Method and system for cloud intelligent management of crop root water, fertilizer and soil properties

Abstract

The present invention provides a method and system for cloud intelligent management of water, fertilizer and soil properties of crop roots, wherein the method comprises the following steps: obtaining growth environment information of crop roots in a health management area; determining growth environment interference items of crop roots in a health management area; using an interference item disturbance degree algorithm to determine the priority and interference degree of growth environment interference items according to growth environment information; regulating and managing the growth environment of crop roots according to the priority and interference degree of growth environment interference items. The present invention manages the most important rhizosphere part of plant nutrition and material exchange through four aspects of plant water, fertilizer, gas and medicine, so as to achieve the effect of 1% rhizosphere management>100% whole field management, which can not only realize the automation of plant health management, but also save control management costs and improve production management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a method for performing cloud intelligent management on water, fertilizer and soil properties at the roots of crops and a system for performing cloud intelligent management on water, fertilizer and soil properties at the roots of crops. Background Art

[0002] With the rapid advancement of science and technology, the great changes in modern agricultural automation, informatization, and standardization have brought about the improvement of agricultural productivity, and the development of agricultural automation is the concrete manifestation of scientific and technological progress. However, agriculture is a complex system, and not all subdivided agricultural fields have kept up with the historical trend in the process of automation, such as the fruit planting industry. The degree of mechanization in my country's orchards is still in its infancy. Although the research and development of orchard mechanization and intelligence is very fast, due to the complexity of the orchard itself and the dependence on agronomic experience, the popularity of orchard automation is not high at present, and orchard management operations are still mainly performed manually, which greatly reduces the production efficiency of the fruit industry. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method for cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops. The rhizosphere, which is the most important part for plant nutrition and material exchange, is managed through the four aspects of plant water, fertilizer, gas and medicine use, so as to achieve the effect of 1% rhizosphere management > 100% whole-field management. This method can not only realize the automation of plant health management, but also save control management costs and improve production management efficiency.

[0004] The technical solution adopted by the present invention is as follows:

[0005] A method for cloud intelligent management of water, fertilizer and soil properties of crop roots, comprising the following steps: obtaining growth environment information of crop roots in a health management area; determining growth environment interference items of crop roots in the health management area; using an interference item disturbance degree algorithm to determine the priority and interference degree of the growth environment interference items according to the growth environment information; and regulating and managing the growth environment of the crop roots according to the priority and interference degree of the growth environment interference items.

[0006] According to an embodiment of the present invention, the growth environment information includes soil temperature, soil moisture, soil oxygen concentration, soil pH value, soil EC value and soil respiration rate.

[0007] According to one embodiment of the present invention, the growth environment interference items include irrigation water amount, fertilizer amount, oxygen addition amount and added microorganism amount.

[0008] According to one embodiment of the present invention, the interference term disturbance degree algorithm is:

[0009]

[0010] Among them, x i represents the interference degree of the ith growth environment interference term, f i,j () represents the functional relationship between each growth environment interference term and the growth environment information, y' j Represents the initial value of the j-th growth environment information, y" j represents the target value of the jth growth environment information, a j represents the weight of the j-th growth environment information difference, and δ represents the weighted difference of the overall growth environment information.

[0011] According to an embodiment of the present invention, the priority of the growth environment interference item is determined by the weight.

[0012] According to an embodiment of the present invention, the interference degree of the growth environment interference item is determined by calculating the minimum value of the overall weighted difference.

[0013] According to one embodiment of the present invention, the soil microbial biomass is characterized by the soil respiration rate. Specifically, the model in which the soil respiration rate characterizes the soil microbial biomass is:

[0014]

[0015] Where W represents soil microbial biomass, h represents monitoring time, and W h0 represents the initial soil microbial biomass, R represents the intrinsic growth rate, SRV represents the soil total respiration rate, and SRVR represents the soil root respiration rate.

[0016] According to one embodiment of the present invention, x n Obtained by the following formula:

[0017] x n =εQ i ÷(εQ i ÷y i )

[0018] Among them, Q i represents the weight of the i-th growth environment information, y i Represents the available value of the i-th growth environment information.

[0019] According to one embodiment of the present invention,

[0020] y i Obtained by the following formula:

[0021] y i =WPF i ÷WPF s

[0022] Among them, WPF i Indicates the current value of the i-th growth environment information, WPF s represents the optimum value of the i-th growth environment information;

[0023] Q i Obtained by the following formula:

[0024]

[0025] Among them, Y i 0.5 It indicates the value when the actual growth rate of soil microorganisms is half of the maximum growth rate.

[0026] A system for cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops, comprising: an acquisition module, the acquisition module is used to acquire growth environment information of crop roots in a health management area; an input module, the input module is used to input growth environment interference items for determining the growth environment of crop roots in the health management area; a processing module, the processing module is used to determine the priority and interference degree of the growth environment interference items according to the growth environment information using an interference item disturbance degree algorithm; and an adjustment module, the adjustment module is used to adjust and manage the growth environment of the crop roots according to the priority and interference degree of the growth environment interference items.

[0027] Beneficial effects of the present invention:

[0028] The present invention manages the rhizosphere, which is the most important part for plant nutrition and material exchange, by adjusting the four aspects of plant water use, fertilizer use, gas use and drug use, so as to achieve the effect of 1% rhizosphere management > 100% whole field management. It can not only realize the automation of plant health management, but also save control management costs and improve production management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flowchart of a method for performing cloud intelligent management of water, fertilizer and soil properties at the roots of crops according to an embodiment of the present invention;

[0030] Figure 2 This is a block diagram of a system for performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Figure 1 The present invention is a flowchart of a method for performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops according to an embodiment of the present invention.

[0033] like Figure 1 As shown, the method for performing cloud intelligent management of crop root water, fertilizer and soil properties according to an embodiment of the present invention includes the following steps:

[0034] S1, obtaining the growth environment information of the crop roots in the health management area.

[0035] Specifically, the growth environment information of the roots of crops in the health management area can be obtained through the data sensing device. Among them, the growth environment information may include soil temperature, soil moisture, soil oxygen concentration, soil pH value, soil EC value and soil respiration rate, and the data sensing device may include a soil temperature sensor, a soil moisture sensor and a soil water dissolved oxygen sensor, a soil pH value sensor, a soil EC value sensor and a soil respiration rate sensor, which can be used to obtain the soil temperature, soil moisture, soil oxygen concentration, soil pH value, soil EC value and soil respiration rate of the roots of crops in the health management area.

[0036] S2, determine the growth environment interference items of crop roots in the health management area.

[0037] Specifically, the growth environment interference items of crop roots in the health management area can be determined based on the prior knowledge of crop root health management. For example, it can be determined that the growth environment interference items include the amount of irrigation water, the amount of fertilizer, the amount of oxygen added, and the amount of added microorganisms.

[0038] S3, using an interference item disturbance degree algorithm to determine the priority and interference degree of the growth environment interference item according to the growth environment information.

[0039] Specifically, the disturbance degree algorithm of the interference term can be:

[0040]

[0041] Among them, x i represents the interference degree of the ith growth environment interference term, f i，j () represents the functional relationship between each growth environment interference term and the growth environment information, y' j Represents the initial value of the j-th growth environment information, y"j represents the target value of the jth growth environment information, a j represents the weight of the j-th growth environment information difference, and δ represents the weighted difference of the overall growth environment information.

[0042] More specifically, the establishment process of the interference term disturbance degree algorithm of the embodiment of the present invention is as follows:

[0043] First, the initial value of the growth environment information obtained by the data sensing device, that is, each sensor, can be set to:

[0044] y' i , y'2, y'3, y'4...y' m (m∈N)

[0045] Wherein, m represents the number of sensors, and N represents an integer greater than or equal to zero.

[0046] Furthermore, the interference mode of the growth environment interference items, such as watering, fertilizing, oxygenation, medication, etc., can be set as follows:

[0047] x1, x2, x3, x4...x n (n∈N)

[0048] Wherein, n represents the type of interference mode.

[0049] Furthermore, the functional relationship between each interference mode and the growth environment information can be set as:

[0050] Δ(y1, y2...y j ) = f i，j (x1, x2, ..., x i )(i, j∈N, 0<i≤n, 0<j≤m)

[0051] Among them, each interference method has more than one interference item. For example, watering and irrigation have two interference items: irrigation water volume and oxygen addition volume.

[0052] Furthermore, the growth environment information obtained after each interference operation can be set as:

[0053]

[0054] Furthermore, the target value of the growth environment information after each interference operation can be set as:

[0055] y”1,y”2,y”3,y”4....y” j (j∈N);

[0056] Therefore, the difference between the initial value and the target value of each growth environment information is:

[0057] δ i =|y" j -y j |.

[0058] Furthermore, the importance of different growth environment information can be determined based on prior knowledge, and corresponding weights can be set for each type of growth environment information based on the importance to obtain an overall weighted difference:

[0059]

[0060] Furthermore, Substituting the above formula into the above formula, the interference term disturbance degree algorithm of the embodiment of the present invention can be obtained:

[0061]

[0062] Furthermore, the priority of the growth environment interference item can be determined by the weight of the growth environment information difference, and the interference degree, ie, the interference mode, of the growth environment interference item can be determined by calculating the minimum value of the overall weighted difference.

[0063] In addition, it should be noted that there is a correlation between the soil respiration rate of different soil types and the soil microbial biomass, activity and microbial community. Therefore, in the present invention, the soil respiration rate can be used as a representative value of the soil microbial biomass.

[0064] Specifically, the model of soil respiration rate characterizing soil microbial biomass can be:

[0065]

[0066] Where W represents soil microbial biomass, h represents monitoring time, and W h0 It represents the initial soil microbial biomass (which can be directly measured by soil sampling), R represents the intrinsic growth rate (which can be measured by rhizosphere soil microbial experiment), SRV represents the total soil respiration rate (which can be detected by soil respiration rate sensor), and SRVR represents the soil root respiration rate (which can be measured by setting a rootless zone and having the soil respiration rate sensor record the difference in soil respiration rate inside and outside the zone).

[0067] More specifically, x n It can be obtained by the following formula:

[0068] x n =εQ i ÷(εQ i ÷y i )

[0069] Among them, Q i represents the weight of the i-th growth environment information, y iRepresents the available value of the i-th growth environment information.

[0070] Furthermore, i It can be obtained by the following formula:

[0071] y i =WPF i ÷WPF s

[0072] Among them, WPF i Indicates the current value of the i-th growth environment information, WPF s Represents the optimal value of the i-th growth environment information;

[0073] Q i It can be obtained by the following formula:

[0074]

[0075] Among them, Y i 0.5 It indicates the value when the actual growth rate of soil microorganisms is half of the maximum growth rate. The available value of the growth environment information is the horizontal coordinate, and the soil microorganism growth rate is the vertical coordinate. Draw a curve graph and get the weight value Q through the above equation group. i Multi-surface approximate solution of .

[0076] In addition, it should be noted that plants cannot directly absorb organic matter through their roots. The main ways of consumption of soil organic matter are surface runoff, leaching and soil respiration. Therefore, the accuracy of detecting soil organic matter consumption through soil respiration rate in the rhizosphere soil of water-saving irrigation agricultural farmland is very high. Different soil types can determine the correlation function through experimental monitoring methods, and calculate the changes in soil organic matter content in real time.

[0077] Specifically, the simulation process of the functional relationship between soil respiration rate and soil organic matter can be as follows: determine the rhizosphere soil respiration value and soil organic matter content in the test area, with a sample size of no less than 100, and use SPSS software to perform correlation analysis, regression analysis and plot. Thus, it can be concluded that:

[0078] Linear Model:

[0079] SRV-SRVR=a+b×C÷f

[0080] Power function model:

[0081] SRV-SRVR=a×(C÷f) b

[0082] Exponential Model:

[0083] SRV-SRVR=a×e b×C÷f

[0084] Among them, a and b are conditional coefficients, C is the soil carbon content, and f is the organic matter conversion coefficient, which can be 1.724, for example. The specific value can be obtained by correlation analysis and regression analysis. By solving the equation, the organic matter content can be obtained.

[0085] S4, regulating and managing the growth environment of crop roots according to the priority and degree of interference items in the growth environment.

[0086] Specifically, the growth environment of crop roots can be regulated and managed through the smart cloud platform according to the priority and degree of interference of growth environment interference items. For example, corresponding health management control instructions can be generated through the smart cloud platform according to the priority and degree of interference of growth environment interference items, so that water, fertilizer and air can regulate the nutrient and water supply of crop roots, soil oxygen content, and soil microbial biomass through the integrated water-fertilizer management system.

[0087] In addition, the cloud-based intelligent management method for water, fertilizer and soil properties of crop roots of the present invention can also determine the priority and degree of interference of growth environment interference items according to plant species and plant growth cycle, thereby achieving healthy management of crop roots.

[0088] The beneficial effects of the present invention are as follows:

[0089] The present invention manages the most important rhizosphere part of plant nutrition and material exchange by using water, fertilizer, gas and medicine to achieve the effect of 1% rhizosphere management>100% whole field management.

[0090] The following will take the apple rhizosphere as an example to further illustrate the practical application effect of the method of the present invention for cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops.

[0091] In a specific embodiment of the present invention, taking irrigation of an apple orchard (adding water only) as an example, during the period of fertilization for the shoots, the target value of the growth environment information of the apple orchard and the weight value of each growth environment information are shown in Table 1:

[0092]

[0093] Table 1

[0094] Therefore, the functional relationship between each interference mode and the growth environment information can be determined as follows:

[0095]

[0096] Among them, x1 represents the amount of irrigation water, x2 represents the oxygen content of irrigation water, y1 represents soil moisture, y2 represents soil nitrogen content, y3 represents soil phosphorus content, y4 represents soil potassium content, y5 represents soil oxygen content in ppm, y6 represents the reduction rate of soil respiration rate, and A represents a 6×2 matrix.

[0097] Furthermore, the matrix A can be set as:

[0098]

[0099] Where y′5 is the initial value of the sensor for soil oxygen content in ppm.

[0100] The following two groups of measured values ​​can be obtained through experiments, among which the first group of measured values ​​is shown in Table 2:

[0101]

[0102] Table 2

[0103] Referring to Table 1, it can be seen that in the target value of the apple orchard growth environment information, the soil temperature should be greater than 10, so even if the soil moisture deviates from the target value, no interference operation is performed here.

[0104] The second set of measured values ​​is shown in Table 3:

[0105]

[0106] Table 3

[0107] Referring to Table 1, it can be seen that in the target value of the apple orchard growth environment information, the soil temperature should be greater than 10. Therefore, when the soil temperature is greater than 10, an interference operation, that is, an irrigation operation, can be performed.

[0108] Specifically, the measured values ​​in Table 3 can be substituted into the above formula to obtain:

[0109] Δ(y1,y2,y3,y4,y5,y6)

[0110] =(0.4x1, -0.05x1, -0.05x1, -0.05x1, 0.08(x2-12)x1, 0.08(x2-12)x1)

[0111] Furthermore, in combination with the interference term disturbance degree algorithm of the present invention, it can be obtained that:

[0112]

[0113] Furthermore, substituting Δ(y1, y2, y3, y4, y5, y6) into the equation:

[0114] δ=1×|7-0.4x1|+1.5×|0.1+0.05x1|+1.3×|0.05x1|+1.2×|0.05x1|+0.7×|0.08(12-x2)x1|+1.1×|0.06(12-x2)x1|

[0115] Furthermore, since this interference operation only involves irrigation without oxygenation, x2=12 can be obtained, which can be obtained by substituting into the above formula:

[0116] δ=1×|7-0.4x1|+1.5×|0.1+0.05x1|+1.3×|0.05x1|+1.2×|0.05x1|

[0117] Furthermore, the value of x1 is determined by calculating the minimum value of the overall weighted difference δ:

[0118] x1=17.5

[0119] That is, this interference operation can obtain the minimum δ value after irrigating 17.5 cubic meters of water into the apple orchard.

[0120] Test example

[0121] In another specific embodiment of the present invention, taking apple orchard fertilization as an example, specific fertilizer types and proportions are shown in Tables 4-6, the test site can be an orchard area, the test time is March-December, the test apple variety is Fuji, the planting row spacing is 2m×4m, the planting area is 30 mu in total, and each treatment area is about 10 mu / plot.

[0122] Specifically, three fertilization operations T1, T2, and T3 can be designed, and each fertilization operation is repeated and randomly arranged. The experimental treatments are as follows:

[0123] T1: conventional fertilization treatment: the specific fertilizer amount and nutrient content are shown in Table 4;

[0124] T2: Conventional fertilization treatment with integrated water and fertilizer: the specific fertilizer amount and nutrient content are shown in Table 5;

[0125] T3: Fertilization treatment is performed based on the cloud intelligent management method of crop root water, fertilizer and soil properties of the present invention: the specific fertilizer amount and nutrient content are shown in Table 6.

[0126]

[0127]

[0128] Table 4

[0129]

[0130]

[0131] Table 5

[0132]

[0133] Article 6

[0134] Among them, it should be noted that during conventional fertilization, the fertilizer can be evenly spread on the soil surface and then turned over by a rotary tiller; during conventional water-fertilizer integrated fertilization, the fertilizer is first dissolved and mixed, and then drip-irrigated together with irrigation water; the fertilization process based on the cloud-based intelligent management method of water, fertilizer and soil properties at the roots of crops of the present invention determines the control instructions, that is, whether to fertilize and the amount of fertilizer to be applied, through the real-time acquisition of growth environment information. For example, when it is determined that fertilization is needed, the fertilizer applicator can be started to increase the corresponding nutrients as needed. When it is determined that irrigation is needed, the infiltration irrigation system can be started to start infiltration irrigation. When it is determined that oxygenation is needed, the soil aerator can be started to start oxygenation.

[0135] The test of apple orchard was carried out based on the standards in Table 4-6, and other health management measures were ensured to remain consistent. Thus, the yield per mu, single fruit weight, firmness, soluble solids content, titratable acid content and soluble sugar content of apples could be measured during the fruit ripening period of the apple orchard. The test results are shown in Table 7.

[0136]

[0137] Table 7

[0138] Referring to Table 7, the per-acre yield of the apple orchard under the method of performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops based on the present invention is significantly higher than the per-acre yield of the apple orchard treated with T2 and T1. And compared with the apple orchards treated with T2 and T1, the single-fruit weight, hardness, soluble solids, and soluble total sugar content of apples in the apple orchard under the method of performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops based on the present invention are all improved, and the titratable acid content is significantly reduced. It can be seen that the method of performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops of the present invention can achieve healthy management of the rhizosphere of apples and achieve the purpose of keeping the whole apple plant healthy.

[0139] Corresponding to the method for performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops in the above-mentioned embodiment, the present invention also proposes a system for performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops.

[0140] like Figure 2As shown, the cloud intelligent management system for water, fertilizer and soil properties of crop roots according to the embodiment of the present invention includes an acquisition module 10, an input module 20, a processing module 30 and an adjustment module 40. Among them, the acquisition module 10 is used to acquire the growth environment information of the crop roots in the health management area; the input module 20 is used to input the growth environment interference items of the crop roots in the health management area; the processing module 30 is used to determine the priority and interference degree of the growth environment interference items according to the growth environment information by using the interference item disturbance degree algorithm; the adjustment module 40 is used to adjust and manage the growth environment of the crop roots according to the priority and interference degree of the growth environment interference items.

[0141] In one embodiment of the present invention, the acquisition module 10 can acquire the growth environment information of the roots of crops in the health management area through a data sensing device. The growth environment information may include soil temperature, soil moisture, soil oxygen concentration, soil pH value, soil EC value and soil respiration rate, and the data sensing device may include a soil temperature sensor, a soil moisture sensor and a soil water dissolved oxygen sensor, a soil pH value sensor, a soil EC value sensor and a soil respiration rate sensor, which can be used to correspondingly acquire the soil temperature, soil moisture, soil oxygen concentration, soil pH value, soil EC value and soil respiration rate of the roots of crops in the health management area.

[0142] In one embodiment of the present invention, the input module 20 can determine the growth environment interference items of the crop roots in the health management area based on the prior knowledge of crop root health management. For example, it can be determined that the growth environment interference items include the amount of irrigation water, the amount of fertilizer, the amount of oxygen added, and the amount of added microorganisms.

[0143] In one embodiment of the present invention, the processing module 30 may be provided with an algorithm for determining the disturbance degree of interference items.

[0144] Specifically, the disturbance degree algorithm of the interference term can be:

[0145]

[0146] Among them, x i represents the interference degree of the ith growth environment interference term, f i,j () represents the functional relationship between each growth environment interference term and the growth environment information, y' j Represents the initial value of the j-th growth environment information, y" j represents the target value of the jth growth environment information, a j represents the weight of the j-th growth environment information difference, and δ represents the weighted difference of the overall growth environment information.

[0147] More specifically, the establishment process of the interference term disturbance degree algorithm of the embodiment of the present invention is as follows:

[0148] First, the initial value of the growth environment information obtained by the data sensing device, that is, each sensor, can be set to:

[0149] y′1, y'2, y′3, y'4...y' m (m∈N)

[0150] Wherein, m represents the number of sensors, and N represents an integer greater than or equal to zero.

[0151] Furthermore, the interference mode of the growth environment interference items, such as watering, fertilizing, oxygenation, medication, etc., can be set as follows:

[0152] x1, x2, x3, x4...x n (n∈N)

[0153] Wherein, n represents the type of interference mode.

[0154] Furthermore, the functional relationship between each interference mode and the growth environment information can be set as:

[0155] Δ(y1, y2...y j ) = f i，j (x1, x2, ..., x i )(i, j∈N, 0<i≤n, 0<j≤m)

[0156] Among them, each interference method has more than one interference item. For example, watering and irrigation have two interference items: irrigation water volume and oxygen addition volume.

[0157] Furthermore, the growth environment information obtained after each interference operation can be set as:

[0158]

[0159] Furthermore, the target value of the growth environment information after each interference operation can be set as:

[0160] y”1,y”2,y”3,y”4....y” j (j∈N);

[0161] Therefore, the difference between the initial value and the target value of each growth environment information is:

[0162] δ i =|y" j -y j |.

[0163] Furthermore, the importance of different growth environment information can be determined based on prior knowledge, and corresponding weights can be set for each type of growth environment information based on the importance to obtain an overall weighted difference:

[0164]

[0165] Furthermore, Substituting the above formula into the above formula, the interference term disturbance degree algorithm of the embodiment of the present invention can be obtained:

[0166]

[0167] Furthermore, the priority of the growth environment interference item can be determined by the weight of the growth environment information difference, and the interference degree, ie, the interference mode, of the growth environment interference item can be determined by calculating the minimum value of the overall weighted difference.

[0168] In addition, it should be noted that there is a correlation between the soil respiration rate of different soil types and the soil microbial biomass, activity and microbial community. Therefore, in the present invention, the soil respiration rate can be used as a representative value of the soil microbial biomass.

[0169] Specifically, the model of soil respiration rate characterizing soil microbial biomass can be:

[0170]

[0171] Where W represents soil microbial biomass, h represents monitoring time, and W h0 It represents the initial soil microbial biomass (which can be directly measured by soil sampling), R represents the intrinsic growth rate (which can be measured by rhizosphere soil microbial experiment), SRV represents the total soil respiration rate (which can be detected by soil respiration rate sensor), and SRVR represents the soil root respiration rate (which can be measured by setting a rootless zone and having the soil respiration rate sensor record the difference in soil respiration rate inside and outside the zone).

[0172] More specifically, x n It can be obtained by the following formula:

[0173] x n =εQ i ÷(εQ i ÷y i )

[0174] Among them, Q i represents the weight of the i-th growth environment information, y i Represents the available value of the i-th growth environment information.

[0175] Furthermore, i It can be obtained by the following formula:

[0176] y i =WPF i ÷WPF s

[0177] Among them, WPFi Indicates the current value of the i-th growth environment information, WPF s Represents the optimal value of the i-th growth environment information;

[0178] Q i It can be obtained by the following formula:

[0179]

[0180] Among them, Y i 0.5 It indicates the value when the actual growth rate of soil microorganisms is half of the maximum growth rate. The available value of the growth environment information is the horizontal coordinate, and the soil microorganism growth rate is the vertical coordinate. Draw a curve graph and get the weight value Q through the above equation group. i Multi-surface approximate solution of .

[0181] In addition, it should be noted that plants cannot directly absorb organic matter through their roots. The main ways of consumption of soil organic matter are surface runoff, leaching and soil respiration. Therefore, the accuracy of detecting soil organic matter consumption through soil respiration rate in the rhizosphere soil of water-saving irrigation agricultural farmland is very high. Different soil types can determine the correlation function through experimental monitoring methods, and calculate the changes in soil organic matter content in real time.

[0182] Specifically, the simulation process of the functional relationship between soil respiration rate and soil organic matter can be as follows: determine the rhizosphere soil respiration value and soil organic matter content in the test area, with a sample size of no less than 100, and use SPSS software to perform correlation analysis, regression analysis and plot. Thus, it can be concluded that:

[0183] Linear Model:

[0184] SRV-SRVR=a+b×C÷f

[0185] Power function model:

[0186] SRV-SRVR=a×(C÷f) b

[0187] Exponential Model:

[0188] SRV-SRVR=a×e b×C÷f

[0189] Among them, a and b are conditional coefficients, C is the soil carbon content, and f is the organic matter conversion coefficient, which can be 1.724, for example. The specific value can be obtained by correlation analysis and regression analysis. By solving the equation, the organic matter content can be obtained.

[0190] In one embodiment of the present invention, the adjustment module 40 can adjust and manage the growth environment of the crop roots according to the priority and degree of interference of the growth environment interference items through the smart cloud platform. For example, the corresponding health management control instructions can be generated through the smart cloud platform according to the priority and degree of interference of the growth environment interference items, so that water, fertilizer, and air can regulate the nutrient and water supply of the crop roots, the soil oxygen content, and the soil microbial biomass through the water-fertilizer integrated management system.

[0191] In addition, the cloud-based intelligent management system for water, fertilizer and soil properties at the roots of crops of the present invention can also determine the priority and degree of interference of growth environment interference items according to plant species and plant growth cycle, thereby achieving healthy management of crop roots.

[0192] The beneficial effects of the present invention are as follows:

[0193] The present invention manages the most important rhizosphere part of plant nutrition and material exchange by using water, fertilizer, gas and medicine to achieve the effect of 1% rhizosphere management>100% whole field management.

[0194] The following will take the apple rhizosphere as an example to further illustrate the actual application effect of the system for cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops of the present invention.

[0195] In a specific embodiment of the present invention, taking irrigation of an apple orchard (adding water only) as an example, during the period of fertilization for the shoots, the target value of the growth environment information of the apple orchard and the weight value of each growth environment information are shown in Table 1:

[0196]

[0197] Table 1

[0198] Therefore, the functional relationship between each interference mode and the growth environment information can be determined as follows:

[0199]

[0200] Among them, x1 represents the amount of irrigation water, x2 represents the oxygen content of irrigation water, y1 represents soil moisture, y2 represents soil nitrogen content, y3 represents soil phosphorus content, y4 represents soil potassium content, y5 represents soil oxygen content in ppm, y6 represents the reduction rate of soil respiration rate, and A represents a 6×2 matrix.

[0201] Furthermore, the matrix A can be set as:

[0202]

[0203] Where y′5 is the initial value of the sensor for soil oxygen content in ppm.

[0204] The following two groups of measured values ​​can be obtained through experiments, among which the first group of measured values ​​is shown in Table 2:

[0205]

[0206] Table 2

[0207] Referring to Table 1, it can be seen that in the target value of the apple orchard growth environment information, the soil temperature should be greater than 10, so even if the soil moisture deviates from the target value, no interference operation is performed here.

[0208] The second set of measured values ​​is shown in Table 3:

[0209]

[0210]

[0211] Table 3

[0212] Referring to Table 1, it can be seen that in the target value of the apple orchard growth environment information, the soil temperature should be greater than 10. Therefore, when the soil temperature is greater than 10, an interference operation, that is, an irrigation operation, can be performed.

[0213] Specifically, the measured values ​​in Table 3 can be substituted into the above formula to obtain:

[0214] Δ(y1,y2,y3,y4,y5,y6)

[0215] =(0.4x1, -0.05x1, -0.05x1, -0.05x1, 0.08(x2-12)x1, 0.08(x2-12)x1)

[0216] Furthermore, in combination with the interference term disturbance degree algorithm of the present invention, it can be obtained that:

[0217]

[0218] Furthermore, substituting Δ(y1, y2, y3, y4, y5, y6) into the equation:

[0219] δ=1×|7-0.4x1|+1.5×|0.1+0.05x1|+1.3×|0.05x1|+1.2×|0.05x1|+0.7×|0.08(12-x2)x1|+1.1×|0.06(12-x2)x1|

[0220] Furthermore, since this interference operation only involves irrigation without oxygenation, x2=12 can be obtained, which can be obtained by substituting into the above formula:

[0221] δ=1×|7-0.4x1|+1.5×|0.1+0.05x1|+1.3×|0.05x1|+1.2×|0.05x1|

[0222] Furthermore, the value of x1 is determined by calculating the minimum value of the overall weighted difference δ:

[0223] x1=17.5

[0224] That is, this interference operation can obtain the minimum δ value after irrigating 17.5 cubic meters of water into the apple orchard.

[0225] Test example

[0226] In another specific embodiment of the present invention, taking apple orchard fertilization as an example, specific fertilizer types and proportions are shown in Tables 4-6, the test site can be an orchard area, the test time is March-December, the test apple variety is Fuji, the planting row spacing is 2m×4m, the planting area is 30 mu in total, and each treatment area is about 10 mu / plot.

[0227] Specifically, three fertilization operations T1, T2, and T3 can be designed, and each fertilization operation is repeated and randomly arranged. The experimental treatments are as follows:

[0228] T1: conventional fertilization treatment: the specific fertilizer amount and nutrient content are shown in Table 4;

[0229] T2: Conventional fertilization treatment with integrated water and fertilizer: the specific fertilizer amount and nutrient content are shown in Table 5;

[0230] T3: Fertilization is performed based on the cloud-based intelligent management system for water, fertilizer and soil properties of crop roots of the present invention: the specific fertilizer amount and nutrient content are shown in Table 6.

[0231]

[0232] Table 4

[0233]

[0234] Table 5

[0235]

[0236] Article 6

[0237] Among them, it should be noted that during conventional fertilization, the fertilizer can be evenly spread on the soil surface and then turned over by a rotary tiller; during conventional water-fertilizer integrated fertilization, the fertilizer is first dissolved and mixed, and then drip-irrigated together with irrigation water; based on the fertilization processing of the system for cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops of the present invention, the control instructions, that is, whether to fertilize and the amount of fertilizer to be applied, are determined by real-time acquired growth environment information. For example, when it is determined that fertilization is needed, the fertilizer applicator can be started to increase the corresponding nutrients as needed. When it is determined that irrigation is needed, the infiltration irrigation system can be started to start infiltration irrigation. When it is determined that oxygenation is needed, the soil aerator can be started to start oxygenation.

[0238] The test of apple orchard was carried out based on the standards in Table 4-6, and other health management measures were ensured to remain consistent. Thus, the yield per mu, single fruit weight, firmness, soluble solids content, titratable acid content and soluble sugar content of apples could be measured during the fruit ripening period of the apple orchard. The test results are shown in Table 7.

[0239]

[0240] Table 7

[0241] Referring to Table 7, the per-acre yield of the apple orchard under the system for performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops based on the present invention is significantly higher than the per-acre yield of the apple orchard treated with T2 and T1. And compared with the apple orchard treated with T2 and T1, the single-fruit weight, hardness, soluble solids, and soluble total sugar content of apples in the apple orchard under the system for performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops based on the present invention are all improved, and the titratable acid content is significantly reduced. It can be seen that the system for performing cloud-based intelligent management of water, fertilizer and soil properties at the roots of crops of the present invention can achieve healthy management of the rhizosphere of apples and achieve the purpose of keeping the whole apple plant healthy.

[0242] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0243] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0244] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0245] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

Claims

1. A method for cloud intelligent management of crop root water, fertilizer and soil properties, characterized in that: The following steps are involved: Obtain information on the growth environment of crop roots in the health management area; Identify environmental disturbances to crop roots within the health management area; Determining the priority and interference degree of the growth environment interference item according to the growth environment information by using an interference item disturbance degree algorithm; The growth environment of the crop roots is regulated and managed according to the priority and degree of interference of the growth environment interference items, The growth environment information includes soil temperature, soil moisture, soil oxygen concentration, soil pH value, soil EC value and soil respiration rate. The growth environment interference factors include irrigation water, fertilizer, oxygen and microbial addition. The interference term disturbance degree algorithm is: Among them, x i represents the interference degree of the ith growth environment interference term, f i,j () represents the functional relationship between each growth environment interference term and the growth environment information, y′ j represents the initial value of the jth growth environment information, y″ j represents the target value of the jth growth environment information, a j represents the weight of the j-th growth environment information difference, δ represents the weighted difference of the overall growth environment information, The soil microbial biomass is characterized by the soil respiration rate. Specifically, the model in which the soil respiration rate is characterized by the soil microbial biomass is: Where W represents soil microbial biomass, h i Indicates the monitoring time, W h0 represents the initial soil microbial biomass, R represents the intrinsic growth rate, SRV represents the total soil respiration rate, SRVR represents the soil root respiration rate, Among them, x n Obtained by the following formula: x n =εQ i ÷(εQ i ÷y i ) Among them, Q i represents the weight of the i-th growth environment information, y i represents the available value of the i-th growth environment information, Among them, y i Obtained by the following formula: and i =WPF i ÷WPF s Among them, WPF i Indicates the current value of the i-th growth environment information, WPF s represents the optimum value of the i-th growth environment information; Q i Obtained by the following formula: in, It indicates the value when the actual growth rate of soil microorganisms is half of the maximum growth rate.

2. The method for cloud intelligent management of crop root water, fertilizer and soil properties according to claim 1, characterized in that: The priorities of the growth environment interference items are determined by the weights.

3. The method for cloud intelligent management of crop root water, fertilizer and soil properties according to claim 1, characterized in that: The interference degree of the growth environment interference item is determined by calculating the minimum value of the overall weighted difference.

4. A cloud-based intelligent management system for crop root water, fertilizer and soil properties, characterized in that: include: An acquisition module, the acquisition module is used to acquire the growth environment information of the roots of crops in the health management area; An input module, the input module is used to input growth environment interference items of crop roots in the health management area; A processing module, the processing module is used to determine the priority and interference degree of the growth environment interference item according to the growth environment information by using an interference item disturbance degree algorithm; an adjustment module, the adjustment module being used to adjust and manage the growth environment of the crop roots according to the priority and interference degree of the growth environment interference items, The growth environment information includes soil temperature, soil moisture, soil oxygen concentration, soil pH value, soil EC value and soil respiration rate. The growth environment interference factors include irrigation water, fertilizer, oxygen and microbial addition. The interference term disturbance degree algorithm is: Among them, x i represents the interference degree of the ith growth environment interference term, f i,j () represents the functional relationship between each growth environment interference term and the growth environment information, y′ j represents the initial value of the jth growth environment information, y″ j represents the target value of the jth growth environment information, a j represents the weight of the j-th growth environment information difference, δ represents the weighted difference of the overall growth environment information, The soil microbial biomass is characterized by the soil respiration rate. Specifically, the model in which the soil respiration rate is characterized by the soil microbial biomass is: Where W represents soil microbial biomass, h i Indicates the monitoring time, W h0 represents the initial soil microbial biomass, R represents the intrinsic growth rate, SRV represents the total soil respiration rate, SRVR represents the soil root respiration rate, Among them, x n Obtained by the following formula: x n =εQ i ÷(εQ i ÷y i ) Among them, Q i represents the weight of the i-th growth environment information, y i represents the available value of the i-th growth environment information, Among them, y i Obtained by the following formula: and i =WPF i ÷WPF s Among them, WPF i Indicates the current value of the i-th growth environment information, WPF s represents the optimum value of the i-th growth environment information; Q i Obtained by the following formula: in, It indicates the value when the actual growth rate of soil microorganisms is half of the maximum growth rate.

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