plant identification region corresponding to the first predicted plant category of the plants to be screened; The second determination module 705 is used to determine the overall stress resistance parameters of the plants to be screened based on the regional stress resistance parameters of the plants to be screened in the plurality of survey plots; The third determination module 706 is configured to determine the plants to be screened whose overall stress resistance parameters are higher than a preset value as stress resistance plants in the target steep slope area.

[0100] In an optional embodiment, determining the regional stress resistance parameters of the plants to be screened within the survey plot area based on the plant identification region corresponding to the first predicted plant category of the plants to be screened includes: Obtaining the total area of ​​the plant identification region corresponding to the first predicted plant category of the plants to be screened; The regional stress resistance parameters of the plants to be screened within the survey plot area are determined based on the total area of ​​the plant identification area corresponding to the first predicted plant category of the plants to be screened, wherein the larger the total area, the larger the regional stress resistance parameters.

[0101] In an optional embodiment, a three-dimensional topographic map of the target high and steep slope area is obtained; Generating multiple candidate areas on the three-dimensional topographic map multiple times, and treating the multiple candidate areas generated each time as a region set to obtain multiple region sets; Determining the regional feature uniformity of the region set based on the regional information of the plurality of candidate regions in the region set, to obtain the regional feature uniformity of the plurality of region sets, wherein the regional information includes the regional altitude and the regional slope; The plurality of candidate regions in the region set with the highest regional feature uniformity among the plurality of region sets are determined as the plurality of survey plot regions.

[0102] In an optional embodiment, determining the uniformity of regional features of the region set based on the region information of the plurality of candidate regions in the region set includes: Calculating the regional similarity between the candidate region and other candidate regions in the region set based on the region information of the plurality of candidate regions in the region set, and obtaining a plurality of region similarities corresponding to the candidate region; Sorting the multiple region similarities corresponding to the candidate region from large to small and calculating the similarity difference between any two adjacent region similarities after sorting to obtain multiple similarity differences corresponding to the candidate region; Determine the coefficient of variation of multiple similarity differences corresponding to the candidate regions as a uniformity quantization value of the candidate regions, and obtain a quantitative average of the uniformity quantization values ​​of the multiple candidate regions; The regional feature uniformity of the region set is determined based on the quantized average value, wherein the higher the quantized average value is, the smaller the regional feature uniformity is.

[0103] In an optional embodiment, a plurality of soil improvement formulas and a plurality of plant seed formulas are obtained, wherein the soil improvement formulas include dosages of a plurality of soil materials, and the plant seed formulas include dosages of a plurality of categories of plants to be screened; Allocating a soil improvement formula and a plant seed formula to each survey plot area to obtain candidate seed soil area allocation information, and after multiple allocations, obtaining multiple candidate seed soil area allocation information, wherein the candidate seed soil area allocation information includes multiple seed soil area mapping information, and the seed soil area mapping information includes a soil improvement formula and a plant seed formula corresponding to each survey plot area; determining a distribution uniformity of the candidate seed soil region distribution information based on the seed soil region mapping information in the candidate seed soil region distribution information; determining the candidate seed soil area allocation information with the highest distribution uniformity among the plurality of candidate seed soil area allocation information as the target seed soil area allocation information; Based on the target seed soil area distribution information, multiple categories of plants to be screened are planted in multiple survey plots.

[0104] In an optional embodiment, the step of planting multiple categories of plants to be screened in multiple survey plots based on target seed soil area distribution information includes: Obtaining location information of a plurality of survey plot areas; Planning an action route based on location information of the plurality of survey plot areas; The drone is controlled to arrive at each of the survey plot areas based on the action route, and when in the survey plot area, the drone is controlled to release the soil with the soil improvement formula corresponding to the survey plot area and the plant seeds corresponding to the plant seed formula corresponding to the survey plot area in the target seed soil area allocation information in the survey plot area.

[0105] The specific implementation of each of the above modules can be found in the previous embodiments and will not be described again here.

[0106] Compared with related technologies, multiple survey plot areas are obtained within a target high and steep slope area, wherein multiple categories of plants to be screened grow within the survey plot areas; plant growth images are obtained by taking photos of the survey plot areas by a drone; plant identification is performed on the plant growth images to obtain each plant identification area and the corresponding first predicted plant category on the plant growth image; regional stress resistance parameters of the plants to be screened within the survey plot area are determined based on the plant identification areas corresponding to the first predicted plant category of the plants to be screened; overall stress resistance parameters of the plants to be screened are determined based on the regional stress resistance parameters of the plants to be screened within multiple survey plot areas; plants to be screened whose overall stress resistance parameters are higher than preset values ​​are determined as stress-resistant plants in the target high and steep slope area. This application can improve the accuracy of screening stress-resistant plant materials on high-altitude and steep slopes.

[0107] Please refer to Figure 6 , Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0108] The electronic device may include components such as a processor 101 with one or more processing cores, a memory 102 with one or more computer-readable storage media, a power supply 103, and an input unit 104. Those skilled in the art will appreciate that the electronic device structure shown in the figure does not limit the electronic device and may include more or fewer components than shown, or combine certain components, or arrange the components differently. Among them: Processor 101 is the control center of the electronic device. It connects all parts of the electronic device using various interfaces and circuits. It executes software programs and / or modules stored in memory 102 and accesses data stored in memory 102 to perform various functions of the electronic device and process data. Optionally, processor 101 may include one or more processing cores. Alternatively, processor 101 may integrate an application processor and a modem processor. The application processor primarily handles the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 101.

[0109] Memory 102 can be used to store software programs and modules. Processor 101 executes various functional applications and data processing by running the software programs and modules stored in memory 102. Memory 102 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the electronic device. Furthermore, memory 102 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory 102 may also include a memory controller to provide processor 101 with access to memory 102.

[0110] The electronic device also includes a power supply 103 for supplying power to various components. Optionally, the power supply 103 can be logically connected to the processor 101 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 103 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0111] The electronic device may further include an input unit 104, which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0112] Although not shown, the electronic device may also include a display unit, an image acquisition element, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 101 in the electronic device will load the executable code corresponding to one or more computer programs into the memory 102 according to the following instructions, and the processor 101 will execute the steps of the method for screening high-altitude and steep slope stress-resistant plant materials provided in this application, such as: Acquire multiple survey plot areas within a target high and steep slope area, wherein multiple categories of plants to be screened grow in the survey plot areas; obtain plant growth images obtained by photographing the survey plot areas with a drone; perform plant identification on the plant growth images to obtain each plant identification area and a corresponding first predicted plant category on the plant growth image; determine regional stress resistance parameters of the plants to be screened within the survey plot areas based on the plant identification areas corresponding to the first predicted plant categories of the plants to be screened; determine overall stress resistance parameters of the plants to be screened based on the regional stress resistance parameters of the plants to be screened within multiple survey plot areas; and determine the plants to be screened whose overall stress resistance parameters are higher than preset values ​​as stress resistance plants in the target high and steep slope area.

[0113] It should be noted that the electronic device provided in the embodiment of the present application belongs to the same concept as the method for screening excellent plant materials resistant to stress on high-altitude steep slopes in the above embodiment. The specific implementation process is detailed in the above related embodiments and will not be repeated here.

[0114] This application also provides a computer-readable storage medium storing a computer program. When the stored computer program is executed on a processor of an electronic device provided in an embodiment of this application, the processor of the electronic device executes the steps of the method for screening high-quality, stress-resistant plant materials for high-altitude, cold, steep slopes provided in this application. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0115] The present application also provides a computer program product or computer program, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to execute various optional implementations of the aforementioned method for screening high-quality stress-resistant plant materials for high-altitude, cold, steep slopes.

[0116] The above is a detailed introduction to the method and device for screening high-altitude steep slope resistant plant materials provided by this application. Specific examples are used in this article to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method of this application and its core idea; at the same time, for technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.

[0117] It should be noted that when the above embodiments of this application are applied to specific products or technologies, the relevant user data is involved, and the user's permission or consent must be obtained, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

Claims

1. A method for screening high-altitude and steep slope stress-resistant excellent plant materials, characterized in that: The method for screening high-altitude and steep slope stress-resistant excellent plant materials comprises: Acquire multiple survey plots within a target high and steep slope area, wherein multiple categories of plants to be screened grow within the survey plots; Obtaining plant growth images obtained by taking photos of the survey plot area by a drone; Performing plant recognition on the plant growth image to obtain each plant recognition area on the plant growth image and a corresponding first predicted plant category; Determining the regional stress resistance parameters of the plants to be screened within the survey plot based on the plant identification region corresponding to the first predicted plant category of the plants to be screened; Determining the overall stress resistance parameters of the plants to be screened based on the regional stress resistance parameters of the plants to be screened in the plurality of survey plots; The plants to be screened whose overall stress resistance parameters are higher than the preset values ​​are determined as the stress resistance plants in the target steep slope area.

2. The method for screening high-altitude cold steep slope stress-resistant excellent plant materials according to claim 1, characterized in that, The method of determining the regional stress resistance parameters of the plants to be screened within the survey plot area based on the plant identification region corresponding to the first predicted plant category of the plants to be screened comprises: Obtaining the total area of ​​the plant identification region corresponding to the first predicted plant category of the plants to be screened; The regional stress resistance parameters of the plants to be screened within the survey plot area are determined based on the total area of ​​the plant identification area corresponding to the first predicted plant category of the plants to be screened, wherein the larger the total area, the larger the regional stress resistance parameters.

3. The method for screening high-altitude cold steep slope stress-resistant excellent plant materials according to claim 1, characterized in that, The method for screening high-altitude and steep slope stress-resistant excellent plant materials comprises: Obtain a three-dimensional topographic map of the target steep slope area; Generating multiple candidate areas on the three-dimensional topographic map multiple times, and treating the multiple candidate areas generated each time as a region set to obtain multiple region sets; Determining the regional feature uniformity of the region set based on the regional information of the plurality of candidate regions in the region set, to obtain the regional feature uniformity of the plurality of region sets, wherein the regional information includes the regional altitude and the regional slope; The plurality of candidate regions in the region set with the highest regional feature uniformity among the plurality of region sets are determined as the plurality of survey plot regions.

4. The method for screening high-altitude cold steep slope stress-resistant excellent plant materials according to claim 3, characterized in that, The determining the uniformity of regional features of the region set based on the region information of the plurality of candidate regions in the region set includes: Calculating the regional similarity between the candidate region and other candidate regions in the region set based on the region information of the plurality of candidate regions in the region set, and obtaining a plurality of region similarities corresponding to the candidate region; Sorting the multiple region similarities corresponding to the candidate region from large to small and calculating the similarity difference between any two adjacent region similarities after sorting to obtain multiple similarity differences corresponding to the candidate region; Determine the coefficient of variation of multiple similarity differences corresponding to the candidate regions as a uniformity quantization value of the candidate regions, and obtain a quantitative average of the uniformity quantization values ​​of the multiple candidate regions; The regional feature uniformity of the region set is determined based on the quantized average value, wherein the higher the quantized average value is, the smaller the regional feature uniformity is.

5. The method for screening high-altitude cold steep slope stress-resistant excellent plant materials according to claim 4, characterized in that, The method for screening high-altitude and steep slope stress-resistant excellent plant materials comprises: Obtaining a plurality of soil improvement formulas and a plurality of plant seed formulas, wherein the soil improvement formulas include dosages of a plurality of soil materials, and the plant seed formulas include dosages of a plurality of categories of plants to be screened; Allocating a soil improvement formula and a plant seed formula to each survey plot area to obtain candidate seed soil area allocation information, and after multiple allocations, obtaining multiple candidate seed soil area allocation information, wherein the candidate seed soil area allocation information includes multiple seed soil area mapping information, and the seed soil area mapping information includes a soil improvement formula and a plant seed formula corresponding to each survey plot area; determining a distribution uniformity of the candidate seed soil region distribution information based on the seed soil region mapping information in the candidate seed soil region distribution information; determining the candidate seed soil area allocation information with the highest distribution uniformity among the plurality of candidate seed soil area allocation information as the target seed soil area allocation information; Based on the target seed soil area distribution information, multiple categories of plants to be screened are planted in multiple survey plots.

6. The method for screening high-altitude cold steep slope stress-resistant excellent plant materials according to claim 5, characterized in that, The method of planting multiple categories of plants to be screened in multiple survey plots based on target seed soil area distribution information includes: Obtaining location information of a plurality of survey plot areas; Planning an action route based on location information of the plurality of survey plot areas; The drone is controlled to arrive at each of the survey plot areas based on the action route, and when in the survey plot area, the drone is controlled to release the soil with the soil improvement formula corresponding to the survey plot area and the plant seeds corresponding to the plant seed formula corresponding to the survey plot area in the target seed soil area allocation information in the survey plot area.

7. A device for screening high-altitude and steep slope stress-resistant plant materials, characterized in that: The device for screening high-altitude and steep slope stress-resistant excellent plant materials comprises: A first acquisition module is used to acquire a plurality of survey plots within a target high and steep slope area, wherein a plurality of categories of plants to be screened grow in the survey plots; The second acquisition module is used to obtain plant growth images obtained by taking photos of the survey sample area by a drone; a plant identification module, configured to perform plant identification on the plant growth image, and obtain each plant identification region on the plant growth image and a corresponding first predicted plant category; A first determination module is used to determine the regional stress resistance parameters of the plants to be screened within the survey plot based on the plant identification region corresponding to the first predicted plant category of the plants to be screened; The second determination module is used to determine the overall stress resistance parameters of the plants to be screened based on the regional stress resistance parameters of the plants to be screened in the plurality of survey plots; The third determination module is used to determine the plants to be screened whose overall stress resistance parameters are higher than preset values ​​as the stress resistance plants in the target steep slope area.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the steps of the method for screening high-altitude, cold and steep slope stress-resistant excellent plant materials according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of instructions, which are suitable for loading by a processor to execute the steps in the method for screening high-altitude, cold, steep slope and excellent stress-resistant plant materials according to any one of claims 1 to 6.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method for screening high-altitude, cold and steep slope stress-resistant excellent plant materials according to any one of claims 1 to 6 are implemented.

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