Tobacco planting soil health analysis and fertilization prediction system

Abstract

The invention relates to the technical field of soil health analysis, in particular to a tobacco planting soil health analysis and fertilization prediction system. Comprising a server, a health analysis module and a prediction management module, soil state parameters are obtained through soil microorganism activity action state analysis, soil nutrient state analysis and soil moisture state analysis of tobacco planting soil, then the soil change degree is analyzed, a prediction updating instruction is generated, and comprehensive detection, analysis and evaluation of the soil are achieved. By comprehensively analyzing the state parameters of the soil, the demand change of the soil, including the opportunity and quantification of fertilization, pesticide application and irrigation, can be accurately predicted, so that the growth efficiency and yield of crops can be improved; meanwhile, through the generated prediction notice, a farmer or a manager can be guided to take corresponding measures in time, it is guaranteed that soil and crops grow in the optimal state, and malnutrition, pest and disease damage and other problems are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of soil health analysis, and in particular to a tobacco-growing soil health analysis and fertilization prediction system. Background Art

[0002] Tobacco is one of the important leaf economic crops in China. Soil conditions have a very important impact on the growth and quality of tobacco. Suitable soil conditions can promote the growth of tobacco plants, increase yields, and are conducive to the formation of tobacco quality. On the contrary, unsuitable soil conditions will limit the growth of tobacco plants, affect the nutrient absorption and metabolism of leaves, and further affect the key quality indicators of tobacco, such as nicotine content, leaf color, and taste. Therefore, the soil conditions of tobacco are particularly important during the tobacco planting process.

[0003] Traditional tobacco cultivation uses fixed-cycle soil analysis and fertilization. However, the nutrient status of the soil is affected by many factors, resulting in dynamic changes in the soil condition, including climate, precipitation, temperature, crop growth conditions, etc. Therefore, this fixed-mode soil analysis and fertilization method has time lags and is difficult to cope with dynamic soil changes and crop needs at different growth stages. Summary of the invention

[0004] The purpose of the present invention is to provide a tobacco-growing soil health analysis and fertilization prediction system to solve the problems mentioned in the above background technology.

[0005] The purpose of the present invention can be achieved by the following technical solution: a tobacco-growing soil health analysis and fertilization prediction system, the system comprising:

[0006] The health analysis module analyzes the soil microbial action status, nutrient status and water retention status based on soil information to obtain soil status parameters, and then analyzes soil status changes based on soil status parameters to generate prediction update instructions, and sends them to the prediction management module;

[0007] When the prediction management module receives the prediction update instruction, it conducts a comprehensive analysis and prediction of the soil state parameters to generate a corresponding prediction notification. The specific process is as follows:

[0008] Step 1: Retrieve soil state parameters of each monitoring point, wherein the soil state parameters include symbiotic state value, competitive state value, nutrient state value and water retention state value; calculate the mean of the symbiotic state value, competitive state value, nutrient state value and water retention state value of each monitoring point in the region and obtain the mean values ​​as the symbiotic state value, competitive state value, nutrient state value and water retention state value of the region;

[0009] Step 2: Perform predictive analysis on the symbiotic state value, competition state value, and nutrient state value of the region to obtain an adjustment demand assessment value. When the adjustment demand assessment index is greater than the maximum value in the set demand interval, a pesticide and fertilization forecast notification is generated; when the adjustment demand assessment index is within the set demand interval, the competition state value and nutrient state value at each collection time in the region are averaged to obtain the competition state mean and nutrient state mean. If the competition state mean is greater than or equal to the nutrient state mean, a pesticide application forecast notification is generated; if the competition state mean is less than the nutrient state mean, a fertilization forecast notification is generated;

[0010] Step 3: Retrieve the soil moisture of each monitoring point, calculate the average of the soil moisture at each monitoring point to obtain the average as the soil moisture of the region, and perform predictive analysis on it and the water retention status value of the region to obtain the irrigation demand assessment index. When the irrigation demand assessment index is greater than or equal to the set irrigation demand threshold, an irrigation forecast notification is generated.

[0011] Preferably, the specific process of predictively analyzing the symbiotic state value, competitive state value, and nutrient state value of the region to obtain the adjustment demand assessment value is as follows:

[0012] Set the symbiotic state value H of the region w j. Competition status value H V j. Nutrient status value H Y Substitute j into the set formula The adjustment demand index Hfj corresponding to each collection time in the region is calculated, where f1, f2, and f3 are the set proportional coefficients, and e is a natural constant;

[0013] A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the adjusted demand index as the vertical coordinate. The adjusted demand index is input into the coordinate axis according to the corresponding collection time, and the position of the adjusted demand index in the coordinate axis is recorded as the demand point. The demand points are connected in sequence using a smooth curve to obtain a curve relationship diagram of the adjusted demand index changing with time; a tangent line is drawn at the demand point, and the slope of the tangent line is calculated to obtain the adjusted slope of each demand point; the adjusted slopes greater than zero are summed up to obtain the demand increase, which is recorded as X1, and the adjusted slopes less than zero are summed up to obtain the demand decrease, which is recorded as X2;

[0014] Substitute the adjusted demand index Hfj, demand increase X1 and demand decrease X2 into the set formula The adjustment demand assessment value Xf is calculated, where f4 and f5 are respectively

[0015] is the set proportional coefficient.

[0016] Preferably, the specific process of predictive analysis of the soil moisture of a region and the water retention status value of the region is as follows:

[0017] The soil moisture Lj of the region is related to the water retention state value H S Substitute j into the set formula Calculate to obtain the irrigation demand index Lfj, where f6 and f7 are respectively the set proportional coefficients; establish a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the irrigation demand index as the vertical coordinate, input the irrigation demand index into the coordinate axis according to the corresponding collection time, and record the position of the irrigation demand index in the coordinate axis as the irrigation point, draw a tangent line at the irrigation point, calculate the slope of the tangent line to obtain the irrigation slope of each irrigation point, sum the irrigation slopes greater than zero to obtain the monotonic increase of irrigation demand and record it as X3, and sum the irrigation slopes less than zero and take the absolute value to obtain the monotonic decrease of irrigation demand and record it as X4;

[0018] Substitute the irrigation demand index Lfj, the monotonic increase in irrigation demand X3, and the monotonic decrease in irrigation demand X4 into the set formula The irrigation demand assessment index LX is calculated, where f8 and f9 are the set proportional coefficients.

[0019] Preferably, the specific process of generating the prediction update instruction is as follows:

[0020] 401: Divide the tobacco planting area into several areas, set several monitoring points in each area, and use non-destructive testing technology and high-throughput testing technology to test the soil at each monitoring point to obtain soil information at each sampling time corresponding to each monitoring point in the area; the soil information includes soil texture type, soil moisture, various nutrient contents, organic matter content, ambient temperature, ambient humidity, light intensity, microbial community types, and gene abundance and metabolite content of each microbial community type;

[0021] 402: Retrieving the biomass of microorganisms, microorganism species, and gene abundance and metabolite content corresponding to each microorganism species at each monitoring point, and analyzing the soil microbial activity state of the soil based on the biomass, and obtaining the symbiotic state value and competitive state value of each monitoring point; wherein the microbial activity state analysis includes symbiotic activity analysis and competitive activity analysis;

[0022] 403: Retrieve the content of various nutrients in the soil of each monitoring point, wherein the nutrient types include but are not limited to nitrogen, phosphorus, potassium, etc., obtain the current growth cycle of the set tobacco, set different growth cycles corresponding to different important proportions of various nutrients, compare the current growth cycle of the tobacco with all set growth cycles to match the corresponding important proportions of various nutrients; multiply the content of various nutrients by the corresponding important proportions to obtain the growth value of various nutrients, and sum them to obtain the nutrient status value of each monitoring point;

[0023] 404: Compare the soil texture of the monitoring point with all the set soil texture types to match the corresponding soil water retention base value recorded as Ri, where i = 1, 2, 3...I, I values ​​are all positive integers, I represents the total number of monitoring points in the area, and i represents the sequence number of any monitoring point in the area; retrieve the water retention base Ri, organic matter content Qi j, ambient temperature Ti j, ambient humidity Si j and light intensity Gi j of each monitoring point and substitute them into the set formula Calculate the soil water retention value H S i, where c1, c2, c3 are the set proportional coefficients, UT, US and UG are the optimal ambient temperature, optimal ambient humidity and optimal ambient light intensity respectively; thus the water retention status value of each monitoring point can be obtained;

[0024] 405: The symbiosis state value, competition state value, nutrient state value and water retention state value of each monitoring point are recorded as soil state parameters, and soil state change analysis is performed based on the soil state parameters to generate prediction update instructions.

[0025] Preferably, the specific process of symbiotic activity analysis is:

[0026] The beneficial microorganisms corresponding to the tobacco species planted in the area were compared with all the microorganisms detected at the monitoring points to obtain the target microbial species and their corresponding biomass carbon and biomass nitrogen, which were recorded as BH C and BH N ; Compare the target microbial species with the monitored microorganisms to obtain the gene abundance and metabolite content corresponding to each target microbial species, and record them as A1 and A2 respectively; Set a value coefficient for each target microbial species planted in the tobacco area, and the value coefficient corresponding to each target microorganism is recorded as γ1. The symbiotic effect value Aa corresponding to each target microorganism is calculated, where a1, a2, a3, and a4 are the set proportional coefficients, e is a natural constant, and H C and H N are the biomass carbon and biomass nitrogen of all microorganisms at the monitoring site;

[0027] Compare and analyze the symbiotic effect value with the set symbiotic interval to divide the symbiotic effect value into a primary effect value, a secondary effect value, and a tertiary effect value, and count the number of the primary effect value, the secondary effect value, and the tertiary effect value, and record them as Z1, Z2, and Z3 respectively; sum the primary effect value, the secondary effect value, and the tertiary effect value to obtain the primary effect total value, the secondary effect total value, and the tertiary effect total value, and record them as Z4, Z5, and Z6 respectively;

[0028] Substitute Z1, Z2, Z3, Z4, Z5 and Z6 into the given formula Calculate the symbiotic state value H of the monitoring point w , where b1, b2, and b3 are the set proportional coefficients respectively, from which the symbiotic state values ​​of each monitoring point in the area can be obtained.

[0029] Preferably, the specific process of competitive activity analysis is:

[0030] The different harmful microorganisms corresponding to the tobacco species planted in the area are compared with all the microorganisms detected at the monitoring points to obtain the biomass carbon and biomass nitrogen corresponding to each harmful microorganism species, and they are recorded as YH C and YH N ; Set a hazard coefficient for each type of harmful microorganism planted in the tobacco planting area, and the hazard coefficient corresponding to the harmful microorganism is recorded as γ2; compare the types of harmful microorganisms with all the monitored microorganisms to obtain the gene abundance and metabolite content corresponding to each type of harmful microorganism, and record them as B1 and B2 respectively, and use the set formula The competitive action value is obtained, where a5, a6, a7, and a8 are respectively the set proportional coefficients;

[0031] Compare and analyze the competitive effect value with the set competitive interval to divide the competitive effect value into a primary competitive value, a secondary competitive value, and a tertiary competitive value; count the number of primary competitive values, secondary competitive values, and tertiary competitive values, and record them as P1, P2, and P3, respectively; sum the primary competitive value, secondary competitive value, and tertiary competitive value to obtain the primary competition total value, the secondary competition total value, and the tertiary competition total value, and record them as P4, P5, and P6, respectively;

[0032] Substitute P1, P2, P3, P4, P5 and P6 into the given formula Get the competition state value H of the monitoring point V , where b4, b5, and b6 are the set proportional coefficients respectively; thus, the competition state value of each monitoring point can be obtained.

[0033] Preferably, the specific process of soil state change analysis is:

[0034] The symbiotic state value H of each monitoring point w ij, competition state value H V ij, nutrient status value H Y ij and water retention value H S Substitute ij into the set formula The state evaluation index HZj of the region is calculated, where c4, c5, c6, and c7 are the set proportional coefficients respectively;

[0035] A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the state assessment value as the vertical coordinate. The state assessment index is input into the coordinate system according to the corresponding collection time, and the position of the state assessment index in the coordinate axis is recorded as the assessment point. The assessment points are connected in sequence using a smooth curve to obtain a curve relationship diagram of the state assessment index changing with time; a tangent line is drawn at the assessment point, and the tangent expression is obtained by data fitting. The evaluation derivative of the assessment point is obtained by derivation and recorded as Mj; wherein j = 1, 2, 3...J, J is a positive integer, J represents the total number of collection moments, and j represents the sequence number of any collection moment; the evaluation derivatives greater than zero are summed up to obtain a state monotonic increase, recorded as N1, and the evaluation indexes less than zero are summed up and the absolute value is taken to obtain a state monotonic decrease, recorded as N2; the number of evaluation derivatives equal to zero is recorded as N3; using the set formula The state change index Nd of the region is calculated, where d1, d2, d3, and d4 are the set proportional coefficients. When , d5 takes an even number; when When , d5 takes the value of even zero; when When , d5 takes an odd value, so we can see that when When

[0036] When the state change index is greater than or equal to the set change threshold, the symbiosis state value, competition state value, nutrient state value and water retention state value of each monitoring point are recorded as soil state parameters, and the soil state parameters are integrated into prediction update instructions and sent to the prediction management module.

[0037] Beneficial effects of the present invention:

[0038] 1. Through the analysis of soil microbial activity, soil nutrient status and soil moisture status of tobacco-growing soil, soil status parameters are obtained, and then the degree of soil change is analyzed and prediction and update instructions are generated to achieve comprehensive soil detection, analysis and evaluation, providing detailed information on soil status and providing an important basis for soil management of tobacco planting;

[0039] 2. Through comprehensive analysis of soil state parameters, it is possible to accurately predict changes in soil demand, including the timing and quantification of fertilization, pesticide application and irrigation, which helps to improve the efficiency and yield of crop growth; at the same time, through the generated forecast notifications, it can guide farmers or managers to take corresponding measures in a timely manner to ensure that the soil and crops grow in the best condition and avoid the occurrence of problems such as malnutrition, diseases and pests; this precise management method helps to improve the efficiency and sustainability of crop planting and promote the modernization and intelligent development of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below in conjunction with the accompanying drawings.

[0041] Figure 1 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0042] 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.

[0043] See also Figure 1 As shown, the present invention is a tobacco-growing soil health analysis and fertilization prediction system, which includes: a server, a health analysis module and a prediction management module;

[0044] The server stores different types of beneficial microorganisms and harmful microorganisms corresponding to different types of tobacco, different important proportions of nutrients corresponding to different growth cycles, and the optimal environmental conditions corresponding to each growth cycle of tobacco;

[0045] The health analysis module dynamically monitors the soil condition and environmental status to determine the changing state of the tobacco planting environment, and extracts soil state parameters to integrate them into fertilization update instructions; specifically:

[0046] The tobacco planting area is divided into several areas, each of which is equipped with several monitoring points. The soil at each monitoring point is tested using non-destructive testing technology and high-throughput testing technology to obtain soil information at each sampling time corresponding to each monitoring point in the area; the soil information includes soil texture type, soil moisture, various nutrient contents, organic matter content, ambient temperature, ambient humidity, light intensity, microbial community types, and gene abundance and metabolite content of each microbial community type;

[0047] Analysis of soil microbial activity status:

[0048] The biomass of microorganisms, microbial species, gene abundance and metabolite content corresponding to each microbial species at each monitoring point were retrieved. The biomass of microorganisms includes biomass carbon and biomass nitrogen, which are recorded as H C and H N; Set different target microbial species corresponding to different types of tobacco, compare the tobacco species in the area with all the set tobacco species to obtain the corresponding target microbial species; the target microorganism refers to the microorganism that has a symbiotic relationship with the planted tobacco among all the microorganisms detected at the monitoring point; it should be noted that according to the principle of plant-soil microbial interaction, the same microorganism can have different effects on different plants. This is because the differences in chemical substances secreted by the roots of different plants and the root structure will affect the way of interaction with microorganisms. For the same microorganism, it has a symbiotic relationship (beneficial relationship) with a certain plant. For example, some microorganisms promote plant growth by fixing nitrogen or providing nutrients when in contact with the roots of plants; but for another plant, there may be a competitive relationship (harmful relationship), resulting in plant growth inhibition or disease induction;

[0049] The different beneficial microorganisms corresponding to the tobacco species planted in the area were compared with all the microorganisms detected at the monitoring points to obtain the biomass carbon and biomass nitrogen corresponding to each target microbial species, and they were recorded as BH C and BH N ;

[0050] The target microbial species are compared with the monitored microorganisms to obtain the gene abundance and metabolite content corresponding to each target microbial species, and they are recorded as A1 and A2 respectively; it should be noted that gene abundance refers to the number or abundance of specific genes contained in each microbial species in the soil, which is used to evaluate the diversity, abundance and community structure of soil microorganisms; gene abundance reflects the structure and composition of the microbial community, while the metabolite content reflects the activity and function of the microorganisms;

[0051] Different target microorganisms have different positive effects on tobacco planted in the region. Therefore, different target microorganisms for tobacco planted in the region are set to correspond to a value coefficient. The larger the value coefficient, the greater the positive effect of the target microorganism corresponding to the value coefficient on tobacco planted in the region, and the greater the importance. Therefore, the value coefficient corresponding to each target microorganism is recorded as γ1; using the set formula Calculate and obtain the symbiotic effect value Aa corresponding to each target microorganism, where a1, a2, a3, and a4 are the set proportional coefficients, and e is a natural constant; compare and analyze the symbiotic effect value with the set symbiotic interval. When the symbiotic effect value is greater than the maximum value in the set symbiotic interval, the symbiotic effect value is recorded as the first-level effect value; when the symbiotic effect value is within the set symbiotic interval, the symbiotic effect value is recorded as the second-level effect value; when the symbiotic effect value is less than the minimum value in the set symbiotic interval, the symbiotic effect value is recorded as the third-level effect value; count the number of first-level effect values, second-level effect values, and third-level effect values, and record them as Z1, Z2, and Z3, respectively; sum the first-level effect value, second-level effect value, and third-level effect value, respectively, to obtain the first-level effect total value, second-level effect total value, and third-level effect total value, and record them as Z4, Z5, and Z6, respectively; use the set formula Calculate the symbiotic state value H of the monitoring point w , where b1, b2, and b3 are the set proportional coefficients respectively; the symbiotic state value of each monitoring point in the region can be obtained and recorded as H w ij; where i = 1, 2, 3 ... I, j = 1, 2, 3 ... J, I and J are both positive integers, I represents the total number of monitoring points in the area, J represents the total number of collection moments, i represents the sequence number of any monitoring point in the area, and j represents the sequence number of any collection moment;

[0052] Different types of tobacco are set to correspond to different types of harmful microorganisms, and the tobacco types in the area are compared with all the set tobacco types to obtain the corresponding harmful microorganism types; the harmful microorganisms refer to the microorganisms that compete with the planted tobacco among all the microorganisms detected at the monitoring point, and the different harmful microorganisms corresponding to the tobacco types planted in the area are compared with all the microorganisms detected at the monitoring point to obtain the biomass carbon and biomass nitrogen corresponding to each harmful microorganism type, and they are recorded as YH C and YH N Different types of harmful microorganisms have different negative effects on tobacco planted in the region. Therefore, different types of harmful microorganisms planted in the region are set to correspond to a hazard coefficient. The larger the hazard coefficient, the greater the negative effect of the harmful microorganism corresponding to the value coefficient on tobacco planted in the region, and the greater the degree of hazard. The hazard coefficient corresponding to the harmful microorganism is recorded as γ2. The types of harmful microorganisms are compared with all the monitored microorganisms to obtain the gene abundance and metabolite content corresponding to each type of harmful microorganism, and they are recorded as B1 and B2 respectively. The set formula The competitive action value is obtained, wherein a5, a6, a7, and a8 are respectively set proportional coefficients; the competitive action value is compared and analyzed with the set competitive interval, when the competitive action value is greater than the maximum value in the set competitive interval, the competitive action value is recorded as the first-level competitive value; when the competitive action value is within the set competitive interval, the competitive action value is recorded as the second-level competitive value; when the competitive action value is less than the minimum value in the set competitive interval, the competitive action value is recorded as the third-level competitive value; the number of first-level competitive values, second-level competitive values, and third-level competitive values ​​is counted respectively, and they are recorded as P1, P2, and P3 respectively; the first-level competitive value, the second-level competitive value, and the third-level competitive value are summed up to obtain the first-level competition total value, the second-level competition total value, and the third-level competition total value, and they are recorded as P4, P5, and P6 respectively; using the set formula Get the competition state value H of the monitoring point V , where b4, b5, and b6 are the set proportional coefficients respectively; thus, the competition state value of each monitoring point is recorded as H V ij;

[0053] Analysis of soil nutrient status:

[0054] The content of various nutrients in the soil of each monitoring point is retrieved, where the nutrient types include but are not limited to nitrogen, phosphorus, potassium, etc., the current growth cycle of the set tobacco is obtained, different growth cycles are set to correspond to different important proportions of each nutrient, and the current growth cycle of the tobacco is compared with all the set growth cycles to match the corresponding important proportions of each nutrient; the content of each nutrient is multiplied by the corresponding important proportion to obtain the growth value of each nutrient, and the sum is calculated to obtain the nutrient status value of each monitoring point, which is recorded as H Y ij;

[0055] It should be noted that tobacco usually has four growth cycles, namely the seedling stage, the strong seedling stage, the flowering stage and the mature stage; the demand for each nutrient is different in different growth cycles. For example, nitrogen plays a key role in improving the quality of tobacco leaves and increasing yield. In the strong seedling stage and the flowering stage, tobacco has a greater demand for nitrogen; phosphorus is an important element for plant growth and development, which is beneficial to promoting root growth and improving tobacco's stress resistance. In the seedling stage and the strong seedling stage, tobacco has a greater demand for phosphorus; potassium is an important element for regulating plant osmotic pressure and activating enzymes, which is helpful for improving tobacco's disease resistance and increasing yield. The demand for potassium is greater in the mature stage; it can be seen that when tobacco is in the mature stage, the important proportion of nutrient potassium is relatively large, and when tobacco is in the flowering stage, the important proportion of nutrient nitrogen is relatively large; therefore, the demand for each nutrient is different in different growth cycles, and there are different important proportions of each nutrient;

[0056] Analysis of soil moisture status:

[0057] The soil texture type, organic matter content, ambient temperature, ambient humidity, and light intensity of each monitoring point were retrieved and recorded as Qij, Tij, Sij, and Gij, respectively. Different soil textures were set to correspond to different soil water retention base values, and the soil texture of the monitoring point was compared with all the set soil texture types to match the corresponding soil water retention base value, which was recorded as Ri. It should be noted that different soil textures have different water retention capacities. For example, sandy loam has a poor water retention capacity, while loam and clay loam have a good water retention capacity. The higher the organic matter content in the soil, the better the soil water retention capacity. Using the set formula Calculate the soil water retention value H S i, where c1, c2, c3 are the set proportional coefficients, UT, US and UG are the optimal ambient temperature, optimal ambient humidity and optimal ambient light intensity respectively; thus, the water retention state value of each monitoring point can be recorded as H S ij; From the formula, we can see that when the water retention base value is larger, the ambient temperature, ambient humidity and ambient light intensity are closer to the optimal environmental conditions, indicating that tobacco can better utilize water and nutrients and carry out photosynthesis, and the water retention state value is larger;

[0058] It should be noted that the optimal environmental conditions refer to the optimal ambient temperature, optimal ambient humidity and optimal ambient light intensity. When tobacco is in the optimal environmental conditions, the plants can usually perform photosynthesis better and use water and nutrients more efficiently.

[0059] The symbiotic state value H of each monitoring point w ij, competition state value H V ij, nutrient status value H Y ij and water retention value H S ij is set by the formula The state evaluation index HZj of the region is calculated, where c4, c5, c6, and c7 are the set proportional coefficients respectively; a two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the state evaluation value as the vertical coordinate, the state evaluation index is input into the coordinate system according to the corresponding acquisition time, and the position of the state evaluation index in the coordinate axis is recorded as the evaluation point, and the evaluation points are connected in sequence with a smooth curve to obtain a curve relationship diagram of the state evaluation index changing with time; a tangent line is drawn at the evaluation point, and the tangent expression is obtained by data fitting, and the evaluation derivative of the evaluation point is obtained by derivation and recorded as Mj; the evaluation derivatives greater than zero are summed to obtain a state monotonic increase, recorded as N1, and the evaluation indexes less than zero are summed and the absolute value is taken to obtain a state monotonic decrease, recorded as N2; the number of evaluation derivatives equal to zero is recorded as N3; using the set formula The state change index Nd of the region is calculated, where d1, d2, d3, and d4 are the set proportional coefficients. >1, then d5 takes an even number; when When , d5 takes the value of even zero; when When , d5 takes an odd value, so we can see that when hour, It can be seen from the formula that the larger the state monotonically decreases, the greater the change in soil state in the area, and the larger the state change index; the larger the state monotonically increases, the greater the change in soil state in the area, and the larger the state change index; the larger the difference between the state monotonically decreases and the state monotonically increases (i.e. The larger the value is, the greater the difference between the increasing trend and decreasing trend of the soil state in the region is, that is, the greater the change of soil state in the region, the greater the state change index is; conversely, when the difference between the monotonic decrease and the monotonic increase of the state is smaller (that is, The closer it is to zero), the smaller the gap between the increasing trend and the decreasing trend of the soil state in the region is, that is, the smaller the change of soil state in the region is, the smaller the state change index is;

[0060] The state change index is compared and analyzed with the set change threshold. When the state change index is greater than or equal to the set change threshold, it indicates that the soil state in the tobacco planting area has changed significantly, and it is highly likely that the soil state does not meet the tobacco growth requirements. The symbiotic state value, competitive state value, nutrient state value, and water retention state value of each monitoring point are recorded as soil state parameters, and the soil state parameters are integrated into a prediction update instruction, which is sent to the prediction management module.

[0061] Soil state parameters are obtained by analyzing the soil microbial activity status, soil nutrient status and soil moisture status of tobacco-growing soil, and then the degree of soil change is analyzed and prediction and update instructions are generated to achieve comprehensive soil testing, analysis and evaluation, providing detailed information on soil status and providing an important basis for soil management in tobacco planting.

[0062] When the prediction management module receives the prediction update instruction, it will generate the corresponding prediction notification by comprehensively analyzing the soil state parameters to ensure the maximization of fertilization effect, irrigation effect and disease control effect, and realize accurate prediction and timely management of the whole process of tobacco planting, specifically:

[0063] Step 1: Retrieve soil state parameters at each monitoring point, where soil state parameters include the symbiotic state value H w ij, competition state value H V ij, nutrient status value H Y ij and water retention value H Sij; the symbiotic state value H of each monitoring point in the region w ij, competition state value H V ij, nutrient status value H Y ij and water retention value H S ij are respectively averaged and the averages are taken as the symbiotic state values ​​H of the regions. w j. Competition status value H V j. Nutrient status value H Y j and water retention value H S j;

[0064] Step 2: Set the symbiotic state value H of the region w j. Competition status value H V j. Nutrient status value H Y jUsing the set formula The adjustment demand index Hfj corresponding to each sampling time in the region is calculated, where f1, f2, and f3 are the set proportional coefficients, and e is a natural constant. It can be seen from the formula that the larger the soil competition state value, the smaller the symbiotic state value, and the smaller the nutrient state value, the greater the possibility that the soil is in the presence of soil diseases, resulting in fewer beneficial microbial communities, and then insufficient nutrient status. The greater the need for soil adjustment intervention, the greater the adjustment demand index. The adjustment demand of the soil in the region is analyzed based on the adjustment demand index, as follows:

[0065] A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the adjusted demand index as the vertical coordinate. The adjusted demand index is input into the coordinate axis according to the corresponding collection time, and the position of the adjusted demand index in the coordinate axis is recorded as the demand point. A smooth curve is used to connect the demand points in sequence to obtain a curve relationship diagram of the adjusted demand index changing with time; a tangent line is drawn at the demand point, and the slope of the tangent line is calculated to obtain the adjusted slope of each demand point; the adjusted slopes greater than zero are summed up to obtain the demand increase, recorded as X1, and the adjusted slopes less than zero are summed up to obtain the demand decrease, recorded as X2; using the set formula The adjustment demand assessment value Xf is calculated, where f4 and f5 are respectively

[0066] is the set proportional coefficient; when the adjustment demand assessment index is greater than the maximum value in the set demand range, it means that the soil is seriously unhealthy and needs to be adjusted urgently, then a pesticide and fertilization forecast notification is generated; when the adjustment demand assessment index is within the set demand range, the competition state value H at each collection time in the area is V j and nutrient status value H Yj performs mean calculation to obtain the mean of competition status and the mean of nutrient status. If the mean of competition status is greater than or equal to the mean of nutrient status, it indicates that the main reason for the need for soil adjustment is the impact of harmful microbial communities (usually soil diseases), and a pesticide application forecast notification is generated; if the mean of competition status is less than the mean of nutrient status, it indicates that the main reason for the need for soil adjustment is the impact of insufficient soil nutrients (usually the soil itself is poor), and a fertilization forecast notification is generated; when the demand assessment index is less than the minimum value in the set demand range, no operation is required;

[0067] Step 3: retrieve the soil moisture of each monitoring point and record it as Lij, calculate the mean of the soil moisture of each monitoring point and get the mean as the soil moisture of the region and record it as Lj; use the set formula Calculate and obtain the irrigation demand index Lfj, where f6 and f7 are the set proportional coefficients respectively; establish a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the irrigation demand index as the vertical coordinate, input the irrigation demand index into the coordinate axis according to the corresponding collection time, and record the position of the irrigation demand index in the coordinate axis as the irrigation point, draw a tangent line at the irrigation point, calculate the slope of the tangent line to obtain the irrigation slope of each irrigation point, sum the irrigation slopes greater than zero to obtain the monotonic increase of irrigation demand and record it as X3, and sum the irrigation slopes less than zero and take the absolute value to calculate the monotonic decrease of irrigation demand and record it as X4; use the set formula The irrigation demand assessment index LX is calculated, where f8 and f9 are respectively set proportional coefficients; when the irrigation demand assessment index is greater than or equal to the set irrigation demand threshold, an irrigation forecast notification is generated;

[0068] By comprehensively analyzing soil state parameters, it is possible to accurately predict changes in soil demand, including the timing and quantification of fertilization, pesticide application and irrigation, which helps to improve the efficiency and yield of crop growth. At the same time, through the generated forecast notifications, farmers or managers can be guided to take corresponding measures in a timely manner to ensure that the soil and crops grow in the best condition and avoid the occurrence of problems such as malnutrition, diseases and pests. This precise management method helps to improve the efficiency and sustainability of crop cultivation and promote the modernization and intelligent development of agricultural production.

[0069] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A tobacco-growing soil health analysis and fertilization prediction system, It is characterized in that include: Health analysis module and prediction management module; The health analysis module analyzes the soil microbial action status, nutrient status and water retention status based on soil information to obtain soil status parameters, and then analyzes soil status changes based on soil status parameters to generate prediction update instructions, and sends them to the prediction management module; When the prediction management module receives the prediction update instruction, it conducts a comprehensive analysis and prediction of the soil state parameters to generate a corresponding prediction notification. The specific process is as follows: Step 1: Retrieve soil state parameters of each monitoring point, wherein the soil state parameters include symbiotic state value, competitive state value, nutrient state value and water retention state value; calculate the mean of the symbiotic state value, competitive state value, nutrient state value and water retention state value of each monitoring point in the region and obtain the mean values ​​as the symbiotic state value, competitive state value, nutrient state value and water retention state value of the region; Step 2: Perform predictive analysis on the symbiotic state value, competition state value, and nutrient state value of the region to obtain an adjustment demand assessment value. When the adjustment demand assessment index is greater than the maximum value in the set demand interval, a pesticide and fertilization forecast notification is generated; when the adjustment demand assessment index is within the set demand interval, the competition state value and nutrient state value at each collection time in the region are averaged to obtain the competition state mean and nutrient state mean. If the competition state mean is greater than or equal to the nutrient state mean, a pesticide application forecast notification is generated; if the competition state mean is less than the nutrient state mean, a fertilization forecast notification is generated; Step 3: Retrieve the soil moisture of each monitoring point, calculate the average of the soil moisture at each monitoring point to obtain the average as the soil moisture of the region, and perform predictive analysis on it and the water retention status value of the region to obtain the irrigation demand assessment index. When the irrigation demand assessment index is greater than or equal to the set irrigation demand threshold, an irrigation forecast notification is generated.

2. The tobacco-growing soil health analysis and fertilization prediction system according to claim 1, It is characterized in that The specific process of predictive analysis of the regional symbiosis status value, competition status value, and nutrient status value to obtain the adjustment demand assessment value is as follows: The symbiotic state value, competitive state value, and nutrient state value of the region are normalized and their values ​​are taken, and the numerical values ​​are analyzed to obtain the adjustment demand index corresponding to each sampling time in the region; A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the adjusted demand index as the vertical coordinate. The adjusted demand index is input into the coordinate axis according to the corresponding collection time, and the position of the adjusted demand index in the coordinate axis is recorded as the demand point. The demand points are connected in sequence using a smooth curve to obtain a curve relationship diagram of the adjusted demand index changing with time; a tangent line is drawn at the demand point, and the slope of the tangent line is calculated to obtain the adjusted slope of each demand point; the adjusted slopes greater than zero are summed up to obtain the demand increase, and the adjusted slopes less than zero are summed up to obtain the demand decrease; The adjusted demand index, demand increase and demand decrease are numerically analyzed to obtain the adjusted demand assessment value.

3. The tobacco-growing soil health analysis and fertilization prediction system according to claim 1, It is characterized in that The specific process of predictive analysis of regional soil moisture and regional water retention status value is as follows: The soil moisture of the region and the water retention state value of the region are numerically analyzed to obtain the irrigation demand index; a two-dimensional rectangular coordinate system is established with time as the horizontal coordinate and the irrigation demand index as the vertical coordinate, the irrigation demand index is input into the coordinate axis according to the corresponding collection time, and the position of the irrigation demand index in the coordinate axis is recorded as the irrigation point, a tangent line is drawn at the irrigation point, the slope of the tangent line is calculated to obtain the irrigation slope of each irrigation point, the irrigation slopes greater than zero are summed to obtain the monotonic increase in irrigation demand, and the irrigation slopes less than zero are summed and the absolute value is taken to obtain the monotonic decrease in irrigation demand; The irrigation demand index, the monotonic increase of irrigation demand and the monotonic decrease of irrigation demand are numerically analyzed to obtain the irrigation demand assessment index.

4. The tobacco-growing soil health analysis and fertilization prediction system according to claim 1, It is characterized in that The specific process of predicting update instruction generation is as follows: 401: Divide the tobacco planting area into several areas, set several monitoring points in each area, and use non-destructive testing technology and high-throughput testing technology to test the soil at each monitoring point to obtain soil information at each sampling time corresponding to each monitoring point in the area; the soil information includes soil texture type, soil moisture, various nutrient contents, organic matter content, ambient temperature, ambient humidity, light intensity, microbial community types, and gene abundance and metabolite content of each microbial community type; 402: Retrieving the biomass of microorganisms, microorganism species, and gene abundance and metabolite content corresponding to each microorganism species at each monitoring point, and analyzing the soil microbial activity state of the soil based on the biomass, and obtaining the symbiotic state value and competitive state value of each monitoring point; wherein the microbial activity state analysis includes symbiotic activity analysis and competitive activity analysis; 403: Retrieve the content of various nutrients in the soil of each monitoring point, wherein the nutrient types include but are not limited to nitrogen, phosphorus, potassium, etc., obtain the current growth cycle of the set tobacco, set different growth cycles corresponding to different important proportions of various nutrients, compare the current growth cycle of the tobacco with all set growth cycles to match the corresponding important proportions of various nutrients; multiply the content of various nutrients by the corresponding important proportions to obtain the growth value of various nutrients, and sum them to obtain the nutrient status value of each monitoring point; 404: different soil textures are set to correspond to different soil water retention base values, and the soil texture of the monitoring point is compared with all the set soil texture types to match the corresponding soil water retention base value; the soil texture type, organic matter content, ambient temperature, ambient humidity and light intensity of each monitoring point are retrieved, and the soil water retention base value and the optimal environmental conditions are analyzed by formula calculation to obtain the soil water retention status value of each monitoring point; thus, the water retention status value of each monitoring point can be obtained; 405: The symbiosis state value, competition state value, nutrient state value and water retention state value of each monitoring point are recorded as soil state parameters, and soil state change analysis is performed based on the soil state parameters to generate prediction update instructions.

5. The tobacco-growing soil health analysis and fertilization prediction system according to claim 4, It is characterized in that The specific process of symbiotic activity analysis is as follows: The beneficial microorganisms corresponding to the tobacco species planted in the area were compared with all the microorganisms detected at the monitoring points to obtain the target microbial species and their corresponding biomass carbon and biomass nitrogen, which were recorded as BH C and BH N ; Compare the target microbial species with the monitored microorganisms to obtain the gene abundance and metabolite content corresponding to each target microbial species, and record them as A1 and A2 respectively; Set a value coefficient for each target microbial species planted in the tobacco area, and the value coefficient corresponding to each target microorganism is recorded as γ1. The symbiotic effect value Aa corresponding to each target microorganism is calculated, where a1, a2, a3, and a4 are the set proportional coefficients, e is a natural constant, and H C and H N are the biomass carbon and biomass nitrogen of all microorganisms at the monitoring site; Comparing and analyzing the symbiotic effect value with the set symbiotic interval to divide the symbiotic effect value into a primary effect value, a secondary effect value, and a tertiary effect value, respectively counting the number of the primary effect value, the secondary effect value, and the tertiary effect value, and summing the primary effect value, the secondary effect value, and the tertiary effect value to obtain the primary effect total value, the secondary effect total value, and the tertiary effect total value; The number of first-level action values, the number of second-level action values, the number of third-level action values, the total value of first-level action, the total value of second-level action and the total value of third-level action are calculated and analyzed by formulating, and the symbiotic state value of the monitoring point is obtained; thereby, the symbiotic state value of each monitoring point in the region can be obtained.

6. The tobacco-growing soil health analysis and fertilization prediction system according to claim 5, It is characterized in that The specific process of competitive activity analysis is as follows: The different harmful microorganisms corresponding to the tobacco species planted in the area are compared with all the microorganisms detected at the monitoring points to obtain the biomass carbon and biomass nitrogen corresponding to each harmful microorganism species, and they are recorded as YH C and YH N ; Set a hazard coefficient for each type of harmful microorganism planted in the tobacco planting area, and the hazard coefficient corresponding to the harmful microorganism is recorded as γ2; compare the types of harmful microorganisms with all the monitored microorganisms to obtain the gene abundance and metabolite content corresponding to each type of harmful microorganism, and record them as B1 and B2 respectively, and use the set formula The competitive action value is obtained, where a5, a6, a7, and a8 are respectively the set proportional coefficients; Comparing and analyzing the competitive effect value with the set competitive interval to divide the competitive effect value into a primary competitive value, a secondary competitive value, and a tertiary competitive value; The number of first-level competition values, second-level competition values, and third-level competition values ​​are counted respectively, and the first-level competition values, second-level competition values, and third-level competition values ​​are summed up to obtain the first-level competition total value, the second-level competition total value, and the third-level competition total value; The number of first-level competition values, the number of second-level competition values, the number of third-level competition values, the total first-level competition value, the total second-level competition value and the total third-level competition value are calculated and analyzed by formulating to obtain the competition status value of the monitoring point; thereby, the competition status value of each monitoring point can be obtained.

7. The tobacco-growing soil health analysis and fertilization prediction system according to claim 6, It is characterized in that The specific process of soil state change analysis is as follows: The symbiosis state value, competition state value, nutrient state value and water retention state value of each monitoring point are numerically analyzed to obtain the state assessment index of the region; A two-dimensional rectangular coordinate system is constructed with time as the horizontal coordinate and the state assessment value as the vertical coordinate. The state assessment index is input into the coordinate system according to the corresponding collection time, and the position of the state assessment index in the coordinate axis is recorded as the assessment point. The assessment points are connected in sequence using a smooth curve to obtain a curve relationship diagram of the state assessment index changing with time; a tangent line is drawn at the assessment point, and the tangent expression is obtained by data fitting. The evaluation derivative of the assessment point is obtained by derivation and recorded as Mj; wherein j = 1, 2, 3...J, J is a positive integer, J represents the total number of collection moments, and j represents the sequence number of any collection moment; the evaluation derivatives greater than zero are summed up to obtain a state monotonic increase, recorded as N1, and the evaluation indexes less than zero are summed up and the absolute value is taken to obtain a state monotonic decrease, recorded as N2; the number of evaluation derivatives equal to zero is recorded as N3; using the set formula The state change index Nd of the region is calculated, where d1, d2, d3, and d4 are the set proportional coefficients. When , d5 takes an even number; when When , d5 takes the value of even zero; when When , d5 takes an odd value, so we can see that when hour, When the state change index is greater than or equal to the set change threshold, the symbiosis state value, competition state value, nutrient state value and water retention state value of each monitoring point are recorded as soil state parameters, and the soil state parameters are integrated into prediction update instructions and sent to the prediction management module.

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