Vegetation recovery planning method for soil index analysis of construction disturbance area in red soil hilly area

Abstract

The invention discloses a vegetation restoration planning method for soil index analysis of a construction disturbance area of a red soil hilly area, and the method comprises the steps: obtaining the growth habits and nutritional requirements of suitable dominant plant species in the construction disturbance area of the red soil hilly area, and determining the key soil nutrient indexes of the suitable dominant plant species; obtaining a soil nutrient threshold range corresponding to the key soil nutrient index of the suitable dominant plant species; determining a soil index interval suitable for plant growth based on the soil nutrient threshold range; monitoring soil nutrients in the construction disturbance area of the red soil hilly area, and adjusting a soil index interval according to a plant growth feedback result; and carrying out vegetation restoration on the soil of the construction disturbance area of the red soil hilly area based on the adjusted soil index interval. By determining key soil nutrient indexes of plants, soil nutrients are monitored for a long time, soil index intervals are adjusted in time according to plant growth feedback, and it is guaranteed that vegetation recovery work is continuously and effectively carried out.

Description

Technical Field

[0001] The invention relates to the technical field of vegetation restoration, in particular to a vegetation restoration planning method for analyzing soil indicators in a construction disturbance area in a red soil hilly area. Background Art

[0002] The climate of Jiangxi Red Soil Hilly Area belongs to the subtropical monsoon climate, which is warm and humid with abundant precipitation. The terrain is mainly hilly with gentle undulations. In terms of soil, red soil is widely distributed, and its soil is heavy, highly acidic, and has relatively low fertility. During the construction of power transmission and transformation projects in Jiangxi Red Soil Hilly Area, on the one hand, construction disturbance may affect the original soil structure and texture, leading to problems such as decreased soil fertility and increased risk of soil erosion. On the other hand, due to the particularity of power transmission and transformation projects, it is also necessary to ensure the safe and stable operation of related facilities during the construction process, and at the same time, it is inevitable. Causing ecological environmental damage; therefore, vegetation restoration after construction is completed has become an urgent problem to be solved; in the existing technology, the fertility of the soil is usually increased blindly during the vegetation restoration process, and the analysis of soil indicators is often ignored, especially the lack of analysis of key soil nutrient indicators of suitable dominant plant species, and the soil indicator range of key soil nutrient indicators is not clear enough or even inaccurate, resulting in some soil nutrient indicators being too high, making the overall vegetation restoration effect relatively poor. Therefore, a vegetation restoration planning method for soil indicator analysis in construction disturbance areas in red soil hilly areas is urgently needed to solve the above problems. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems in the above-mentioned technologies to a certain extent. To this end, the purpose of the present invention is to propose a vegetation restoration planning method for soil index analysis in construction disturbance areas in red soil hilly areas, determine key soil nutrient indicators by clarifying the growth habits and nutritional requirements of suitable dominant plant species, determine soil index intervals based on soil nutrient threshold ranges, conduct long-term monitoring of soil nutrients and timely adjust soil index intervals based on plant growth feedback to ensure that vegetation restoration work is carried out continuously and effectively.

[0004] To achieve the above-mentioned purpose, the embodiment of the present invention proposes a vegetation restoration planning method for analyzing soil indicators in construction disturbance areas in red soil hilly areas, comprising: Obtain the growth habits and nutritional requirements of dominant plant species suitable for construction disturbance areas in red soil hilly areas, and determine the key soil nutrient indicators of dominant plant species suitable for construction disturbance areas in red soil hilly areas; Obtain the soil nutrient threshold range corresponding to the key soil nutrient indicators of the dominant plant species; Determining a soil index interval suitable for plant growth based on the soil nutrient threshold range; Monitor soil nutrients in construction-disturbed areas in red soil hilly regions and adjust soil index ranges based on plant growth feedback; Based on the adjusted soil index range, vegetation restoration is carried out on the soil in the construction disturbance area of ​​the red soil hilly area.

[0005] Preferably, the key soil nutrient indicators of the suitable dominant plant species include at least one of macroelements, secondary elements and trace elements; the macroelements include at least one of organic matter content, nitrogen, phosphorus and potassium; the secondary elements include at least one of calcium, magnesium and sulfur; the trace elements include at least one of iron, zinc and copper.

[0006] Preferably, determining a soil index interval suitable for plant growth based on the soil nutrient threshold range includes: A key soil nutrient index of an adaptive dominant plant species is randomly selected as a target soil nutrient index; Performing an intersection operation on the soil nutrient threshold range corresponding to the target soil nutrient index to obtain a target soil index interval corresponding to the target soil nutrient index; Traverse all key soil nutrient indicators of suitable dominant plant species to obtain target soil indicator ranges corresponding to all key soil nutrient indicators; The target soil index range corresponding to all key soil nutrient indicators is taken as the soil index range suitable for plant growth.

[0007] Preferably, soil nutrients in the construction disturbance area of ​​the red soil hilly region are monitored, and the soil index range is adjusted according to the plant growth feedback results, including: Conducting systematic sampling and chemical analysis of soil in construction disturbance areas in the red soil hilly region to determine the actual content data corresponding to key soil nutrient indicators of the dominant plant species; The construction disturbance area in the red soil hilly area is evenly divided into three areas, namely the first monitoring area, the second monitoring area and the third monitoring area; Based on the maximum value of the soil index range suitable for plant growth, the actual content corresponding to the key soil nutrient index of the dominant plant species suitable for growth in the first monitoring area is adjusted to obtain the first target monitoring area; Based on the minimum value of the soil index range suitable for plant growth, the actual content of the key soil nutrient index corresponding to the dominant plant species suitable for growth in the second monitoring area is adjusted to obtain the second target monitoring area; Based on the median value of the soil index range suitable for plant growth, the actual content of the key soil nutrient index corresponding to the dominant plant species in the third monitoring area is adjusted to obtain the third target monitoring area; Planting suitable and advantageous plants in the first target monitoring area, the second target monitoring area and the third target monitoring area respectively; Regularly obtain satellite remote sensing images of the dominant plants suitable for the first target monitoring area, the second target monitoring area, and the third target monitoring area; Inputting the satellite remote sensing image into a pre-trained plant growth state recognition model for recognition, and obtaining growth feedback information of advantageous plants in a first target monitoring area, growth feedback information of advantageous plants in a second target monitoring area, and growth feedback information of advantageous plants in a third target monitoring area; Compare the growth feedback information of the advantageous plants in the first target monitoring area, the growth feedback information of the advantageous plants in the second target monitoring area, and the growth feedback information of the advantageous plants in the third target monitoring area, and adjust the soil index range based on the comparison results; If the growth feedback information of the advantageous plants in the first target monitoring area is consistent with the growth feedback information of the advantageous plants in the third target monitoring area and the growth feedback information of the advantageous plants in the second target monitoring area shows that the plants grow poorly, the minimum value of the soil target area is adjusted; If the growth feedback information of the advantageous plants in the second target monitoring area is consistent with the growth feedback information of the advantageous plants in the third target monitoring area and the growth feedback information of the advantageous plants in the first target monitoring area shows that the plants grow poorly, the maximum value of the soil target area is adjusted.

[0008] Preferably, the soil in the construction disturbance area of ​​the red soil hilly area is systematically sampled and chemically analyzed to determine the actual content data corresponding to the key soil nutrient indicators of the suitable dominant plant species, including: Soil samples were systematically collected at different locations and depths in the construction disturbance area in the red soil hilly region; The Kjeldahl method was used to determine the total nitrogen content in the soil samples through digestion, distillation and titration to obtain the content of nitrogen in the soil samples; The total phosphorus content in the soil sample was determined by the molybdenum antimony colorimetric method to obtain the content of phosphorus in the soil sample; The total potassium content in the soil sample is measured by flame photometry or atomic absorption spectrophotometry to obtain the content value of potassium in the soil sample; The content of organic matter in soil samples was measured by potassium dichromate volumetric method to obtain the content of organic matter in soil samples; The contents of calcium, magnesium, iron, zinc and copper in soil samples were determined by atomic absorption spectrophotometry to obtain the content values ​​of calcium, magnesium, iron, zinc and copper in soil samples; The content of sulfur in the soil sample was determined by barium sulfate turbidimetry to obtain the content of sulfur in the soil sample; The mean of the measured values ​​of the nitrogen content, the phosphorus content, the potassium content, the organic matter content, the calcium content, the magnesium content, the sulfur content, the iron content, the zinc content and the copper content in the soil samples are taken as the actual content data corresponding to the key soil nutrient indicators of the suitable dominant plant species.

[0009] Preferably, before inputting the satellite remote sensing image into a pre-trained plant growth state recognition model for recognition, the method further comprises: The satellite remote sensing image is subjected to denoising to obtain a denoised satellite remote sensing image.

[0010] Preferably, performing noise reduction on the satellite remote sensing image to obtain a noise-reduced satellite remote sensing image includes: Take any denoised satellite remote sensing image as the image to be denoised; Grayscale the image to be denoised to obtain a grayscale image; dividing the grayscale image into a plurality of grayscale sub-images; Take any grayscale sub-image and obtain the grayscale value corresponding to each pixel in the grayscale sub-image; Calculate the clarity value corresponding to the grayscale sub-image based on the grayscale value corresponding to each pixel in the grayscale sub-image; comparing the clarity value with a preset clarity threshold; Taking the grayscale sub-image whose definition value is less than or equal to a preset definition threshold as the target image; Randomly select a pixel point in the target image as the target pixel point; Determine the target area with the target pixel as the center and the preset step size as the radius; Calculate the grayscale difference between the target pixel and all pixels in the target area except the target pixel to obtain a number of grayscale difference values; The average of several grayscale differences is taken as the grayscale difference value corresponding to the target pixel; Comparing the grayscale difference value with a preset grayscale difference threshold, and when it is determined that the grayscale difference value is greater than or equal to the preset grayscale difference threshold, taking the target pixel point as a pixel point to be processed; Traverse all the pixels in the target image to obtain a number of pixels to be processed; Performing noise reduction on the pixel points to be processed based on a median filter to obtain a target image after noise reduction; Traverse all satellite remote sensing images to obtain denoised satellite remote sensing images.

[0011] Preferably, calculating the clarity value corresponding to the grayscale sub-image based on the grayscale value corresponding to each pixel in the grayscale sub-image includes: Randomly select a row of pixel points in the grayscale sub-image as the first pixel point set; obtain the grayscale maximum value, grayscale minimum value and grayscale mean value in the first pixel point set; The difference between the maximum grayscale value and the mean grayscale value in the first pixel point set is taken as the first difference value; The difference between the grayscale mean and the grayscale minimum in the first pixel point set is taken as the second difference; Calculate the square sum of the first difference and the second difference and take the square root to obtain the clarity evaluation value of the row corresponding to the first pixel point set; Traversing all rows of pixel points in the grayscale sub-image, and obtaining clarity evaluation values ​​of rows corresponding to a plurality of first pixel point sets; Randomly select a column of pixel points in the grayscale sub-image as the second pixel point set; obtain the grayscale maximum value, grayscale minimum value and grayscale mean value in the second pixel point set; The difference between the grayscale maximum value and the grayscale mean value in the second pixel point set is taken as the third difference value; The difference between the grayscale mean and the grayscale minimum in the second pixel point set is taken as the fourth difference; Calculate the sum of squares of the third difference and the fourth difference and take the square root thereof to obtain a clarity evaluation value of a column corresponding to the second pixel point set; Traversing all columns of pixel points in the grayscale sub-image to obtain clarity evaluation values ​​of columns corresponding to a plurality of second pixel point sets; Calculating the average of the clarity evaluation values ​​of the rows corresponding to the first pixel point set to obtain a first clarity evaluation average value; Calculating the average of the clarity evaluation values ​​of the columns corresponding to the plurality of second pixel point sets to obtain a second clarity evaluation average value; Based on the first clarity evaluation mean value and the second clarity evaluation mean value, a clarity value corresponding to the grayscale sub-image is determined.

[0012] Preferably, after adjusting the soil index interval based on the comparison result, the method further includes: Obtain data on soil microbial community structure and its transformation of soil nutrients in construction-disturbed areas in red soil hilly areas; Obtain climate and geomorphic data for construction disturbance areas in red soil hilly areas; The soil index range is revised based on the soil microbial community structure and soil nutrient transformation data, climate data and geomorphological data in the construction disturbance area of ​​the red soil hilly region; Based on the corrected soil index interval, vegetation restoration is carried out on the soil in the construction disturbance area of ​​the red soil hilly area.

[0013] Preferably, the method for constructing a plant growth state recognition model comprises: Obtain a plant growth status training data set; Inputting the plant growth state training data set into a neural network model for iterative training to obtain an initial plant growth state recognition model; Obtain a plant growth status test data set; The initial plant growth state recognition model is tested based on the plant growth state test data set, and when the test result is qualified, a trained initial plant growth state recognition model is obtained.

[0014] The invention discloses a vegetation restoration planning method for analyzing soil indicators in a construction disturbance zone in a red soil hilly area. By clarifying the growth habits and nutritional requirements of suitable dominant plant species, key soil nutrient indicators are determined, so that vegetation restoration planning is more targeted and the survival rate and growth quality of plants are improved. The soil indicator interval is determined according to the soil nutrient threshold range, which provides a scientific basis for vegetation restoration and avoids blindness. Long-term monitoring of soil nutrients and timely adjustment of the soil indicator interval according to plant growth feedback can better adapt to changes in actual conditions and ensure that vegetation restoration work is carried out continuously and effectively. Reasonable planning and dynamic adjustment are helpful to improve the efficiency and effect of vegetation restoration and reduce resource waste. It is beneficial to promote the stability and sustainable development of the ecosystem in the construction disturbance zone in the red soil hilly area and improve the quality of the ecological environment.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0016] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 is a flow chart of a vegetation restoration planning method for analyzing soil indicators in a construction disturbance area in a red soil hilly area according to an embodiment of the present invention; Figure 2 is a flow chart of determining a soil index interval suitable for plant growth according to an embodiment of the present invention; Figure 3 The figure is a flow chart of a method for constructing a plant growth state recognition model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0019] Example 1 like Figure 1 As shown, a vegetation restoration planning method for soil index analysis in construction disturbance areas in red soil hilly areas includes steps S1-S5: S1: Obtain the growth habits and nutritional requirements of the dominant plant species suitable for the construction disturbance area in the red soil hilly area, and determine the key soil nutrient indicators of the dominant plant species; S2: Obtain the soil nutrient threshold range corresponding to the key soil nutrient indicators of the dominant plant species; S3: Determining a soil index interval suitable for plant growth based on the soil nutrient threshold range; S4: Monitor soil nutrients in construction-disturbed areas in red soil hilly areas and adjust soil index ranges based on plant growth feedback results; S5: Based on the adjusted soil index range, soil vegetation restoration is performed on the soil in the construction disturbance area of ​​the red soil hilly area.

[0020] In this embodiment, the growth habits and nutritional requirements of dominant plant species suitable for construction disturbance areas in red soil hilly areas are obtained, and key soil nutrient indicators of dominant plant species suitable for growth are determined. The specific implementation method is as follows: extensively investigate and collect dominant plant species that are common in red soil hilly areas and suitable for growth in construction disturbance areas, regularly observe the growth conditions of plants in natural environments, including growth rate, morphological characteristics, branching conditions, etc., and record environmental data such as light, temperature, and humidity; collect plant samples for nutrient component analysis to determine the content of major nutrients; set up experimental groups with different nutrient supply levels to observe differences in plant growth; select soil samples from multiple representative locations in the construction disturbance area; analyze the content of major nutrients such as organic matter, nitrogen, phosphorus, potassium, and the content of secondary elements and trace elements in the soil; correlate and analyze plant growth habits, nutritional requirements and soil nutrient data; find out the key soil nutrients that have a greater impact on plant growth; comprehensively analyze the results and clarify the key soil nutrient indicators of dominant plant species suitable for growth.

[0021] In this embodiment, the soil nutrient threshold range corresponding to the key soil nutrient indicators of the dominant plant species is obtained, specifically including: collecting soil samples from different locations in the typical area where the plant grows, and conducting a detailed analysis of key soil nutrients such as nitrogen, phosphorus, potassium, organic matter, etc.; observing and recording the growth performance of the plants at these sampling points, including indicators such as growth rate, plant height, yield, and health status; correlating and analyzing the soil nutrient data with the plant growth status data to find out the nutrient content range corresponding to good growth; conducting potted or field comparative experiments, setting up treatments with different nutrient gradients, observing the growth differences of plants under different nutrient conditions, and further determining the threshold range; consulting research literature and materials on related plants to obtain the soil nutrient threshold range under similar circumstances as a reference and verification.

[0022] The beneficial effects of the above technical scheme are: by clarifying the growth habits and nutritional requirements of the dominant plant species, the key soil nutrient indicators are determined, making the vegetation restoration plan more targeted and improving the survival rate and growth quality of the plants; determining the soil indicator interval based on the soil nutrient threshold range provides a scientific basis for vegetation restoration and avoids blindness; long-term monitoring of soil nutrients and timely adjustment of the soil indicator interval based on plant growth feedback can better adapt to changes in actual conditions and ensure that vegetation restoration work is carried out continuously and effectively; reasonable planning and dynamic adjustment help to improve the efficiency and effectiveness of vegetation restoration and reduce resource waste; it is conducive to promoting the stability and sustainable development of the ecosystem in the construction disturbance area of ​​the red soil hilly area and improving the quality of the ecological environment.

[0023] Example 2 The key soil nutrient indicators of the suitable dominant plant species include at least one of macroelements, secondary elements and trace elements; the macroelements include at least one of organic matter content, nitrogen, phosphorus and potassium; the secondary elements include at least one of calcium, magnesium and sulfur; the trace elements include at least one of iron, zinc and copper.

[0024] Example 3 like Figure 2 As shown, the soil index interval suitable for plant growth is determined based on the soil nutrient threshold range, including S31-S34: S31: Randomly select a key soil nutrient index of an adaptive dominant plant species as the target soil nutrient index; S32: performing an intersection operation on the soil nutrient threshold range corresponding to the target soil nutrient index to obtain a target soil index interval corresponding to the target soil nutrient index; S33: traverse all key soil nutrient indicators of the dominant plant species, and obtain target soil indicator intervals corresponding to all key soil nutrient indicators; S34: The target soil index range corresponding to all key soil nutrient indicators is used as the soil index range suitable for plant growth.

[0025] In this embodiment, the soil nutrient threshold range corresponding to the target soil nutrient index is subjected to an intersection operation to obtain a target soil index interval corresponding to the target soil nutrient index; for example: assuming that the target soil nutrient index is a nitrogen element index, assuming that there are currently four dominant plant species A, B, C, and D, the soil nutrient threshold range corresponding to the nitrogen element index of plant A is 10 to 30 g / kg; the soil nutrient threshold range corresponding to the nitrogen element index of plant B is 12 to 35 g / kg; the soil nutrient threshold range corresponding to the nitrogen element index of plant C is 20 to 30 g / kg; the soil nutrient threshold range corresponding to the nitrogen element index of plant D is 25 to 40 g / kg; then the target soil nutrient index is a target soil index interval corresponding to the nitrogen element index of 25 to 30 g / kg.

[0026] The beneficial effects of the above technical scheme are: by selecting key soil nutrient indicators and performing intersection operations, the specific soil condition range suitable for plant growth can be determined more accurately, thereby improving the grasp of plant growth requirements; traversing all key soil nutrient indicators to ensure that the various factors affecting plant growth are taken into account, so that the obtained suitable soil indicator range is more comprehensive and integrated; analyzing specific suitable and advantageous plant species makes the results more targeted and can better guide the planting and maintenance of such plants; using a systematic method and logic to determine the soil indicator range, which is highly scientific and reasonable, and provides a reliable basis for agricultural production, ecological restoration, etc.; helping people better understand the soil requirements for plant growth, thereby rationally allocating soil resources and improving land use efficiency and plant growth quality.

[0027] Example 4 Monitor soil nutrients in construction-disturbed areas in red soil hilly areas, and adjust soil index ranges based on plant growth feedback, including: Conducting systematic sampling and chemical analysis of soil in construction disturbance areas in the red soil hilly region to determine the actual content data corresponding to key soil nutrient indicators of the dominant plant species; The construction disturbance area in the red soil hilly area is evenly divided into three areas, namely the first monitoring area, the second monitoring area and the third monitoring area; Based on the maximum value of the soil index range suitable for plant growth, the actual content corresponding to the key soil nutrient index of the dominant plant species suitable for growth in the first monitoring area is adjusted to obtain the first target monitoring area; Based on the minimum value of the soil index range suitable for plant growth, the actual content of the key soil nutrient index corresponding to the dominant plant species suitable for growth in the second monitoring area is adjusted to obtain the second target monitoring area; Based on the median value of the soil index range suitable for plant growth, the actual content of the key soil nutrient index corresponding to the dominant plant species in the third monitoring area is adjusted to obtain the third target monitoring area; Planting suitable and advantageous plants in the first target monitoring area, the second target monitoring area and the third target monitoring area respectively; Regularly obtain satellite remote sensing images of the dominant plants suitable for the first target monitoring area, the second target monitoring area, and the third target monitoring area; Inputting the satellite remote sensing image into a pre-trained plant growth state recognition model for recognition, and obtaining growth feedback information of advantageous plants in a first target monitoring area, growth feedback information of advantageous plants in a second target monitoring area, and growth feedback information of advantageous plants in a third target monitoring area; Compare the growth feedback information of the advantageous plants in the first target monitoring area, the growth feedback information of the advantageous plants in the second target monitoring area, and the growth feedback information of the advantageous plants in the third target monitoring area, and adjust the soil index range based on the comparison results; If the growth feedback information of the advantageous plants in the first target monitoring area is consistent with the growth feedback information of the advantageous plants in the third target monitoring area and the growth feedback information of the advantageous plants in the second target monitoring area shows that the plants grow poorly, the minimum value of the soil target area is adjusted; If the growth feedback information of the advantageous plants in the second target monitoring area is consistent with the growth feedback information of the advantageous plants in the third target monitoring area and the growth feedback information of the advantageous plants in the first target monitoring area shows that the plants grow poorly, the maximum value of the soil target area is adjusted.

[0028] In this embodiment, the construction disturbance area in the red soil hilly area is evenly divided into three areas. Specifically, the undulation, slope, slope orientation, etc. of the hills should be considered to try to make each area representative and relatively balanced in terms of topography, ensuring that different soil types are reflected in each area.

[0029] The beneficial effects of the above technical scheme are: through systematic sampling and analysis, the soil nutrient status can be accurately grasped, and targeted adjustments can be made according to the needs of plant growth to improve the suitability of the soil for plant growth; the area is evenly divided and monitored and adjusted separately, and combined with satellite remote sensing images and model recognition, it is possible to fully understand the plant growth conditions in different regions and achieve more refined management and evaluation; timely adjustment of soil index intervals based on feedback information is conducive to creating a soil environment that is more conducive to the growth of adaptive and advantageous plants and promotes healthy plant growth and development; targeted adjustment of soil nutrients to avoid excessive or insufficient fertilization, improve resource utilization efficiency and reduce costs; implementation of this scheme in construction disturbance areas in red soil hilly areas will help accelerate the ecological restoration process and enhance the stability and sustainability of the ecosystem.

[0030] Example 5 Conduct systematic sampling and chemical analysis of the soil in the construction disturbance area of ​​the red soil hilly region to determine the actual content data corresponding to the key soil nutrient indicators of the dominant plant species, including: Soil samples were systematically collected at different locations and depths in the construction disturbance area in the red soil hilly region; The Kjeldahl method was used to determine the total nitrogen content in the soil samples through digestion, distillation and titration to obtain the content of nitrogen in the soil samples; The total phosphorus content in the soil sample was determined by the molybdenum antimony colorimetric method to obtain the content of phosphorus in the soil sample; The total potassium content in the soil sample is measured by flame photometry or atomic absorption spectrophotometry to obtain the content value of potassium in the soil sample; The content of organic matter in soil samples was measured by potassium dichromate volumetric method to obtain the content of organic matter in soil samples; The contents of calcium, magnesium, iron, zinc and copper in soil samples were determined by atomic absorption spectrophotometry to obtain the content values ​​of calcium, magnesium, iron, zinc and copper in soil samples; The content of sulfur in the soil sample was determined by barium sulfate turbidimetry to obtain the content of sulfur in the soil sample; The mean of the measured values ​​of the nitrogen content, the phosphorus content, the potassium content, the organic matter content, the calcium content, the magnesium content, the sulfur content, the iron content, the zinc content and the copper content in the soil samples are taken as the actual content data corresponding to the key soil nutrient indicators of the suitable dominant plant species.

[0031] In this embodiment, the Kjeldahl method is used to determine the total nitrogen content in the soil sample by digestion, distillation and titration to obtain the content measurement value of the nitrogen element in the soil sample; the Kjeldahl method is a classic and commonly used method for determining the total nitrogen content in the soil. The nitrogen-containing organic matter in the soil is converted into ammonium nitrogen by digestion, and then the ammonium nitrogen is released in the form of ammonia gas by distillation, and finally the nitrogen content is determined by titration, thereby obtaining an accurate content measurement value.

[0032] In this embodiment, the total phosphorus content in the soil sample is determined by the molybdenum antimony colorimetric method to obtain the measured value of the phosphorus content in the soil sample; the molybdenum antimony colorimetric method is one of the commonly used methods for determining the total phosphorus content in the soil. The principle is to use the molybdenum antimony reagent to react with phosphate ions to form a blue complex, and determine the phosphorus content by colorimetry.

[0033] In this embodiment, the total potassium content in the soil sample is measured by flame photometry or atomic absorption spectrophotometry to obtain the content measurement value of potassium in the soil sample; the flame photometry method uses flame to excite potassium to produce a characteristic spectrum, and determines the potassium content by measuring the spectrum intensity; the atomic absorption spectrophotometry has high sensitivity and accuracy, and can accurately measure the content of potassium; the content measurement value of potassium in the soil sample obtained by these methods can provide an important basis for soil fertility assessment, soil improvement, and the rational application of potassium fertilizer in agricultural production.

[0034] In this embodiment, the potassium dichromate volumetric method is used to measure the organic matter content in the soil sample to obtain the measured value of the organic matter content in the soil sample; the potassium dichromate volumetric method is one of the classic methods for measuring the organic matter content in the soil; potassium dichromate undergoes an oxidation-reduction reaction with the organic matter in the soil under heating conditions, and the organic matter content is indirectly calculated by measuring the amount of remaining potassium dichromate; the measured value of the organic matter content in the soil sample obtained using this method is of great significance for evaluating the fertility, quality and ecological function of the soil.

[0035] In this embodiment, the content of calcium, magnesium, iron, zinc and copper in the soil sample is measured by atomic absorption spectrophotometry to obtain the content measurement values ​​of calcium, magnesium, iron, zinc and copper in the soil sample; atomic absorption spectrophotometry is a method for accurately measuring the content of these elements; the calcium content measurement value obtained by this method can reflect the abundance or deficiency of calcium in the soil, which has an important impact on plant growth and soil structure; the magnesium content measurement value is related to the photosynthesis and enzyme activity of the plant; the iron content measurement value is crucial to the respiration and chlorophyll synthesis of the plant; the zinc content measurement value will affect the growth, development and metabolism of the plant; and the copper content measurement value is also involved in the physiological process of the plant to a certain extent.

[0036] In this embodiment, the content of sulfur in the soil sample is determined by barium sulfate turbidimetry to obtain the content of sulfur in the soil sample; barium sulfate turbidimetry is a commonly used method for determining the content of sulfur. The content of sulfur obtained by this method can provide information about the sulfur content level in the soil.

[0037] The beneficial effects of the above technical scheme are: by systematically sampling different positions and depths in the construction disturbance area, it can comprehensively and accurately reflect the actual condition of the soil in the area, avoiding one-sidedness; using a variety of professional measurement methods to measure various key nutrient indicators in the soil, such as nitrogen, phosphorus, potassium, organic matter, calcium, magnesium, iron, zinc, copper and sulfur, detailed and accurate data are obtained, providing a reliable basis for subsequent analysis and decision-making; obtaining the mean of these nutrient indicators as the actual content data corresponding to the key soil nutrient indicators is helpful to understand the overall nutrient level and characteristics of the soil, provide scientific guidance for the selection and planting of suitable dominant plant species, and improve the survival rate and growth quality of plants; it is also conducive to the assessment and monitoring of soil quality in the construction disturbance area, providing strong data support for soil remediation and ecological restoration, and promoting the improvement and sustainable development of the ecological environment in the red soil hilly area.

[0038] Example 6 Before inputting the satellite remote sensing image into a pre-trained plant growth state recognition model for recognition, the method further includes: The satellite remote sensing image is subjected to denoising to obtain a denoised satellite remote sensing image.

[0039] Example 7 Denoising the satellite remote sensing image to obtain a denoised satellite remote sensing image, comprising: Take any denoised satellite remote sensing image as the image to be denoised; Grayscale the image to be denoised to obtain a grayscale image; dividing the grayscale image into a plurality of grayscale sub-images; Take any grayscale sub-image and obtain the grayscale value corresponding to each pixel in the grayscale sub-image; Calculate the clarity value corresponding to the grayscale sub-image based on the grayscale value corresponding to each pixel in the grayscale sub-image; comparing the clarity value with a preset clarity threshold; Taking the grayscale sub-image whose definition value is less than or equal to a preset definition threshold as the target image; Randomly select a pixel point in the target image as the target pixel point; Determine the target area with the target pixel as the center and the preset step size as the radius; Calculate the grayscale difference between the target pixel and all pixels in the target area except the target pixel to obtain a number of grayscale difference values; The average of several grayscale differences is taken as the grayscale difference value corresponding to the target pixel; Comparing the grayscale difference value with a preset grayscale difference threshold, and when it is determined that the grayscale difference value is greater than or equal to the preset grayscale difference threshold, taking the target pixel point as a pixel point to be processed; Traverse all the pixels in the target image to obtain a number of pixels to be processed; Performing noise reduction on the pixel points to be processed based on a median filter to obtain a target image after noise reduction; Traverse all satellite remote sensing images to obtain denoised satellite remote sensing images.

[0040] In this embodiment, the preset clarity threshold is set in advance based on industry experience.

[0041] In this embodiment, the preset grayscale difference threshold is determined based on historical image data processing.

[0042] The working principle of the above technical solution is: take a satellite remote sensing image as the image to be denoised and perform grayscale processing to convert it into a grayscale image. Then divide the grayscale image into multiple grayscale sub-images, calculate the clarity value for each grayscale sub-image, and compare it with the preset clarity threshold to screen out the target image with a lower clarity value. Then, in the target image, determine the target area with the target pixel as the center, calculate the grayscale difference between the target pixel and other pixels in the area, and average the grayscale difference value, and then compare it with the preset grayscale difference threshold to determine the pixel to be processed. After traversing the target image to find all the pixels to be processed, use the median filter to perform noise reduction processing on these pixels to be processed, so as to obtain the target image after noise reduction. Finally, traverse all satellite remote sensing images to complete the entire noise reduction process.

[0043] The beneficial effects of the above technical solution are: by graying and segmenting the satellite remote sensing images, the images can be analyzed and processed more carefully, and the pertinence and accuracy of noise reduction are improved; by calculating the clarity value and grayscale difference value to screen out the target images and pixels that need to be processed, it is avoided to process the entire image indiscriminately, improve efficiency, and better preserve the details and features of the image; using the median filter to perform noise reduction processing on the determined pixels to be processed can effectively remove noise, improve image quality, and make the satellite remote sensing images clearer and more accurate, which is of great significance for the subsequent analysis, recognition and application based on these images.

[0044] Example 8 Calculating the clarity value corresponding to the grayscale sub-image based on the grayscale value corresponding to each pixel in the grayscale sub-image includes: Randomly select a row of pixel points in the grayscale sub-image as the first pixel point set; obtain the grayscale maximum value, grayscale minimum value and grayscale mean value in the first pixel point set; The difference between the maximum grayscale value and the mean grayscale value in the first pixel point set is taken as the first difference value; The difference between the grayscale mean and the grayscale minimum in the first pixel point set is taken as the second difference; Calculate the square sum of the first difference and the second difference and take the square root to obtain the clarity evaluation value of the row corresponding to the first pixel point set; Traversing all rows of pixel points in the grayscale sub-image, and obtaining clarity evaluation values ​​of rows corresponding to a plurality of first pixel point sets; Randomly select a column of pixel points in the grayscale sub-image as the second pixel point set; obtain the grayscale maximum value, grayscale minimum value and grayscale mean value in the second pixel point set; The difference between the grayscale maximum value and the grayscale mean value in the second pixel point set is taken as the third difference value; The difference between the grayscale mean and the grayscale minimum in the second pixel point set is taken as the fourth difference; Calculate the sum of squares of the third difference and the fourth difference and take the square root thereof to obtain a clarity evaluation value of a column corresponding to the second pixel point set; Traversing all columns of pixel points in the grayscale sub-image to obtain clarity evaluation values ​​of columns corresponding to a plurality of second pixel point sets; Calculating the average of the clarity evaluation values ​​of the rows corresponding to the first pixel point set to obtain a first clarity evaluation average value; Calculating the average of the clarity evaluation values ​​of the columns corresponding to the plurality of second pixel point sets to obtain a second clarity evaluation average value; Based on the first clarity evaluation mean value and the second clarity evaluation mean value, a clarity value corresponding to the grayscale sub-image is determined.

[0045] In this embodiment, determining the clarity value corresponding to the grayscale sub-image based on the first clarity evaluation mean value and the second clarity evaluation mean value includes: The clarity value corresponding to the grayscale sub-image is determined by taking the square root of the sum of the squares of the first clarity evaluation mean value and the second clarity evaluation mean value.

[0046] The working principle of the above technical solution is: operate on the rows and columns of the grayscale sub-image respectively. For each row, select a set of pixel points of a row (the first pixel point set), calculate the difference between the maximum grayscale value and the mean value (the first difference) and the difference between the mean value and the minimum grayscale value (the second difference) in the set, and calculate their squares and square roots to obtain the clarity evaluation value of the row, and traverse all rows to obtain a series of clarity evaluation values ​​of the rows. Similar operations are performed for each column to obtain the clarity evaluation value of the column. Then, calculate the mean of the clarity evaluation values ​​of the rows (the first row clarity evaluation mean) and the mean of the clarity evaluation values ​​of the columns (the second column clarity evaluation mean) respectively. Finally, the two means are combined to determine the clarity value of the entire grayscale sub-image, which reflects the clarity characteristics of the grayscale sub-image in the horizontal and vertical directions and can be used for subsequent judgment and processing.

[0047] The beneficial effects of the above technical solution are: by calculating the clarity evaluation values ​​of rows and columns respectively and finding the average, it is possible to more comprehensively and accurately reflect the clarity of the grayscale sub-image in different directions, thereby improving the reliability of the clarity evaluation; a detailed analysis is performed on each row and column to better capture the detail differences of the image in the horizontal and vertical directions, which is helpful for subsequent targeted processing; based on this accurate clarity evaluation, noise reduction and other processing can be performed more effectively, thereby improving the quality of the entire satellite remote sensing image, making the image clearer and more usable.

[0048] Example 9 After adjusting the soil index range based on the comparison results, it also includes: Obtain data on soil microbial community structure and its transformation of soil nutrients in construction-disturbed areas in red soil hilly areas; Obtain climate and geomorphic data for construction disturbance areas in red soil hilly areas; The soil index range is revised based on the soil microbial community structure and soil nutrient transformation data, climate data and geomorphological data in the construction disturbance area of ​​the red soil hilly region; Based on the corrected soil index interval, vegetation restoration is carried out on the soil in the construction disturbance area of ​​the red soil hilly area.

[0049] In this embodiment, soil microbial community structure data and soil nutrient conversion data of the construction disturbance area in the red soil hilly area are obtained; soil sampling is carried out at representative locations in the construction disturbance area and the control area; the DNA of soil microorganisms is analyzed using molecular biology techniques, such as high-throughput sequencing, to determine the types and abundance of microorganisms; soil smears are observed under a microscope to understand the morphology and distribution of microorganisms. To obtain soil nutrient conversion data, soil samples are collected regularly to analyze the changes in the content of major nutrients such as nitrogen, phosphorus, and potassium; indoor cultivation experiments can be carried out to simulate construction disturbance conditions, monitor the conversion process and rate of nutrients, establish monitoring points on site, continuously monitor the dynamic changes of soil nutrients at different time points, and measure the physical properties of the soil, such as texture, porosity, etc., and perform correlation analysis with nutrient conversion data.

[0050] In this embodiment, climate data and geomorphic data of the construction disturbance area in the red soil hilly area are obtained; the local meteorological department or meteorological station is contacted to obtain historical and real-time meteorological data, including temperature, precipitation, wind speed, humidity, etc.; topographic maps and geological maps of the area are obtained, from which information such as terrain undulations, slopes, and altitudes can be understood; relevant GIS software and databases are used to obtain geomorphic feature data; and professional personnel can be organized to conduct field measurements and surveys to obtain more detailed and accurate geomorphic data.

[0051] In this embodiment, it is assumed that the soil microbial community structure data of the construction disturbance area in the red soil hilly area is obtained through preliminary research, and it is found that some microbial populations are significantly reduced after disturbance, and these microorganisms are usually closely related to nitrogen conversion. At the same time, climate data show that the region has less precipitation and higher temperatures in the near future. Geomorphological data show that the slope of the area is large; then when revising the soil index interval, for example, the normal interval of soil nitrogen content originally set may need to be adjusted downward, because changes in the microbial community structure may affect the nitrogen conversion efficiency, resulting in a reduction in the actual available nitrogen. For example, due to low precipitation and high temperature, the soil moisture content interval may need to be adjusted accordingly, and a large slope may increase the risk of soil nutrient loss, further affecting the interval setting of relevant soil indicators; for example, the original effective interval of soil phosphorus, considering the impact of microbial communities on phosphorus conversion and the fact that arid climate may reduce the effectiveness of phosphorus, may narrow the effective interval of phosphorus. In this way, the reasonable interval range of various soil indicators can be more accurately corrected and determined by combining these different types of data.

[0052] The beneficial effects of the above technical scheme are: by continuously acquiring and updating soil microbial community structure data and soil nutrient transformation data, the intrinsic ecological characteristics and nutrient dynamics of the soil can be more accurately grasped, providing a scientific basis for subsequent adjustments and vegetation restoration; combined with climate data and geomorphological data, the impact of external environmental factors on the soil can be fully considered, so that the correction of soil index intervals is more in line with actual conditions, and the accuracy and reliability are improved; the soil index interval correction based on multi-faceted data can better adapt to the special conditions of construction disturbance areas in red soil hilly areas, provide more suitable soil conditions for vegetation restoration, and increase the success rate and effect of vegetation restoration; this method of comprehensively considering multiple factors is conducive to achieving sustainable soil remediation and ecological restoration, promoting the stability and healthy development of the ecosystem, and is of great significance to protecting and improving the regional ecological environment.

[0053] Example 10 The method for constructing a plant growth state recognition model includes steps S481-S484: S481: Obtain a plant growth status training data set; S482: inputting the plant growth state training data set into a neural network model for iterative training to obtain an initial plant growth state recognition model; S483: Acquire a plant growth status test data set; S484: Testing the initial plant growth state recognition model based on the plant growth state test data set, and obtaining a trained initial plant growth state recognition model when the test result is qualified.

[0054] The beneficial effects of the above technical solution are: by obtaining a special plant growth status training data set, it can be ensured that the model learns rich plant growth characteristics and patterns, laying the foundation for accurate identification; using the neural network model for iterative training can give full play to the powerful learning and fitting capabilities of the neural network and improve the model's recognition ability of complex plant growth states; by obtaining a test data set to test the initial model, the performance and accuracy of the model can be objectively evaluated to ensure the reliability of the model in practical applications; only when the test results are qualified can the trained model be obtained, which ensures that the model has high accuracy and applicability, and can provide effective technical support for the monitoring and management of plant growth status, help to promptly discover problems and take corresponding measures to promote the healthy growth of plants and the rational use of resources.

[0055] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas, characterized in that: include: Obtain the growth habits and nutritional requirements of dominant plant species suitable for construction disturbance areas in red soil hilly areas, and determine the key soil nutrient indicators of dominant plant species suitable for construction disturbance areas in red soil hilly areas; Obtain the soil nutrient threshold range corresponding to the key soil nutrient indicators of the dominant plant species; Determining a soil index interval suitable for plant growth based on the soil nutrient threshold range; Monitor soil nutrients in construction-disturbed areas in red soil hilly regions and adjust soil index ranges based on plant growth feedback; Based on the adjusted soil index range, vegetation restoration is carried out on the soil in the construction disturbance area of ​​the red soil hilly area.

2. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas as claimed in claim 1 is characterized in that: The key soil nutrient indicators of the suitable dominant plant species include at least one of macroelements, secondary elements and trace elements; the macroelements include at least one of organic matter content, nitrogen, phosphorus and potassium; the secondary elements include at least one of calcium, magnesium and sulfur; the trace elements include at least one of iron, zinc and copper.

3. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas as claimed in claim 2 is characterized in that: Determining a soil index interval suitable for plant growth based on the soil nutrient threshold range includes: A key soil nutrient index of an adaptive dominant plant species is randomly selected as a target soil nutrient index; Performing an intersection operation on the soil nutrient threshold range corresponding to the target soil nutrient index to obtain a target soil index interval corresponding to the target soil nutrient index; Traverse all key soil nutrient indicators of suitable dominant plant species to obtain target soil indicator ranges corresponding to all key soil nutrient indicators; The target soil index range corresponding to all key soil nutrient indicators is taken as the soil index range suitable for plant growth.

4. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas as claimed in claim 3 is characterized in that: Monitor soil nutrients in construction-disturbed areas in red soil hilly areas, and adjust soil index ranges based on plant growth feedback, including: Conducting systematic sampling and chemical analysis of soil in construction disturbance areas in the red soil hilly region to determine the actual content data corresponding to key soil nutrient indicators of the dominant plant species; The construction disturbance area in the red soil hilly area is evenly divided into three areas, namely the first monitoring area, the second monitoring area and the third monitoring area; Based on the maximum value of the soil index range suitable for plant growth, the actual content corresponding to the key soil nutrient index of the dominant plant species suitable for growth in the first monitoring area is adjusted to obtain the first target monitoring area; Based on the minimum value of the soil index range suitable for plant growth, the actual content of the key soil nutrient index corresponding to the dominant plant species suitable for growth in the second monitoring area is adjusted to obtain the second target monitoring area; Based on the median value of the soil index range suitable for plant growth, the actual content of the key soil nutrient index corresponding to the dominant plant species in the third monitoring area is adjusted to obtain the third target monitoring area; Planting suitable and advantageous plants in the first target monitoring area, the second target monitoring area and the third target monitoring area respectively; Regularly obtain satellite remote sensing images of the dominant plants suitable for the first target monitoring area, the second target monitoring area, and the third target monitoring area; Inputting the satellite remote sensing image into a pre-trained plant growth state recognition model for recognition, and obtaining growth feedback information of advantageous plants in a first target monitoring area, growth feedback information of advantageous plants in a second target monitoring area, and growth feedback information of advantageous plants in a third target monitoring area; Compare the growth feedback information of the advantageous plants in the first target monitoring area, the growth feedback information of the advantageous plants in the second target monitoring area, and the growth feedback information of the advantageous plants in the third target monitoring area, and adjust the soil index interval based on the comparison results; If the growth feedback information of the advantageous plants in the first target monitoring area is consistent with the growth feedback information of the advantageous plants in the third target monitoring area and the growth feedback information of the advantageous plants in the second target monitoring area shows that the plants grow poorly, the minimum value of the soil target area is adjusted; If the growth feedback information of the advantageous plants in the second target monitoring area is consistent with the growth feedback information of the advantageous plants in the third target monitoring area and the growth feedback information of the advantageous plants in the first target monitoring area shows that the plants grow poorly, the maximum value of the soil target area is adjusted.

5. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas as claimed in claim 4, characterized in that: Conduct systematic sampling and chemical analysis of the soil in the construction disturbance area of ​​the red soil hilly region to determine the actual content data corresponding to the key soil nutrient indicators of the dominant plant species, including: Soil samples were systematically collected at different locations and depths in the construction disturbance area in the red soil hilly region; The Kjeldahl method was used to determine the total nitrogen content in the soil samples through digestion, distillation and titration to obtain the content of nitrogen in the soil samples; The total phosphorus content in the soil sample was determined by the molybdenum antimony colorimetric method to obtain the content of phosphorus in the soil sample; The total potassium content in the soil sample is measured by flame photometry or atomic absorption spectrophotometry to obtain the content value of potassium in the soil sample; The content of organic matter in soil samples was measured by potassium dichromate volumetric method to obtain the content of organic matter in soil samples; The contents of calcium, magnesium, iron, zinc and copper in soil samples were determined by atomic absorption spectrophotometry to obtain the content values ​​of calcium, magnesium, iron, zinc and copper in soil samples; The content of sulfur in the soil sample was determined by barium sulfate turbidimetry to obtain the content of sulfur in the soil sample; The mean of the measured values ​​of nitrogen content, phosphorus content, potassium content, organic matter content, calcium content, magnesium content, sulfur content, iron content, zinc content and copper content in the soil samples are taken as the actual content data corresponding to the key soil nutrient indicators of the suitable dominant plant species.

6. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas as claimed in claim 4, characterized in that: Before inputting the satellite remote sensing image into a pre-trained plant growth state recognition model for recognition, the method further includes: The satellite remote sensing image is subjected to denoising to obtain a denoised satellite remote sensing image.

7. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas according to claim 6, characterized in that: Denoising the satellite remote sensing image to obtain a denoised satellite remote sensing image, comprising: Take any denoised satellite remote sensing image as the image to be denoised; Grayscale the image to be denoised to obtain a grayscale image; dividing the grayscale image into a plurality of grayscale sub-images; Take any grayscale sub-image and obtain the grayscale value corresponding to each pixel in the grayscale sub-image; Calculate the clarity value corresponding to the grayscale sub-image based on the grayscale value corresponding to each pixel in the grayscale sub-image; comparing the clarity value with a preset clarity threshold; Taking the grayscale sub-image whose definition value is less than or equal to a preset definition threshold as the target image; Randomly select a pixel point in the target image as the target pixel point; Determine the target area with the target pixel as the center and the preset step size as the radius; Calculate the grayscale difference between the target pixel and all pixels in the target area except the target pixel to obtain a number of grayscale difference values; The average of several grayscale differences is taken as the grayscale difference value corresponding to the target pixel; Comparing the grayscale difference value with a preset grayscale difference threshold, and when it is determined that the grayscale difference value is greater than or equal to the preset grayscale difference threshold, taking the target pixel point as a pixel point to be processed; Traverse all the pixels in the target image to obtain a number of pixels to be processed; Performing noise reduction on the pixel points to be processed based on a median filter to obtain a target image after noise reduction; Traverse all satellite remote sensing images to obtain denoised satellite remote sensing images.

8. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas as claimed in claim 7, characterized in that: Calculating the clarity value corresponding to the grayscale sub-image based on the grayscale value corresponding to each pixel in the grayscale sub-image includes: Randomly select a row of pixel points in the grayscale sub-image as the first pixel point set; obtain the grayscale maximum value, grayscale minimum value and grayscale mean value in the first pixel point set; The difference between the maximum grayscale value and the mean grayscale value in the first pixel point set is taken as the first difference value; The difference between the grayscale mean and the grayscale minimum in the first pixel point set is taken as the second difference; Calculate the square sum of the first difference and the second difference and take the square root to obtain the clarity evaluation value of the row corresponding to the first pixel point set; Traversing all rows of pixel points in the grayscale sub-image, and obtaining clarity evaluation values ​​of rows corresponding to a plurality of first pixel point sets; Randomly select a column of pixel points in the grayscale sub-image as the second pixel point set; obtain the grayscale maximum value, grayscale minimum value and grayscale mean value in the second pixel point set; The difference between the grayscale maximum value and the grayscale mean value in the second pixel point set is taken as the third difference value; The difference between the grayscale mean and the grayscale minimum in the second pixel point set is taken as the fourth difference; Calculate the sum of squares of the third difference and the fourth difference and take the square root thereof to obtain a clarity evaluation value of a column corresponding to the second pixel point set; Traversing all columns of pixel points in the grayscale sub-image to obtain clarity evaluation values ​​of columns corresponding to a plurality of second pixel point sets; Calculating the average of the clarity evaluation values ​​of the rows corresponding to the first pixel point set to obtain a first clarity evaluation average value; Calculating the average of the clarity evaluation values ​​of the columns corresponding to the plurality of second pixel point sets to obtain a second clarity evaluation average value; Based on the first clarity evaluation mean value and the second clarity evaluation mean value, a clarity value corresponding to the grayscale sub-image is determined.

9. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas as claimed in claim 4, characterized in that: After adjusting the soil index range based on the comparison results, it also includes: Obtain data on soil microbial community structure and its transformation of soil nutrients in construction-disturbed areas in red soil hilly areas; Obtain climate and geomorphic data for construction disturbance areas in red soil hilly areas; The soil index range is revised based on the soil microbial community structure and soil nutrient transformation data, climate data and geomorphological data in the construction disturbance area of ​​the red soil hilly region; Based on the corrected soil index interval, vegetation restoration is carried out on the soil in the construction disturbance area of ​​the red soil hilly area.

10. The vegetation restoration planning method based on soil index analysis in construction disturbance areas in red soil hilly areas as claimed in claim 4, characterized in that: The method for constructing a plant growth state recognition model includes: Obtain a plant growth status training data set; Inputting the plant growth state training data set into a neural network model for iterative training to obtain an initial plant growth state recognition model; Obtain a plant growth status test data set; The initial plant growth state recognition model is tested based on the plant growth state test data set, and when the test result is qualified, a trained initial plant growth state recognition model is obtained.

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