A seedling management method and system for high-yield direct seeding of annual Angelica sinensis

Abstract

The invention discloses a seedling management method and system for high-yield direct seeding of annual Angelica sinensis, and the invention relates to the technical field of Chinese medicine planting. The method comprises: dividing a planned direct seeding plot into a plurality of sub-areas of equal area, and randomly selecting soil of different depths in each sub-area for sampling to obtain soil nutrient parameters; based on these parameters, calculating the nutrient content of each sub-area, and setting a nutrient content threshold, and improving the soil in areas below the threshold; collecting soil environmental parameters and Angelica sinensis seed diameter data, calculating the sowing depth, determining the plant planting row spacing and column spacing according to the effective illumination area in each sub-area, sowing in the improved soil, establishing a pest and disease classification model, judging the growth state of Angelica sinensis according to the model output, issuing corresponding processing instructions, improving the photosynthetic efficiency and growth balance of the plant, optimizing the utilization efficiency of land and resources, and reducing the impact of pests and diseases on yield and quality.

Description

Technical Field

[0001] The invention relates to the technical field of traditional Chinese medicine planting, and in particular to a seedling management method and system for high-yield direct seeding of annual Angelica sinensis. Background Art

[0002] Annual angelica sinensis is an important Chinese medicinal material, and its growth has strict requirements on soil conditions and environment. However, traditional planting management methods, especially in the seedling stage, often lack precise monitoring and regulation of soil nutrients and environmental conditions. This extensive management may lead to uneven crop growth, frequent pests and diseases, and ultimately affect yield and quality.

[0003] In addition, the determination of sowing depth, row spacing and column spacing in traditional agriculture usually depends on the experience of farmers, while ignoring the differences in soil environment. The soil temperature and humidity in different regions are different, which directly affects the germination rate of angelica seeds and the growth rate of seedlings. If the sowing depth is not appropriate, the seeds may lose vitality due to insufficient or excessive soil moisture. In addition, the setting of row spacing and column spacing lacks scientific basis, which may lead to fierce competition between plants, affecting the efficiency of photosynthesis and the final yield.

[0004] Traditional pest and disease monitoring also relies mainly on manual experience, which is time-consuming, labor-intensive and has low accuracy. Manual identification of pests and diseases requires rich experience and a large amount of labor input, and in large-scale planting, missed detection and misjudgment often occur. This requires a more efficient and accurate method to identify and classify pests and diseases so that timely prevention and control measures can be taken.

[0005] Therefore, the main technical problems faced by Yun Angelicae Sinensis in the production process include: randomness and inaccuracy in soil nutrient management, unscientific setting of sowing and planting parameters, and lag and inaccuracy in pest and disease management. These technical problems seriously restrict the high yield and high quality production of Yun Angelicae Sinensis. This requires a systematic approach to solve these problems, through precise soil nutrient monitoring and adjustment, reasonable planting parameter setting, and intelligent pest and disease monitoring and management to improve production efficiency and product quality.

[0006] In the prior art, publication number CN110476762A discloses a high-yield direct seeding method for annual angelica sinensis, including 1. time selection, 2. land preparation, 3. seed treatment, 4. preparation before direct seeding, 5. sowing, 6. seedling management, and 7. harvesting. First, the sowing is carried out according to a row spacing of 20×25, and 3 to 4 seeds are used in each pond; the seedling management includes temperature management, water management, weeding and prevention of pests and diseases, seedling hardening, fertilization, thinning and intertillage management. After the seedlings emerge, they should be observed at any time. If the central part or the protruding part of the soil surface is short of water on sunny days, water should be supplemented in the morning or evening every day; weeding and Prevention and control of diseases and insect pests, weeds are removed after watering, etc. This solution improves the quality of Angelica sinensis, has a high survival rate, reduces bolting, increases the medicinal value of Angelica sinensis, increases the yield of Angelica sinensis, increases the survival rate of Angelica sinensis, and reduces the planting cost; however, this solution uses fixed experience for sowing, and does not consider the impact of positional factors such as the row spacing of Angelica sinensis planting on the growth and yield of Angelica sinensis. At the same time, after the seedlings emerge, they must be observed at any time, which requires a lot of manpower. Therefore, only considering temperature management, water management, etc. not only wastes resources, but also reduces the real-time and effectiveness of the management system.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0008] The object of the present invention is to provide a seedling management method and system for high-yield direct seeding of annual Angelica sinensis, so as to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A seedling management method for high-yield direct seeding of annual Angelica sinensis, comprising the following specific steps:

[0011] The plot of land to be directly seeded is divided into several sub-areas of equal area, soil at different depths is randomly selected in each sub-area for sampling and analysis, and soil nutrient parameters of the sub-area are obtained, wherein the soil nutrient parameters include soil pH value, nitrogen, phosphorus and potassium concentrations, and soil parameter thresholds are set, and soil improvement is performed on the sub-area in combination with the soil nutrient parameters and the soil parameter thresholds;

[0012] Collect soil environmental parameters, combine with the seed diameter data of Angelica sinensis, calculate the sowing depth, determine the planting row spacing and column spacing of plants according to the light intensity in each sub-area, and sow in the improved soil based on the calculated sowing depth, planting row spacing and column spacing. The soil environmental parameters include the average soil temperature and average humidity. During the seedling growth process, irrigation is carried out according to the seedling growth height and real-time monitoring of soil humidity data;

[0013] Collect images of the leaves of Danggui that grow normally and those that are affected by different pests and diseases, establish a pest and disease classification model based on the SVM model, add labels to different images, specifically, mark the images of the leaves of Danggui that grow normally as normal images, and mark the types of pests and diseases in the images of the leaves affected by pests and diseases, use the images with labels as inputs to the pest and disease classification model, and use the image labels as labels to train the pest and disease classification model;

[0014] Real-time images of Angelica sinensis leaves are collected and input into the trained pest and disease classification model. The growth status of the Angelica sinensis is judged based on the model output, and different instructions are issued according to different growth status.

[0015] Furthermore, the plots to be broadcast live are divided into m Sub-areas of equal area were selected, and a mark point was randomly set in each of the five depth ranges in the sub-area for soil sampling. The pH value of the sampled soil was obtained based on the pH electrode determination method, the nitrogen concentration of the sampled soil was obtained based on the Kjeldahl reagent method, the phosphorus concentration of the sampled soil was obtained based on the chromatography method, and the potassium concentration of the sampled soil was obtained based on the flame photometry method. The average value of the five sampled soils was used as the soil pH value, nitrogen, phosphorus and potassium concentration of the sub-area. The depth range includes: 0-10 cm , Ground Floor 11-20 cm , 21-30, underground cm , 31-40 underground cm and underground 41-50 cm .

[0016] Furthermore, the logic for setting soil parameter thresholds and combining soil nutrient parameters and soil parameter thresholds to improve soil in sub-regions is as follows:

[0017] The soil parameter thresholds include soil pH threshold, soil nitrogen concentration threshold, phosphorus concentration threshold and potassium concentration threshold. When the detected soil pH value is less than or equal to the soil pH threshold, yz When soil pH is adjusted, lime is used, and the amount of lime used is calculated based on the formula: In the formula, G The amount of lime used, β is the adjustment coefficient, T is the soil pH value of the sub-region, A is the land area of ​​the sub-region;

[0018] When the concentrations of soil nitrogen, phosphorus and potassium are less than or equal to the soil nitrogen concentration threshold, phosphorus concentration threshold and potassium concentration threshold, the corresponding fertilizer is applied to the soil, and the formula for calculating the fertilizer amount is: In the formula, F The amount of nitrogen fertilizer applied is ε is the soil nitrogen concentration threshold,N is the soil nitrogen concentration, E is the fertilizer nitrogen concentration, Sd is the soil depth to be fertilized, XN is the soil absorption coefficient of nitrogen; for land whose phosphorus and potassium content is less than or equal to the phosphorus content threshold and potassium content threshold, the same method as that for improving nitrogen content is adopted.

[0019] Furthermore, the seeds were sown in the improved soil, wherein the formula for calculating the sowing depth was based on the soil environmental parameters and the seed diameter data of Angelica sinensis: In the formula, D is the sowing depth, Dbase Indicates the basic sowing depth, k1 and k2 Represent the humidity adjustment coefficient and temperature adjustment coefficient respectively, Hopt and Topt are the optimum soil moisture and optimum soil temperature, respectively. H and T represent the average soil moisture and average soil temperature, respectively. S is the seed diameter.

[0020] Furthermore, the row and column spacing of the plants are determined according to the average effective illumination area of ​​each seedling in each sub-region. The specific logic is as follows: randomly select several moments in a day to record the effective illumination area of ​​all the Angelica sinensis in the sub-region and the row and column spacing of the plants. The effective illumination area is the area of ​​sunlight falling on the leaves of the Angelica sinensis. Based on the effective illumination area of ​​all the Angelica sinensis in the sub-region at each moment recorded, the average value is calculated as the effective illumination area of ​​all the Angelica sinensis in the sub-region within the time range of one day. According to the effective illumination area of ​​all the Angelica sinensis, the average effective illumination area of ​​a single Angelica sinensis is calculated, wherein the row and column spacing of the calibrated plants are respectively d0 and r0 , obtain the average effective illumination area of ​​a single Angelica sinensis plant under the planting row spacing and row spacing through simulation experiments, set the average effective illumination area threshold, mark the planting row spacing and row spacing combinations corresponding to the average effective illumination area threshold that are greater than or equal to the average effective illumination area threshold, generate a preferred planting position set, select the combination with the smallest product of the planting row spacing and row spacing in the preferred planting position set as the optimal planting row spacing and row spacing, and plant according to the optimal planting row spacing and row spacing;

[0021] The logic of real-time monitoring of soil moisture for irrigation is: when the soil moisture is lower than the humidity threshold, the irrigation system is automatically turned on to ensure that the soil remains within an appropriate humidity range. The calculation formula for each irrigation amount is: In the formula, Z is the irrigation amount, R is the irrigation depth, φrepresents irrigation efficiency, where HR and HR Represents the humidity threshold and real-time soil humidity respectively; the irrigation depth R The calculation is based on the seedling growth height, and the specific formula is: In the formula, ρ is the root conversion coefficient, gd The height of seedling growth.

[0022] Furthermore, a pest and disease classification model is established based on the SVM model. The LabelImg tool is used to add labels to the image through manual labeling methods, and the status of the leaves is marked on the image. The image with the label is input into the SVM model, and the image label is used as the label to train the pest and disease classification model. The trained model takes the image of Angelica sinensis leaves as input and the label of the image as output. Specifically, the image of normally growing leaves is output as normal, and the image of leaves with pests and diseases is output as the type of pest and disease.

[0023] Furthermore, the logic for issuing different instructions according to different growth states is as follows: when it is judged that the current growth state of the cloud angelica is normal, a safety instruction is issued without any processing; when it is judged that the current growth state of the cloud angelica is suffering from pests and diseases, a warning instruction is issued, the type of pests and diseases is output, and farmers are reminded to take preventive measures according to the pests and diseases.

[0024] The present invention also provides a seedling management system for high-yield direct seeding of annual Angelica sinensis, which is used to execute the above-mentioned seedling management method for high-yield direct seeding of annual Angelica sinensis, including:

[0025] The land improvement module is used to divide the plot of land planned for direct seeding into several sub-areas of equal area, randomly select soil of different depths in each sub-area for sampling and analysis, obtain soil nutrient parameters of the sub-area, and the soil nutrient parameters include soil pH value, nitrogen, phosphorus and potassium concentrations, set soil parameter thresholds, and improve the soil in the sub-area in combination with soil nutrient parameters and soil parameter thresholds;

[0026] The planting optimization module is used to collect soil environmental parameters, calculate the sowing depth in combination with the diameter data of the angelica sinensis seeds, determine the planting row and column spacing of the plants according to the light intensity in each sub-area, and sow in the improved soil based on the calculated sowing depth, planting row and column spacing. The soil environmental parameters include the average soil temperature and average humidity. During the seedling growth process, irrigation is performed according to the seedling growth height and real-time monitoring of soil humidity data;

[0027] The pest and disease model training module is used to collect the images of the leaves of Danggui growing normally and the images of the leaves under the influence of different pests and diseases, establish the pest and disease classification model based on the SVM model, add labels to different images, specifically, mark the images of the leaves of Danggui growing normally as normal images, mark the types of pests and diseases in the images of the leaves under the influence of pests and diseases, use the images with labels as the input of the pest and disease classification model, and use the labels of the images as labels to train the pest and disease classification model;

[0028] The pest and disease identification module is used to collect real-time images of Angelica sinensis leaves and input them into the trained pest and disease classification model. According to the model output, the growth status of Angelica sinensis is judged, and different instructions are issued according to different growth status.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] First, through the fine division of the planting plots and the detailed measurement of soil parameters, the spatial variability of soil nutrients was accurately grasped. Based on this, soil improvement was carried out systematically to achieve the best soil nutrients in each sub-area, ensuring that the cloud angelica obtains sufficient and balanced nutrient supply during its growth, thereby significantly improving the growth rate and yield of the crop.

[0031] Secondly, the row and column spacing is adjusted according to the illumination area, so that the plants can make full use of light resources and reduce growth problems caused by insufficient light or excessive competition. This dynamic adjustment mechanism not only improves the photosynthetic efficiency and growth balance of the plants, but also optimizes the efficiency of land and resource use.

[0032] Finally, intelligent pest and disease monitoring based on the support vector machine (SVM) model provides a strong guarantee for plant health. The model achieves high-precision and high-efficiency pest and disease identification by learning normal and abnormal leaf images, and can timely warn of pest and disease risks, provide farmers with accurate management suggestions, and reduce the use of chemical agents. This not only reduces the impact of pests and diseases on yield and quality, but also reduces environmental pollution and promotes ecological sustainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0034] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0036] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. Example

[0037] See also Figure 1 , the present invention provides a technical solution:

[0038] A seedling management method for high-yield direct seeding of annual Angelica sinensis, comprising the following specific steps:

[0039] Step 1: Divide the plot of land for direct seeding into several sub-areas of equal area, randomly select soil at different depths in each sub-area for sampling and analysis, obtain the soil nutrient parameters of the sub-area, the soil nutrient parameters include soil pH value, nitrogen, phosphorus and potassium concentrations, set soil parameter thresholds, and improve the soil in the sub-area in combination with soil nutrient parameters and soil parameter thresholds.

[0040] Divide the plots to be broadcast live into m Sub-areas of equal area were selected, and a mark point was randomly set in each of the five depth ranges in the sub-area for soil sampling. The pH value of the sampled soil was obtained based on the pH electrode determination method, the nitrogen concentration of the sampled soil was obtained based on the Kjeldahl reagent method, the phosphorus concentration of the sampled soil was obtained based on the chromatography method, and the potassium concentration of the sampled soil was obtained based on the flame photometry method. The average value of the five sampled soils was used as the soil pH value, nitrogen, phosphorus and potassium concentration of the sub-area. The depth range includes: 0-10 cm , Ground Floor 11-20 cm , 21-30, underground cm , 31-40 underground cm and underground 41-50 cmThe specific method of obtaining soil nutrient parameters is: let the sampled soil dry naturally at room temperature, avoid using high temperature, because this may change the chemical properties of the soil; remove impurities such as stones and roots through a 2 mm sieve. Mix a certain proportion of soil with deionized water (usually a ratio of 1:1 or 1:2.5 soil to water), let it stand for a period of time, and then use a pH meter to measure the pH value of the solution.

[0041] Organic and ammonium nitrogen are determined using the Kjeldahl method, which involves the determination of the total nitrogen content in the soil by distillation and titration. This method is suitable for the determination of organic and ammonium nitrogen.

[0042] The ammonium molybdate method (colorimetric method) was used to determine the phosphorus concentration. The available phosphorus in the soil reacted with ammonium molybdate to form a blue complex, and its concentration was then measured using a spectrophotometer.

[0043] Potassium concentration was determined using flame photometry to measure exchangeable potassium in soil samples. Soil samples were extracted with a neutral ammonium acetate solution and then measured by a flame photometer.

[0044] The logic for setting soil parameter thresholds and combining soil nutrient parameters and soil parameter thresholds to improve soil in sub-areas is as follows:

[0045] The soil parameter thresholds include soil pH threshold, soil nitrogen concentration threshold, phosphorus concentration threshold and potassium concentration threshold. When the detected soil pH value is less than or equal to the soil pH threshold, yz When soil pH is adjusted, lime is used, and the amount of lime used is calculated based on the formula: In the formula, G The amount of lime used, β is the adjustment coefficient, T is the soil pH value of the sub-region, A is the land area of ​​the sub-region;

[0046] The soil pH threshold yz It is set based on expert experience and actual conditions, according to the growth conditions of cloud angelica, soil pH threshold yz It is usually set to 6.5-7.5;

[0047] When the concentrations of soil nitrogen, phosphorus and potassium are less than or equal to the soil nitrogen concentration threshold, phosphorus concentration threshold and potassium concentration threshold, the corresponding fertilizer is applied to the soil, and the formula for calculating the fertilizer amount is: In the formula, F is the nitrogen fertilizer application rate, ε The soil nitrogen concentration threshold is generally set to the ideal nitrogen content required for crop growth. This value is usually determined based on the crop type, growth period, and expected yield. Sdis the soil depth to be fertilized, specifically the entire soil depth range of sampling, that is, 50cm, XN is the soil absorption coefficient of nitrogen, which is usually between 0.3 and 0.7; N is the soil nitrogen concentration, E Fertilizer nitrogen concentration refers to the percentage of nitrogen in the fertilizer used, which determines how much of the applied fertilizer is actually effective nitrogen; for land with phosphorus and potassium content less than or equal to the phosphorus content threshold and potassium content threshold, the same method as improving nitrogen content is adopted.

[0048] Step 2: Collect soil environmental parameters, combine with the cloud angelica seed diameter data, calculate the sowing depth, determine the plant planting row spacing and column spacing according to the light intensity in each sub-area, and sow in the improved soil based on the calculated sowing depth, planting row spacing and column spacing. The soil environmental parameters include the average soil temperature and average humidity. During the seedling growth process, irrigation is carried out according to the seedling growth height and real-time monitoring of soil humidity data.

[0049] Sowing was carried out in the improved soil. According to the soil environmental parameters and the seed diameter data of Angelica sinensis, the formula for calculating the sowing depth was as follows: In the formula, D is the sowing depth, Dbase Indicates the basic sowing depth, k1 and k2 Represent the humidity adjustment coefficient and temperature adjustment coefficient respectively, Hopt and Topt are the optimum soil moisture and optimum soil temperature, respectively. H and T represent the average soil moisture and average soil temperature, respectively. S is the seed diameter.

[0050] The sowing depth should be determined according to the changes in soil temperature. A shallow sowing depth allows the seeds to be exposed to warm soil temperatures earlier, thereby accelerating germination, but fluctuations in soil moisture and temperature must also be considered. Generally, large seeds have greater energy storage and are able to take root and germinate at deeper levels in the soil. Due to their larger size, the seed drill may be set to a deeper sowing depth when sowing to ensure that the seeds can get enough soil coverage to prevent being pecked by animals or blown away by the wind. Relatively speaking, small seeds usually require a shallower sowing depth. They have less energy storage and higher requirements for moisture and temperature when germinating. If sown too deeply, small seeds may not be able to overcome the resistance of the soil, resulting in difficulty or failure in germination.

[0051] The row and column spacing of plants are determined according to the average effective illumination area of ​​each seedling in each sub-region. The specific logic is as follows: randomly select several moments in a day to record the effective illumination area of ​​all Angelica sinensis in the sub-region and the row and column spacing of planting. The effective illumination area is the area of ​​sunlight falling on the leaves of Angelica sinensis. Based on the effective illumination area of ​​all Angelica sinensis in the sub-region at each moment recorded, the average value is calculated as the effective illumination area of ​​all Angelica sinensis in the sub-region within the time range of one day. According to the effective illumination area of ​​all Angelica sinensis, the average effective illumination area of ​​a single Angelica sinensis is calculated, wherein the row and column spacing of the calibrated plants are respectively d0 and r0 , obtain the average effective illumination area of ​​a single Angelica sinensis plant under the planting row spacing and row spacing through simulation experiments, set the average effective illumination area threshold, mark the planting row spacing and row spacing combinations corresponding to the average effective illumination area threshold that are greater than or equal to the average effective illumination area threshold, generate a preferred planting position set, select the combination with the smallest product of the planting row spacing and row spacing in the preferred planting position set as the optimal planting row spacing and row spacing, and plant according to the optimal planting row spacing and row spacing;

[0052] The optimal solution for the planting row spacing and row spacing can be solved through optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and ant colony algorithm. The effective illumination area under different planting row spacing and row spacing can be obtained through simulation experiments. Specifically, the illumination model (such as Ray-tracing, Monte Carlo, etc.) can be used to calculate the illumination distribution under different planting spacings, and the planting row spacing and row spacing are used as optimization parameters. The maximum sum of the average light intensity per unit area and the light energy capture efficiency is used as the optimization goal to solve the optimal value.

[0053] According to the optimization objective function, the planting row spacing that maximizes the average effective illumination area of ​​a single Angelica sinensis plant is solved d and line spacing r , based on the calculated d and r The optimal row and line spacing for planting is obtained, and planting is performed according to the optimal row and line spacing for planting;

[0054] The logic of real-time monitoring of soil moisture for irrigation is: when the soil moisture is lower than the humidity threshold, the irrigation system is automatically turned on to ensure that the soil remains within an appropriate humidity range. The calculation formula for each irrigation amount is: In the formula, Z is the irrigation amount, R is the irrigation depth, φ represents irrigation efficiency, where HR and HR Represents humidity threshold and real-time soil humidity respectively; irrigation depth R The calculation is based on the seedling growth height, and the specific formula is: In the formula, ρ is the root conversion coefficient, generally 0.33-0.55, gd The height of seedling growth.

[0055] The less water the soil lacks, the less irrigation depth is required, because the soil already contains a lot of water, so not much additional water is needed. The less water the soil lacks, the shallower the irrigation depth is required.

[0056] Different crops have different root distribution depths, and the irrigation depth should be set according to the root depth of the crop to ensure that water can reach and meet the absorption needs of the crop root system. For medicinal plants such as cloud angelica, the irrigation depth should be adapted to its specific root structure to support optimal growth.

[0057] Step 3: Collect images of normally growing leaves of Danggui and images of leaves affected by different pests and diseases, establish a pest and disease classification model based on the SVM model, and add labels to different images. Specifically, mark the normally growing leaf images of Danggui as normal images, and mark the types of pests and diseases in the leaf images affected by pests and diseases. Use the images with labels as inputs to the pest and disease classification model, and use the image labels as labels to train the pest and disease classification model.

[0058] A pest and disease classification model is established based on the SVM model. The LabelImg tool is used to add labels to the image through the manual labeling method, and the state of the leaf is marked on the image. The image with the label is input into the SVM model, and the image label is used as the label to train the pest and disease classification model. The trained model inputs the image of the cloud angelica leaf and outputs the label of the image. Specifically, the image of the leaf with normal growth outputs normal, and the image of the leaf with pests and diseases outputs the type of pests and diseases. Common diseases include powdery mildew, rust and leaf spot, and pests include aphids and red spiders. Powdery mildew is a disease caused by fungi, which is mainly manifested by the appearance of white powder on the leaves. In severe cases, it will cause the leaves to wilt. Rust will form rust spots on the leaves. Leaf spot is caused by fungi or bacteria, which is manifested by the appearance of spots of various colors on the leaves, affecting the photosynthesis of plants.

[0059] Aphids are common piercing-sucking pests that suck plant sap, causing leaves to curl and turn yellow. Red spider mites form small spots on plant leaves. The SVM model is trained based on the characteristic symptoms of different types of pests and diseases.

[0060] Support Vector Machine (SVM) is a supervised learning model used for classification and regression analysis. The SVM classification model performs well in processing high-dimensional data and small sample data sets. The SVM model can be used to distinguish image data.

[0061] Step 4: Collect real-time images of the Angelica sinensis leaves and input them into the trained pest and disease classification model. According to the model output, determine the growth status of the Angelica sinensis and issue different instructions according to different growth status.

[0062] According to the model output, the logic for judging the growth status of Angelica sinensis is as follows: the number of normal results of the collected model output is counted. If the number of normal results is greater than 95% of the total number of input model images, the growth status of Angelica sinensis in the current sub-area is judged to be normal; otherwise, the growth status of Angelica sinensis in the current sub-area is judged to be suffering from diseases and pests, and all detected types of diseases and pests are output.

[0063] The logic for issuing different instructions according to different growth states is as follows: when the current growth state of Cloud Angelica is judged to be normal, a safety instruction is issued without any processing; when the current growth state of Cloud Angelica is judged to be suffering from diseases and pests, a warning instruction is issued, the type of diseases and pests is output, and farmers are reminded to prevent and control the diseases and pests, which can be specifically prevented and controlled by improving the planting environment and using antifungal drugs.

[0064] See also Figure 2 The present invention also provides a seedling management system for high-yield direct seeding of annual Angelica sinensis, which is used to execute the above-mentioned seedling management method for high-yield direct seeding of annual Angelica sinensis, including:

[0065] The land improvement module is used to divide the plot of land planned for direct seeding into several sub-areas of equal area, randomly select soil of different depths in each sub-area for sampling and analysis, obtain soil nutrient parameters of the sub-area, and the soil nutrient parameters include soil pH value, nitrogen, phosphorus and potassium concentrations, set soil parameter thresholds, and improve the soil in the sub-area in combination with soil nutrient parameters and soil parameter thresholds;

[0066] The planting optimization module is used to collect soil environmental parameters, calculate the sowing depth in combination with the diameter data of the angelica sinensis seeds, determine the planting row and column spacing of the plants according to the light intensity in each sub-area, and sow in the improved soil based on the calculated sowing depth, planting row and column spacing. The soil environmental parameters include the average soil temperature and average humidity. During the seedling growth process, irrigation is performed according to the seedling growth height and real-time monitoring of soil humidity data;

[0067] The pest and disease model training module is used to collect the images of the leaves of Danggui growing normally and the images of the leaves under the influence of different pests and diseases, establish the pest and disease classification model based on the SVM model, add labels to different images, specifically, mark the images of the leaves of Danggui growing normally as normal images, mark the types of pests and diseases in the images of the leaves under the influence of pests and diseases, use the images with labels as the input of the pest and disease classification model, and use the labels of the images as labels to train the pest and disease classification model;

[0068] The pest and disease identification module is used to collect real-time images of Angelica sinensis leaves and input them into the trained pest and disease classification model. According to the model output, the growth status of Angelica sinensis is judged, and different instructions are issued according to different growth status.

[0069] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0070] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0071] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0072] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A seedling management method for high-yield direct seeding of annual Angelica sinensis, characterized in that: The specific steps include: The plot of land to be directly seeded is divided into several sub-areas of equal area, soil at different depths is randomly selected in each sub-area for sampling and analysis, and soil nutrient parameters of the sub-area are obtained, wherein the soil nutrient parameters include soil pH value, nitrogen, phosphorus and potassium concentrations, and soil parameter thresholds are set, and soil improvement is performed on the sub-area in combination with the soil nutrient parameters and the soil parameter thresholds; The plot to be directly seeded is divided into m sub-areas of equal area, and a mark point is randomly set in each of the five depth ranges in the sub-area for soil sampling, and the pH value of the sampled soil is obtained based on the pH electrode determination method, the nitrogen concentration of the sampled soil is obtained based on the Kjeldahl reagent method, the phosphorus concentration of the sampled soil is obtained based on the chromatography method, and the potassium concentration of the sampled soil is obtained based on the flame photometry method. The average value of the five sampled soils is used as the soil pH value, nitrogen, phosphorus and potassium concentration of the sub-area, and the depth ranges include: 0-10 cm underground, 11-20 cm underground, 21-30 cm underground, 31-40 cm underground, and 41-50 cm underground; The logic for setting soil parameter thresholds and combining soil nutrient parameters and soil parameter thresholds to improve soil in sub-areas is as follows: The soil parameter thresholds include soil pH threshold, soil nitrogen concentration threshold, phosphorus concentration threshold and potassium concentration threshold. When the detected soil pH value is less than or equal to the soil pH threshold, yz When soil pH is adjusted, lime is used, and the amount of lime used is calculated based on the formula: In the formula, G is the amount of lime, β is the adjustment coefficient, T is the soil pH value of the sub-region, A is the land area of ​​the sub-region; When the concentrations of soil nitrogen, phosphorus and potassium are less than or equal to the soil nitrogen concentration threshold, phosphorus concentration threshold and potassium concentration threshold, the corresponding fertilizer is applied to the soil, and the formula for calculating the fertilizer amount is: In the formula, F is the nitrogen fertilizer application amount, ε is the soil nitrogen concentration threshold, N is the soil nitrogen concentration, E is the fertilizer nitrogen concentration, S d is the soil depth to be fertilized, X N is the soil absorption coefficient of nitrogen; for land with phosphorus and potassium content less than or equal to the phosphorus content threshold and potassium content threshold, the same method as that for improving nitrogen content is adopted; Collect soil environmental parameters, combine with the seed diameter data of Angelica sinensis, calculate the sowing depth, determine the planting row spacing and column spacing of plants according to the average effective illumination area of ​​each seedling in each sub-area, and sow in the improved soil based on the calculated sowing depth, planting row spacing and column spacing. The soil environmental parameters include the average soil temperature and average humidity. During the seedling growth process, irrigation is carried out according to the seedling growth height and real-time monitoring of soil humidity data; Sowing was carried out in the improved soil. According to the soil environmental parameters and the seed diameter data of Angelica sinensis, the formula for calculating the sowing depth was as follows: Where D is the sowing depth, D base Indicates the basic sowing depth, k 1 and k 2 Represents humidity adjustment factor and temperature adjustment factor respectively H opt and T opt are the optimum soil moisture and optimum soil temperature, respectively. H and T represent the average soil moisture and average soil temperature, respectively. S is the seed diameter; The row and column spacing of plants are determined according to the average effective illumination area of ​​each seedling in each sub-region. The specific logic is as follows: randomly select several moments in a day to record the effective illumination area of ​​all Angelica sinensis in the sub-region and the row and column spacing of planting. The effective illumination area is the area of ​​sunlight falling on the leaves of Angelica sinensis. Based on the effective illumination area of ​​all Angelica sinensis in the sub-region at each moment recorded, the average value is calculated as the effective illumination area of ​​all Angelica sinensis in the sub-region within the time range of one day. According to the effective illumination area of ​​all Angelica sinensis, the average effective illumination area of ​​a single Angelica sinensis is calculated, wherein the row and column spacing of the calibrated plants are respectively d 0 and r 0 , obtain the average effective illumination area of ​​a single Angelica sinensis plant under the planting row spacing and row spacing through simulation experiments, set the average effective illumination area threshold, mark the planting row spacing and row spacing combinations corresponding to the average effective illumination area threshold that are greater than or equal to the average effective illumination area threshold, generate a preferred planting position set, select the combination with the smallest product of the planting row spacing and row spacing in the preferred planting position set as the optimal planting row spacing and row spacing, and plant according to the optimal planting row spacing and row spacing; The logic of real-time monitoring of soil moisture for irrigation is: when the soil moisture is lower than the humidity threshold, the irrigation system is automatically turned on to ensure that the soil remains within an appropriate humidity range. The calculation formula for each irrigation amount is: In the formula, Z is the irrigation amount, R is the irrigation depth, φ represents irrigation efficiency, where HR and HR Represents humidity threshold and real-time soil humidity respectively; irrigation depth R The calculation is based on the seedling growth height, and the specific formula is: In the formula, ρ is the root conversion coefficient, gd is the growth height of the seedlings; collect the images of the leaves of the Danggui with normal growth and the images of the leaves under the influence of different pests and diseases, establish a pest and disease classification model based on the SVM model, add labels to different images, specifically, mark the images of the leaves of the Danggui with normal growth as normal images, mark the images of the leaves under the influence of pests and diseases with the types of pests and diseases, use the images with labels as the input of the pest and disease classification model, and use the image labels as labels to train the pest and disease classification model; Real-time images of Angelica sinensis leaves are collected and input into the trained pest and disease classification model. Based on the model output, the growth status of the Angelica sinensis is judged, and different instructions are issued according to different growth statuses.

2. A seedling management method for high-yield direct seeding of annual Angelica sinensis according to claim 1, characterized in that: A pest and disease classification model was established based on the SVM model. The LabelImg tool was used to add labels to the image through manual labeling, and the status of the leaves was marked on the image. The labeled image was input into the SVM model, and the image label was used as the label to train the pest and disease classification model. The trained model took the image of Danggui leaves as input and the label of the image as output. Specifically, the image of normally growing leaves was output as normal, and the image of leaves with pests and diseases was output as the type of pest and disease.

3. A seedling management method for high-yield direct seeding of annual Angelica sinensis according to claim 2, characterized in that: The logic for issuing different instructions according to different growth states is as follows: when the current growth state of the cloud angelica is judged to be normal, a safety instruction is issued without any processing; when the current growth state of the cloud angelica is judged to be suffering from diseases and pests, a warning instruction is issued, the type of diseases and pests is output, and farmers are reminded to take preventive measures according to the diseases and pests.

4. A seedling management system for high-yield direct seeding of annual Angelica sinensis, characterized in that: The high-yield direct seeding management system for angelica sinensis is used to implement the high-yield direct seeding management method for angelica sinensis according to any one of claims 1 to 3, comprising: The land improvement module is used to divide the plot of land planned for direct seeding into several sub-areas of equal area, randomly select soil of different depths in each sub-area for sampling and analysis, obtain soil nutrient parameters of the sub-area, and the soil nutrient parameters include soil pH value, nitrogen, phosphorus and potassium concentrations, set soil parameter thresholds, and improve the soil in the sub-area in combination with soil nutrient parameters and soil parameter thresholds; The planting optimization module is used to collect soil environmental parameters, calculate the sowing depth in combination with the diameter data of the angelica sinensis seeds, determine the planting row and column spacing of the plants according to the light intensity in each sub-area, and sow in the improved soil based on the calculated sowing depth, planting row and column spacing. The soil environmental parameters include the average soil temperature and average humidity. During the seedling growth process, irrigation is performed according to the seedling growth height and real-time monitoring of soil humidity data; The pest and disease model training module is used to collect the images of the leaves of Danggui growing normally and the images of the leaves under the influence of different pests and diseases, establish the pest and disease classification model based on the SVM model, add labels to different images, specifically, mark the images of the leaves of Danggui growing normally as normal images, mark the types of pests and diseases in the images of the leaves under the influence of pests and diseases, use the images with labels as the input of the pest and disease classification model, and use the labels of the images as labels to train the pest and disease classification model; The pest and disease identification module is used to collect real-time images of Angelica sinensis leaves and input them into the trained pest and disease classification model. According to the model output, the growth status of Angelica sinensis is judged, and different instructions are issued according to different growth status.

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