A method for fine irrigation of orchards in hilly areas

Abstract

The present invention belongs to the field of watering control during planting, and is intended to solve the problem of fine irrigation of hilly and mountainous orchards. A method for fine irrigation of hilly and mountainous orchards is disclosed, including: monitoring to obtain a low-resolution soil moisture distribution map; drones collect orchard DOM and DSM; identify ground points from DOM and correspond to DSM and interpolate to generate DEM; extract single fruit tree codes from DOM and generate fruit tree area vector files; obtain soil monitoring result maps around single fruit trees with DEM, low-resolution soil moisture distribution maps, and fruit tree area vector files; correspond fruit tree codes to solenoid valves of irrigation devices; generate fine irrigation prescription maps with drought monitoring result maps to control solenoid valve irrigation. The present invention also discloses a method for monitoring drought in hilly and mountainous orchards, a computer-readable storage medium storing computer programs for executing the corresponding steps of the above two methods, and its application. The present invention realizes fine irrigation of hilly and mountainous orchards down to individual trees.

Description

Technical Field

[0001] The invention belongs to the field of watering control during planting, and in particular relates to a fine irrigation method for a hilly orchard. Background Art

[0002] According to the geographical environment of the orchard, the orchard can be generally divided into hilly orchards, general flat orchards, sandy wasteland and saline-alkali land orchards, etc. Most orchards are built on hilly areas, mainly because of the following advantages of hilly areas: 1. The temperature difference between day and night is large, and the photosynthesis of fruit tree leaves is strong during the day; 2. There is sufficient light and good ventilation conditions, and the occurrence of diseases and insect pests is relatively light; 3. Good drainage is conducive to increasing the air content in the soil, raising the soil temperature, making the aerobic microorganisms in the soil active, and the organic matter in the soil is easy to decompose, which increases the nutrients absorbed by the fruit trees.

[0003] However, the terrain in mountainous and hilly areas is complex and the slope is large, which causes large regional variations in soil moisture content. The current fruit tree irrigation methods commonly used in mountainous and hilly areas lack a deep understanding of the changes in slope moisture content, and are also unable to provide fine irrigation at the level of individual trees. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to achieve high-precision soil moisture estimation in complex terrain by means of the high maneuverability of drones and a limited number of ground sensors, and achieve fine irrigation of fruit trees accurate to a single tree by means of a fine irrigation null value device.

[0005] The present invention first provides a method for fine irrigation of orchards in hilly and mountainous areas, the method comprising the following steps:

[0006] S1, receiving continuously monitored soil moisture data from ground soil sensors arranged at different fixed points in hilly orchards, and interpolating the soil moisture data to obtain a low-resolution soil moisture distribution map;

[0007] S2, receiving images of the orchard transmitted from the drone, and performing stitching processing on the images to obtain a high-precision visible light orthophoto and digital surface model of the orchard;

[0008] S3, obtaining ground points identified from the visible light orthophoto by a visual discrimination method, marking them as ground point vector files, assigning corresponding elevation data in the digital surface model to the ground point vector files, and using interpolation to generate a digital elevation model from the ground point vector files as accurate elevation data of the orchard;

[0009] S4, extracting individual fruit trees from the high-precision visible light orthophoto through image processing, encoding each fruit tree and generating a fruit tree region vector file;

[0010] S5, using the digital elevation model to calculate the micro-topography information of the orchard, combining the low-resolution soil moisture distribution map, performing raster calculation, and generating a high-resolution soil moisture distribution map based on terrain factors; using the high-resolution soil moisture distribution map in combination with the fruit tree area vector file to perform zoning statistics on the soil moisture content around each fruit tree, and reclassifying the results of the zoning statistics according to the soil drought grade classification standard to obtain a drought monitoring result map;

[0011] S6, matching the codes of the fruit trees with the numbers of the solenoid valves of the precision irrigation devices one by one, and importing the corresponding relationship into the precision irrigation control center;

[0012] S7, generating a fine irrigation prescription map based on the drought monitoring result map and the water demand of the fruit trees;

[0013] S8. Upload the fine irrigation prescription map to the fine irrigation control center to control the solenoid valve to perform fine irrigation down to the individual plant level.

[0014] In the above irrigation method, the fruit trees may be citrus trees, apple trees, pear trees and other fruit trees commonly cultivated on hills.

[0015] In the above irrigation method, the soil moisture data of the fixed points in the orchard in step S1 are the spatiotemporal variation characteristics of the soil moisture content that are continuously monitored; the fixed points in the orchard are evenly distributed inside the orchard, and are selected according to the classification of slope, slope direction, and slope position; each fixed point is measured in layers and the average value is taken as the average soil moisture content of the fixed point; the classification standard of the slope is shown in Table 1, the classification standard of the slope direction is shown in Table 2, and the classification standard of the slope position is shown in Table 3;

[0016] Table 1. Slope classification standards

[0017]

[0018] Table 2. Slope classification standards

[0019]

[0020] Table 3. Slope classification standards

[0021] .

[0022] In the above irrigation method, the interpolation adopts the inverse distance weighted interpolation method, the distance from the interpolation point to the discrete point is calculated using formula 2, the weight of each discrete point is calculated using formula 3, and the value corresponding to the interpolation point is calculated using formula 4:

[0023] Formula 2

[0024] Formula 3

[0025] Formula 4

[0026] Where: d i is the distance between the interpolation point and the ith discrete point; (x, y) is the coordinate of the interpolation point; (x i ,y i ) is the coordinate of the ith discrete point; w i is the weight of the ith discrete point, is the attribute value of the i-th discrete point; n represents the number of discrete points; Z0 is the attribute value of the interpolation point.

[0027] In the above irrigation method, S4 comprises the following steps:

[0028] S4-1. Calculate the green leaf vegetation index GLI using the high-precision visible light orthophoto, and use formula 5 to calculate the blue, green and red three-band data to obtain the GLI grayscale image:

[0029] GLI = ((G - R) + (G - B)) / ((2 × G) + R + B)Formula 5

[0030] In the formula: R, G, B represent the pixel values ​​of the red, green, and blue bands of the high-precision orthophoto;

[0031] S4-2, using the maximum inter-class variance method to obtain the optimal binarization threshold of the GLI grayscale image, and using the threshold to mask the grayscale image, masking out the soil background factor, and obtaining the masked image for single fruit tree extraction. The mask calculation adopts Formula 6:

[0032] Formula 6

[0033] in is the pixel value of the masked image, b is the pixel value of the original image, and t is the optimal binarization threshold calculated according to the maximum inter-class variance method;

[0034] S4-3. Measure the numerical range of the crown diameter within the range of the masked image, input the maximum value, minimum value, increment number, fruit tree overlap, and threshold parameter of the crown diameter of the fruit trees in the orchard, traverse the qualified areas in the masked image and mark the positions of the fruit trees, encode the fruit trees, and generate a fruit tree area vector file, wherein the fruit tree area vector file includes the center coordinates of the approximate circle of the fruit tree crown and the diameter of the approximate circle.

[0035] In the above irrigation method, generating a high-resolution soil moisture distribution map based on terrain factors in S5 comprises the following steps:

[0036] S5-1, using the digital elevation model to generate a high-resolution slope map, a high-resolution aspect map and a high-resolution slope position map;

[0037] S5-2, reclassifying the high-resolution slope position map according to the classification standard in Table 1, and assigning values ​​to each type of grid according to the coefficient k1, combining the ground soil moisture interpolation data W0 generated in step S1, using a grid calculator to perform grid calculation, and generating a high-resolution soil moisture distribution map W1 based on the slope position, the calculation formula is Formula 7;

[0038] S5-3, reclassifying the high-resolution aspect map according to the classification standard in Table 2, and assigning a value to each type of grid according to the coefficient k2, combining the ground soil moisture distribution map W1 generated in S5-2, using a grid calculator to perform grid calculation, and generating a high-resolution soil moisture distribution map W2 based on the slope position, using Formula 8;

[0039] S5-4, reclassifying the high-resolution slope map according to the classification standard in Table 3, and assigning a value to each type of grid according to the coefficient k3, combining the ground soil moisture distribution map W2 generated in S5-3, using a grid calculator to perform weighted calculation, and generating a high-resolution soil moisture distribution map W3 based on the slope, using Formula 9;

[0040] W1=SP×W0 Formula 7

[0041] W2=A×W1 Formula 8

[0042] W3=S×W2 Formula 9

[0043] Wherein, W0 represents the interpolated data of ground soil moisture generated in step S1, W1 represents the high-precision soil moisture distribution map based on slope position, W2 represents the high-precision soil moisture distribution map based on slope aspect, and W3 represents the high-precision soil moisture distribution map based on slope gradient; S represents the slope gradient map reclassified according to the classification standard in Table 3 and assigned with corresponding coefficients, A represents the slope aspect map reclassified according to the classification standard in Table 2 and assigned with corresponding coefficients, and SP represents the slope position map reclassified according to the classification standard in Table 1 and assigned with corresponding coefficients.

[0044] In the above irrigation method, the soil drought level classification standard is shown in Table 7:

[0045] Table 7. Drought level classification standards

[0046] .

[0047] The present invention also provides a method for monitoring drought in a hilly orchard, which comprises the above steps S1 to S5.

[0048] The present invention also provides a fine irrigation system for hilly orchards, comprising a plurality of soil sensors, a fine irrigation device, a fine irrigation control center, and an industrial computer, wherein the plurality of soil sensors are arranged at different fixed points in the hilly orchard; the fine irrigation device comprises a drip irrigation system and a solenoid valve; the fine irrigation control center is connected to the solenoid valve via wired or wireless communication;

[0049] The industrial computer includes a processor and a memory storing a computer program, and the processor implements steps S1-S7 in the above irrigation method when executing the computer program;

[0050] The fine irrigation control center receives the correspondence between the fruit tree code and the solenoid valve number of the fine irrigation device and the fine irrigation prescription map uploaded from the industrial computer, and controls the solenoid valve to perform fine irrigation down to a single tree.

[0051] The above-mentioned precision irrigation system also includes multiple drones for capturing images of hilly mountain orchards.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention uses the high maneuverability of drones and a limited number of ground sensors to achieve high-precision soil moisture estimation in complex terrain, and adopts a zoned irrigation mode. Each area sets the operating time separately according to the water replenishment prescription map to ensure that the fruit trees in each area get the required water, thus achieving precise control of fruit tree water replenishment, while saving water and reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of Example 1 of the present invention.

[0055] Figure 2 This is a flow chart of stitching images collected by a drone in Example 1 of the present invention.

[0056] Figure 3 This is a flow chart of extracting citrus plants and encoding each citrus plant by image processing using high-precision orthophotos in Example 1 of the present invention.

[0057] Figure 4 This is an example of extracting individual citrus plants using high-precision RGB orthophotos through image processing in Example 1 of the present invention.

[0058] Figure 5 This is the statistical range based on the zoning statistics of the average relative humidity of the soil around a single fruit tree in Example 1 of the present invention. DETAILED DESCRIPTION

[0059] The present invention is further described in detail below in conjunction with specific embodiments, and the examples provided are only for illustrating the present invention, rather than for limiting the scope of the present invention. The examples provided below can be used as a guide for further improvements by those of ordinary skill in the art, and do not constitute a limitation of the present invention in any way.

[0060] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0061] Example 1

[0062] This embodiment uses the citrus orchard in Jingmen City, Hubei Province as the research area, and the flowchart of the method for drought monitoring and fine irrigation in hilly orchards based on drones is as follows: Figure 1 As shown, the following steps are included:

[0063] S1. Use ground soil sensors to obtain soil moisture data at fixed points in the orchard, and interpolate the data to obtain a low-resolution soil moisture distribution map;

[0064] Specifically:

[0065] TDR (Time domain reflectometry, an application of radar detection technology) was used to continuously monitor the spatiotemporal variation characteristics of soil volumetric water content at different fixed points; the fixed points were evenly distributed inside the orchard and were selected according to the classification of slope (classification see Table 1), slope direction (classification see Table 2), and slope position (classification see Table 3), and three points were selected in each category for data collection.

[0066] The classification of the slope is shown in Table 1, the classification of the slope direction is shown in Table 2, and the classification of the slope position is shown in Table 3;

[0067] Table 1. Slope classification standards

[0068]

[0069] Table 2. Slope classification standards

[0070]

[0071] Table 3. Slope classification standards

[0072]

[0073] Each fixed point is divided into 5 soil layers for measurement, and the depth of each soil layer is 0cm (inclusive) to 10cm (exclusive), 10cm (inclusive) to 20cm (exclusive), 20cm (inclusive) to 30cm (exclusive), 30cm (inclusive) to 40cm (exclusive), 40cm (inclusive) to 50cm (inclusive). The soil moisture content data of the 5 soil layers at the point are obtained, and the average soil moisture content of the 5 soil layers at each position is taken as the average soil moisture content (soil volume moisture content) of the point, and the soil relative humidity is calculated; the soil relative humidity calculation formula is as follows:

[0074] Formula 1

[0075] Where: W represents soil relative humidity; θ represents soil water content; F c Indicates the field water holding capacity of the soil.

[0076] The relative soil humidity of the soil at fixed points in the citrus orchard is obtained by using ground soil sensors for interpolation to obtain a low-resolution relative soil humidity distribution map of the citrus orchard. The interpolation method uses the inverse distance weighted interpolation method (IDW), and the calculation formula is as follows:

[0077] The distance from the interpolation point to the discrete point is calculated using formula 2:

[0078] Formula 2

[0079] Where: d i is the distance between the interpolation point and the ith discrete point; (x, y) is the coordinate of the interpolation point; (x i ,y i ) is the coordinate of the ith discrete point.

[0080] The weight of each discrete point is calculated using formula 3:

[0081] Formula 3

[0082] Where: d i is the distance between the interpolation point and the ith discrete point; w i is the weight of the ith discrete point.

[0083] The value corresponding to the interpolation point is calculated using formula 4:

[0084] Formula 4

[0085] Where: w i is the weight of the i-th discrete point; is the attribute value of the i-th discrete point; n represents the number of discrete points; Z0 is the attribute value of the interpolation point.

[0086] S2. Use drones to collect images of the orchard and stitch the images to obtain high-precision visible light orthophotos (DOM) and digital surface models (DSM) of the orchard.

[0087] Specifically:

[0088] The image acquisition of the citrus orchard was carried out by using a drone equipped with a visible light sensor and a route planning method. The flight altitude was 100m, and the heading and lateral overlap was 75%. After the image acquisition was completed, the image was spliced ​​using Pix4Dmapper drone image splicing software. After splicing, the high-precision visible light orthophoto (DOM) and digital surface model (DSM) of the citrus orchard were obtained. The specific splicing process is shown in Figure 2 .

[0089] S3, identifying ground points from the visible light orthophoto by visual discrimination, marking them as ground point vector files, assigning corresponding elevation data in the digital surface model to the ground point vector files, and using interpolation to generate a digital elevation model (DEM) from the ground point vector files as accurate elevation data of the orchard;

[0090] Specifically:

[0091] Using the visible light orthophoto (DOM) and digital surface model (DSM) generated by stitching drone images in S2, the ground points were identified from the visible light orthophoto by visual discrimination, and the ground points were marked as point vector (SHP) files in ARCGIS software. Then, the multi-value extraction to point tool was used to assign the corresponding elevation data in the DSM to the ground point vector file, and the IDW interpolation method (calculated by Formula 2, Formula 3 and Formula 4) was used to generate a digital elevation model (DEM) from the ground point vector file, and the DEM data was used as the precise elevation data of the citrus orchard.

[0092] S4. Utilize the high-precision visible light orthophoto (DOM) to extract individual fruit trees through image processing, encode each fruit tree, and generate a fruit tree region vector file.

[0093] The flowchart of step S4 is shown in Figure 3 , as follows:

[0094] S4-1. Calculate the Green Leaf Index (GLI) using the high-precision visible light orthophoto (DOM). The GLI is calculated by performing band calculation on the blue, green and red band data of the visible light orthophoto (DOM). The index calculation formula adopts Formula 5:

[0095] GLI = ((G - R) + (G - B)) / ((2 × G) + R + B)Formula 5

[0096] Where: R, G, B represent the pixel values ​​of the red, green, and blue bands of the high-precision orthophoto.

[0097] S4-2. Using the GLI grayscale image calculated in S4-1, the maximum between-class variance method (OTSU) is used to obtain the optimal binarization threshold of the grayscale image, and the threshold is used to mask the grayscale image to mask out the soil background factors to facilitate the extraction of individual citrus trees. The mask calculation uses formula 6:

[0098] Formula 6

[0099] in is the pixel value of the masked image, b is the pixel value of the original image, and t is the optimal binarization threshold calculated according to the maximum between-class variance method (OTSU). This formula can be used to mask the soil background. After calculation, the pixel value of the soil background is 0, and the pixel value of the vegetation area remains unchanged.

[0100] S4-3. Use ARCGIS measurement tools to measure the numerical range of tree crown diameters within the masked image range. Use ENVI software Crop Science tool to input the maximum value, minimum value, number of increments (sampling interval), fruit tree overlap, and threshold parameters of the crown diameters of fruit trees in the orchard. The software traverses the areas that meet the conditions in the masked image and marks the positions of fruit trees, encodes the fruit trees, and generates a fruit tree area vector file. The fruit tree area vector file contains the center coordinates of the approximate circle of the fruit tree canopy and the diameter of the approximate circle.

[0101] S5. Calculate the micro-topography information of the orchard using the digital elevation model (DEM), combine it with the low-resolution soil moisture distribution map, perform raster calculation, and generate a high-resolution soil moisture distribution map based on terrain factors; perform zoning statistics on the soil moisture content around each fruit tree using the high-resolution soil moisture distribution map combined with the fruit tree area vector file, and reclassify the results of the zoning statistics according to the soil drought grade classification standard to obtain a drought monitoring result map;

[0102] The details are as follows:

[0103] The digital elevation model is used to calculate the micro-topography information of the citrus orchard, including the slope map, the aspect map, and the slope position map. The low-resolution soil moisture distribution map generated in step S1 is combined with the grid calculator to perform grid calculation to generate a high-resolution soil relative humidity distribution map based on terrain factors. The specific steps are as follows:

[0104] S5-1. Using the high-resolution DEM image generated in S3, use ARCGIS software to generate high-resolution slope, aspect and slope position maps.

[0105] S5-2, using the generated high-resolution slope map, reclassify the slope map according to the classification rules in Table 1, and assign values ​​to each type of grid according to the coefficient k1, combine the ground soil relative humidity interpolation data W0 generated in step S1, use the grid calculator to perform grid calculation, and generate a high-resolution soil relative humidity distribution map W1 based on the slope position. The calculation formula is Formula 7:

[0106] W1=SP×W0 Formula 7

[0107] S5-3, using the generated high-resolution aspect map, reclassify the aspect map according to the classification rules in Table 2, and assign values ​​to each type of grid according to the coefficient k2. Combined with the ground soil relative humidity distribution map W1 generated in S5-2, use the grid calculator to perform grid calculations to generate a high-resolution soil relative humidity distribution map W2 based on the slope position, using formula 8:

[0108] W2=A×W1 Formula 8

[0109] S5-4, using the generated high-resolution slope map, reclassify the slope map according to the classification rules in Table 3, and assign values ​​to each type of grid according to the coefficient k3, combined with the ground soil relative humidity distribution map W2 generated in S5-3, use the grid calculator to perform weighted calculation, and generate a high-resolution soil relative humidity distribution map W3 based on the slope, using formula 9:

[0110] W3=S×W2 Formula 9

[0111] In Formula 7, Formula 8 and Formula 9: W0 represents the interpolation data of the relative humidity of the ground soil generated in step S1, W1 represents the high-precision relative humidity distribution map of the soil based on the slope position, W2 represents the high-precision relative humidity distribution map of the soil based on the slope direction, W3 represents the high-precision relative humidity distribution map of the soil based on the slope, S represents the slope map after reclassification according to the classification standard in Table 3 and assignment of corresponding coefficients, A represents the slope direction map after reclassification according to the classification standard in Table 2 and assignment of corresponding coefficients, and SP represents the slope position map after reclassification according to the classification standard in Table 1 and assignment of corresponding coefficients.

[0112] In order to determine the changing rules of soil relative moisture content under different slopes, slope directions and slope positions, the soil relative moisture content change data of multiple points continuously monitored by TDR in S1 were used to perform statistical analysis according to different slopes, slope directions and slope positions. The flat slope, semi-yin-yang slope and middle slope were used as the benchmarks, and the ratio of each category to the benchmark was calculated as the assignment coefficient of soil relative moisture changing with terrain.

[0113] The statistical results of the various assignment coefficients of this embodiment are shown in Table 4, Table 5 and Table 6 (the coefficients in Tables 4-6 are the coefficients of this embodiment, and other orchards need to be calculated using the measured values ​​of ground sensors):

[0114] Table 4. Slope classification and coefficient

[0115]

[0116] Table 5. Slope classification and coefficient

[0117]

[0118] Table 6. Slope classification and coefficient

[0119]

[0120] S5-5. To achieve fine irrigation of individual citrus trees, the fruit tree vector file generated by S4-3 was used to perform zoning statistics on the high-resolution soil relative humidity based on terrain factors generated by S5-4. Since the horizontal lateral roots of fruit trees generally exceed the extension range of the crown by 1-3 times, in order to ensure the accuracy of the relative soil humidity around each fruit tree, the diameter of the approximate circle of the fruit tree in S4-3 was expanded by 2 times as the influence range of the water absorption of the fruit tree. When performing zoning statistics, the average relative soil humidity within the influence range was taken as the evaluation index of whether the fruit tree was drought-prone. The expansion of the approximate circle diameter was completed using the vector attribute calculation tool of the ARCGIS software, and the zoning statistics were completed using the zoning statistics tool of the ARCGIS software. The results are as follows: Figure 5 shown.

[0121] S5-6. Reclassify the results of the zoning statistics according to the soil drought grade classification standard to obtain a drought monitoring result map; the drought grade classification standard is shown in Table 7:

[0122] Table 7. Drought level classification standards

[0123]

[0124] S6. Match the fruit tree codes with the solenoid valve numbers of the precision irrigation devices one by one, and import the corresponding relationship into the background precision irrigation control center.

[0125] S7. Generate a fine irrigation prescription map based on the drought monitoring result map and the water demand of the fruit trees, and upload it to the fine irrigation control center to control the solenoid valve to perform fine irrigation down to the individual tree.

[0126] The details are as follows:

[0127] The drought monitoring result map is combined with the water requirement of the fruit trees to generate a fine irrigation prescription map. According to the empirical analysis of the water requirement of the fruit trees and the irrigation amount, irrigation is generally required when the relative humidity of the soil is lower than 50%.

[0128] The irrigation prescription map is uploaded to the fine irrigation control platform, and the electromagnetic valves in the corresponding areas are opened for irrigation according to the fruit trees that need to be irrigated in the prescription map. For the irrigation amount requirement of fruit trees, the relative soil humidity generally needs to reach 60-80%, otherwise it cannot meet the needs of fruit tree production and development. This value can be actually determined according to the specific fruit tree type and soil type. In this embodiment, the relative soil humidity threshold for irrigation is set to 70% according to the actual situation on site.

[0129] The soil irrigation time and the rate of change of soil relative humidity are related to the water flow of the solenoid valve and the soil type. For the test data of this embodiment, for soils of different drought levels, the TDR soil moisture real-time monitoring method is used to determine the required irrigation time, that is, when the fifth layer (40-50cm) sensor monitors the soil relative humidity data in real time to reach 70%, it is considered that the irrigation time meets the requirement. After multiple tests, the maximum irrigation time is taken as the irrigation time required for the drought level. The irrigation time corresponding to different drought levels is shown in Table 8.

[0130] Table 8. Irrigation time corresponding to different drought levels

[0131]

[0132] The present invention has been described in detail above. It will be apparent to those skilled in the art that the present invention may be implemented in a wide range under equivalent parameters, concentrations and conditions without departing from the spirit and scope of the present invention and without the need for unnecessary experimentation. Although the present invention provides specific embodiments, it should be understood that further improvements may be made to the present invention. In short, according to the principles of the present invention, this application intends to include any changes, uses or improvements to the present invention, including changes made by conventional techniques known in the art that depart from the scope disclosed in this application. Applications of some of the basic features may be made within the scope of the following appended claims.

Claims

1. A method for fine irrigation of orchards in hilly and mountainous areas, characterized by: The irrigation method comprises the following steps: S1, receiving continuously monitored soil moisture data from ground soil sensors arranged at different fixed points in hilly orchards, and interpolating the soil moisture data to obtain a low-resolution soil moisture distribution map; S2, receiving images of the orchard transmitted from the drone, and performing stitching processing on the images to obtain a high-precision visible light orthophoto and digital surface model of the orchard; S3, obtaining ground points identified from the visible light orthophoto by a visual discrimination method, marking them as ground point vector files, assigning corresponding elevation data in the digital surface model to the ground point vector files, and using interpolation to generate a digital elevation model from the ground point vector files as accurate elevation data of the orchard; S4, performing image processing on the high-precision visible light orthophoto to extract individual fruit trees, encode each fruit tree and generate a fruit tree area vector file; S5, using the digital elevation model to calculate the micro-topography information of the orchard, combining the low-resolution soil moisture distribution map, performing raster calculation, and generating a high-resolution soil moisture distribution map based on terrain factors; using the high-resolution soil moisture distribution map in combination with the fruit tree area vector file to perform zoning statistics on the soil moisture content around each fruit tree, and reclassifying the results of the zoning statistics according to the soil drought grade classification standard to obtain a drought monitoring result map; S6, matching the codes of the fruit trees with the numbers of the solenoid valves of the precision irrigation devices one by one, and importing the corresponding relationship into the precision irrigation control center; S7, generating a fine irrigation prescription map by combining the drought monitoring result map with the water demand of the fruit trees; S8, uploading the fine irrigation prescription map to the fine irrigation control center to control the solenoid valve to perform fine irrigation down to the individual plant; The soil moisture data of the fixed points in the orchard in step S1 are the spatiotemporal variation characteristics of the soil moisture content that are continuously monitored; the fixed points in the orchard are evenly distributed inside the orchard, and are selected according to the classification of slope, slope direction, and slope position; each fixed point is measured in layers and the average value is taken as the average soil moisture content of the fixed point; Slope classification standards: Ridge: within the watershed and the flat land on both sides; Upslope: The uppermost part of the slope divided into three equal parts from below the ridge to the valley; Middle slope: the middle part of the slope divided into three equal parts from below the ridge to the valley; Downslope: The lower third of the slope from below the ridge to the valley range; Valley: the valley on both sides of the waterline; Slope classification standards: 0°≤shady slope<45°, 45°≤semi-shady slope<135°, sunny slope, 225°≤semi-sunny slope<315°, 315°≤shady slope<360°; Slope classification standard: 0°≤flat slope≤5°, 6°≤gentle slope≤15°, 16°≤slope≤25°, 26°≤steep slope≤35°, 36°≤precipitous slope≤45°, dangerous slope>45°; The S4 comprises the following steps: S4-1. Calculate the green leaf vegetation index GLI using the high-precision visible light orthophoto, and use formula 5 to calculate the blue, green and red three-band data to obtain the GLI grayscale image: GLI = ((G - R) + (G - B)) / ((2 × G) + R + B) Formula 5 In the formula: R, G, and B represent the pixel values ​​of the red, green, and blue bands of the high-precision orthophoto respectively; S4-2, using the maximum inter-class variance method to obtain the optimal binarization threshold of the GLI grayscale image, and using the optimal binarization threshold to mask the grayscale image, masking out the soil background factor, and obtaining the masked image for single fruit tree extraction. The mask calculation adopts Formula 6: Where f(b) is the pixel value of the masked image, b is the pixel value of the original image, and t is the optimal binarization threshold calculated according to the maximum inter-class variance method; S4-3, measuring the numerical range of the crown diameter within the range of the masked image, inputting the maximum value, minimum value, increment number, fruit tree overlap, and threshold parameter of the crown diameter of the fruit trees in the orchard, encoding the fruit trees by traversing the qualified areas in the masked image and marking the positions of the fruit trees, and generating a fruit tree area vector file, wherein the fruit tree area vector file includes the center coordinates of the approximate circle of the fruit tree crown layer and the diameter of the approximate circle; S5 generating a high-resolution soil moisture distribution map based on terrain factors comprises the following steps: S5-1, using the digital elevation model to generate a high-resolution slope map, a high-resolution aspect map and a high-resolution slope position map; S5-2, reclassifying the high-resolution slope position map according to the slope position classification standard, and assigning values ​​to each type of grid according to the coefficient k1, combining the ground soil moisture interpolation data W0 generated in step S1, using a grid calculator to perform grid calculation, and generating a high-resolution soil moisture distribution map W1 based on the slope position, the calculation formula is formula 7; S5-3, reclassifying the high-resolution aspect map according to the aspect classification standard, assigning values ​​to each type of grid according to the coefficient k2, combining the high-resolution soil moisture distribution map W1 based on the slope position generated in S5-2, and performing grid calculation using a grid calculator to generate a high-resolution soil moisture distribution map W2 based on the slope aspect, and the calculation formula is Formula 8; S5-4, reclassifying the high-resolution slope map according to the slope classification standard, and assigning a value to each type of grid according to the coefficient k3, combining the high-resolution soil moisture distribution map W2 based on the slope aspect generated in S5-3, and using a grid calculator to perform weighted calculation to generate a high-resolution soil moisture distribution map W3 based on the slope, and the calculation formula is Formula 9; W1=SP×W0 Formula 7 W2=A×W1 Formula 8 W3=S×W2 Formula 9 Wherein, W0 represents the interpolated data of ground soil moisture generated in step S1, W1 represents the high-resolution soil moisture distribution map based on slope position, W2 represents the high-resolution soil moisture distribution map based on slope aspect, and W3 represents the high-resolution soil moisture distribution map based on slope gradient; S represents the slope map reclassified according to the slope classification standard and assigned with corresponding coefficients, A represents the aspect map reclassified according to the aspect classification standard and assigned with corresponding coefficients, and SP represents the slope position map reclassified according to the slope position classification standard and assigned with corresponding coefficients.

2. A method for monitoring drought in a hilly orchard, characterized in that: The method comprises steps S1 to S5 of claim 1.

3. A fine irrigation system for hilly orchards, characterized by: It includes multiple soil sensors, a fine irrigation device, a fine irrigation control center, and an industrial computer, wherein the multiple soil sensors are arranged at different fixed points in a hilly orchard; the fine irrigation device includes a drip irrigation system and a solenoid valve; the fine irrigation control center is connected to the solenoid valve via wired or wireless communication; The industrial computer comprises a processor and a memory storing a computer program, and when the processor executes the computer program, steps S1 to S7 in the method according to claim 1 are implemented; The fine irrigation control center receives the correspondence between the fruit tree code and the solenoid valve number of the fine irrigation device and the fine irrigation prescription map uploaded from the industrial computer, and controls the solenoid valve to perform fine irrigation down to a single tree.

4. The fine irrigation system according to claim 3, characterized in that: It also includes multiple drones for capturing images of the hilly mountain orchards.

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