Fruit and vegetable nutrition dynamic monitoring and intelligent fertilizer supplementing system

Abstract

The invention discloses a fruit and vegetable nutrition dynamic monitoring and intelligent fertilizer supplementing system, belongs to the field of fruit and vegetable planting management systems, replaces a traditional manual observation or regular soil sampling detection mode, effectively avoids subjectivity and hysteresis of manual judgment, can reflect the current nutrition state of crops in real time, provides accurate data support for dynamic nutrition management, and improves the management efficiency. The problem that in the prior art, real-time management is difficult to achieve according to the current nutrition state of crops is solved, meanwhile, accurate matching of the fertilizer type, concentration and dosage with the crop growth stage and environmental conditions is achieved in the fertilizer supplementing process, a traditional one-step nutrition supply mode is avoided, the nutrient absorption efficiency is remarkably improved, and healthy growth of the crops is promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fruit and vegetable planting management system, more particularly, to a fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system. BACKGROUND

[0002] In the process of fruit and vegetable planting, the accuracy of nutrient supply directly affects the growth rate, yield and quality of crops. Different types of fruits and vegetables have significant differences in the demand for nutrient types (such as nitrogen, phosphorus, potassium and trace elements), supply amount, fertilizer concentration and water at different growth stages such as seedling stage, flowering stage and fruiting stage. For example, seedling crops have fragile root systems and require low-concentration nutrients to avoid seedling burn, and are sensitive to water; flowering stage requires increased phosphorus element supply to promote flower bud differentiation, and the concentration needs to be moderately increased; the fruiting stage requires high potassium element support for fruit enlargement, and the water supply amount needs to be adjusted in combination with environmental conditions such as light, temperature and humidity to improve nutrient absorption efficiency. Therefore, dynamic nutrient management based on growth stages is the core requirement for efficient planting of fruits and vegetables.

[0003] Although the existing fruit and vegetable nutrient management and fertilization technology has many advantages, it still has the following shortcomings: For example, traditional management and fertilization technology relies on manual observation of plant appearance or regular soil sampling detection, which is highly subjective, time-consuming and labor-intensive, and it is difficult to manage and fertilize in real time according to the current nutrient state of crops. Even the nutrient supply is provided in the form of "one size fits all", which is not conducive to the better absorption of nutrients and healthy growth of crops; Most systems preset nutrient standard thresholds as preset general values, which lack consideration of different regional soil characteristics, variety differences and changes in soil environment after long-term planting, lack self-learning and correction mechanisms, and lead to an increase in the deviation between the threshold and the actual demand after long-term use, resulting in a gradual decline in fertilization effect; Based on the above, the existing technology cannot achieve dynamic and precise closed-loop management of the whole process of "monitoring-analysis-decision-execution", and cannot meet the fine demand of different growth stages of fruits and vegetables for nutrient supply. Therefore, there is an urgent need for a system that can combine crop growth stages, soil conditions, environmental conditions and plant characteristics to achieve dynamic monitoring and intelligent fertilization of nutrients. Therefore, we propose a fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system to solve the above problems. SUMMARY

[0004] 1. Technical problems to be solved Traditional management and fertilization technology mostly relies on manual observation of plant appearance or regular soil sampling detection, which is highly subjective and time-consuming, and is difficult to manage and fertilize in real time according to the current nutritional status of crops, and even the nutrient supply is in the form of "one size fits all", which is not conducive to the better absorption of nutrients and healthy growth of crops; the preset nutrient standard threshold of most systems is a preset general value, which lacks consideration of different regional soil characteristics, variety differences and changes in soil environment after long-term planting, lacks self-learning and correction mechanism, and leads to an increase in deviation between threshold and actual demand after long-term use, and the fertilization effect gradually decreases.

[0005] 2. Technical solutions To solve the above problems, the technical scheme adopted by the present application is as follows.

[0006] A fruit and vegetable nutrient dynamic monitoring and intelligent fertilization system, comprising: A data acquisition unit composed of a soil parameter acquisition module, an environmental parameter acquisition module and an image acquisition module, the soil parameter acquisition module acquires soil data through soil humidity sensors, soil pH sensors and soil nutrient sensors buried in the soil at the root of the fruit and vegetable, the environmental parameter acquisition module acquires environmental data through temperature and humidity sensors and light sensors arranged above the plant, and the image acquisition module is a multispectral imaging camera with its lens facing the fruit and vegetable plant; A data analysis and processing unit including a data receiving interface, a preset standard database, a comparative analysis circuit and a self-learning circuit, the data receiving interface is connected with the data acquisition unit through a data bus, the preset standard database is stored in a solid state disk, and the preset standard database has a fruit and vegetable nutrient standard threshold table preset according to growth stages, the comparative analysis circuit quantitatively compares the received data with the standard threshold table and outputs an electric signal containing growth stage code, nutrient deficiency type code and difference data, and the self-learning circuit receives the collected data after fertilization through a feedback data interface and corrects the threshold range in the preset standard database; An intelligent fertilization decision unit containing a decision processor, a ROM chip storing a fertilizer type code table, an FPGA module solidified with a concentration calculation algorithm and a fertilization amount calculation circuit, the decision processor is connected with the data analysis and processing unit through a PCI-E interface, and after receiving the electric signal output by the comparative analysis circuit, it performs the following operations: Retrieve the fertilizer type code matching the growth stage code and the nutrient deficiency type code from the ROM chip; Receive the analog signal output by the environmental parameter acquisition module through an A / D conversion module, transmit it to the FPGA module, and generate a concentration correction coefficient after operation by the concentration calculation algorithm; The nutrient difference value data is input into the fertilizer supplement amount calculation circuit, and a control signal containing a single fertilizer supplement amount and a concentration parameter is output in combination with a concentration correction coefficient. The fertilizer supplement execution unit comprises a fertilizer storage tank group, a mixing and stirring device, a conveying pipeline, and a fertilizer application terminal. Each tank body outlet of the fertilizer storage tank group is provided with an electromagnetic valve connected with the decision processor. The stirring motor in the mixing and stirring device is connected with the decision processor through a driving circuit. The conveying pipeline is provided with a flow sensor, and the signal output end of the flow sensor is connected with the input pin of the decision processor. The angle adjusting motor, the flow control valve, and the pressure pump of the fertilizer application terminal are respectively connected with the decision processor through control lines.

[0007] Further, the concentration calculation algorithm fixed in the FPGA module comprises three independent operation sub-modules of seedling stage, flowering stage, and fruiting stage, and the operation formula of each sub-module corresponds to the preset basic concentration parameter.

[0008] Further, the fertilizer supplement amount calculation circuit is realized based on a hardware logic circuit, and the operation formula is Q= (S0-S1) x K x V, wherein S0 is the nutrient standard value of the current growth stage in the preset standard database, S1 is the measured value of the soil nutrient sensor, K is the soil conversion coefficient stored in the circuit register, and V is the preset root soil volume parameter. The operation result is output to the decision processor through a digital signal.

[0009] Further, a data buffer is arranged between the decision processor and the data analysis processing unit, the data buffer stores a plurality of sets of comparison analysis data, and when the absolute value of the difference between two adjacent data sets is less than a preset threshold value, the decision processor calls the average value in the buffer as an input parameter.

[0010] Further, the intelligent fertilizer supplement decision unit further comprises an emergency processing circuit, the emergency circuit is connected with the output end of the soil nutrient sensor, when it is detected that the nutrient value is lower than the critical voltage value, the emergency processing circuit sends a trigger signal to the decision processor, and the decision processor outputs a control instruction to prolong the opening time of the electromagnetic valve and shorten the fertilizer supplement interval timer cycle.

[0011] Further, the decision processor is wirelessly connected with the fertilizer application terminal, and the decision processor outputs an instruction code field containing the fertilizer type code, the concentration parameter, and the fertilizer supplement amount data to the fertilizer application terminal.

[0012] Further, the image signal output end of the multispectral imaging camera is connected with the image decoding circuit of the data analysis and processing unit, the image decoding circuit generates a SPAD value electric signal corresponding to the chlorophyll content through pixel analysis, and when the electric signal is lower than a preset lower limit voltage, the nitrogen fertilizer control pin of the decision processor outputs a high level signal.

[0013] 3. Beneficial effects Compared with the prior art, the application has the advantages that: (1) The scheme, through the real-time acquisition of soil nutrients, environmental conditions and plant characteristics by the data acquisition unit, replaces the traditional manual observation or periodic soil sampling detection mode, effectively avoids the subjectivity and hysteresis of manual judgment, can reflect the current nutritional status of crops in real time, provides accurate data support for dynamic nutrition management, and solves the problem that it is difficult to manage in real time according to the current nutritional status of crops in the traditional technology; (2) The scheme, the intelligent fertilization decision unit based on the real-time data of the data acquisition unit, in combination with the growth stage code and the nutrient deficiency type code, retrieves the matched fertilizer type from the ROM chip, generates a concentration correction coefficient through the concentration calculation algorithm of the FPGA module in combination with the environmental parameters, and finally accurately calculates the fertilization amount through the fertilization amount operation circuit, so that the accurate matching of the fertilizer type, concentration, dosage, crop growth stage and environmental conditions is realized, the traditional "one-size-fits-all" nutrient supply mode is avoided, the nutrient absorption efficiency is significantly improved, and the healthy growth of crops is promoted; (3) The scheme, through the complete link of "data acquisition unit-data analysis and processing unit-intelligent fertilization decision unit-fertilization execution unit", a closed loop of "monitoring-analysis-decision-execution" is formed, the fertilization execution unit automatically completes the fertilizer mixing, conveying and accurate fertilization through the electromagnetic valve, the stirring device and the flow sensor assembly, and the decision processor can be adjusted in real time according to the flow feedback, realizing the full-process automatic control, which solves the problem of "disconnection of each link and dependence on manual intervention" in the prior art, greatly reduces the labor cost, and improves the intelligent level and efficiency of fruit and vegetable nutrition management. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 It is a system architecture diagram of the application; Figure 2 It is a functional composition diagram of the data acquisition unit of the application; Figure 3 It is a functional composition diagram of the data analysis and processing unit of the application; Figure 4 It is a functional operation basis diagram of the intelligent fertilization decision unit of the application; Figure 5 It is a control mode diagram of the fertilization execution unit of the application.

[0015] Explanation of reference numerals in the drawings: 1, data acquisition unit; 101, soil parameter acquisition module; 102, environmental parameter acquisition module; 103, image acquisition module; 2, data analysis processing unit; 201, data receiving interface; 202, preset standard database; 203, comparative analysis circuit; 204, self-learning circuit; 3, intelligent fertilizer supplement decision unit; 301, decision processor; 302, ROM chip; 303, FPGA module; 304, fertilizer supplement amount calculation circuit; 305, emergency processing circuit; 4, fertilizer supplement execution unit; 401, fertilizer storage tank group; 402, mixing and stirring device; 403, conveying pipeline; 404, fertilizer application terminal. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the specification of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0017] Embodiment 1: Please refer to Figures 1-5 A fruit and vegetable nutrition dynamic monitoring and intelligent fertilizer supplement system, comprising a data acquisition unit 1, a data analysis processing unit 2, an intelligent fertilizer supplement decision unit 3, and a fertilizer supplement execution unit 4. The system realizes dynamic nutrition monitoring and precise fertilizer supplement of fruit and vegetable crops throughout the whole growth cycle through a closed-loop process of data acquisition-analysis processing-intelligent decision-fertilizer supplement execution-feedback correction. The specific working principle is as follows: I. Data acquisition: fruit and vegetable crops and the data acquisition unit 1 work cooperatively through three types of modules to comprehensively obtain key data related to crop growth, providing a basis for subsequent analysis: Soil parameter acquisition module 101: through soil humidity sensors, pH sensors, and soil nutrient sensors (which can detect nitrogen, phosphorus, potassium, and trace element content) buried in the soil at the root of the fruit and vegetable, real-time acquisition of soil humidity, pH value, and nutrient concentration data reflects the current nutrient reserves and physicochemical state of the soil; Environmental parameter acquisition module 102: through temperature and humidity sensors (monitoring air temperature and humidity) and light sensors arranged above the fruit and vegetable plants, environmental temperature and humidity, light intensity, and other data are collected. These parameters directly affect the nutrient absorption efficiency of crops (such as high temperature which may accelerate water evaporation, requiring adjustment of fertilizer concentration to avoid seedling burn) ; Image acquisition module 103: Use multispectral imaging camera, lens towards the plant to shoot leaf image, after image signal transmission to data analysis processing unit 2 image decoding circuit, through pixel analysis generates SPAD value electric signal (chlorophyll content and nitrogen fertilizer demand directly related) reflecting chlorophyll content, as the crop own nutrition state of supplementary judgment basis, the data collected by data bus real-time transmission to data analysis processing unit 2, wherein the environmental parameters are analog signals, which need to be converted to digital signals by subsequent A / D conversion module.

[0018] II. Data analysis and processing: based on the comparison of standard threshold and dynamic correction data analysis and processing unit 2 carries out quantitative analysis on the collected data, and optimizes the judgment standard combined with self-learning mechanism to ensure that the analysis result meets the actual demand: Data receiving and storage: data receiving interface 201 receives all data of data acquisition unit 1 through data bus, temporarily stores for subsequent processing, and preset standard database 202 is stored in solid state disk, which has preset nutrient standard threshold table (including suitable range of nitrogen, phosphorus, potassium and other elements) and SPAD value lower threshold according to fruit and vegetable types (such as tomato, cucumber, etc.) and growth stages (seedling stage, flowering stage, fruiting stage, etc.); Comparative analysis: comparative analysis circuit 203 quantitatively compares the real-time collected soil nutrient data and SPAD value with the preset standard threshold table: Combined with soil nutrient data and growth stage characteristics (such as low nitrogen requirement in seedling stage and high potassium requirement in fruiting stage), the current crop growth stage is determined (generate “growth stage code”); Identify the nutrient type below the threshold value (such as insufficient phosphorus element), generate “nutrient deficiency type code”; Calculate the difference between actual value and standard value (generate “nutrient difference data”), which provides basis for subsequent fertilizer amount calculation; Self-learning correction: self-learning circuit 204 receives the reacquired data after fertilization (such as the change of soil nutrient and plant SPAD value 12 hours after fertilization) through feedback data interface, analyzes the deviation between actual fertilization effect and expectation (such as the soil nutrient is still below the threshold value after fertilization, which indicates that the original threshold value may be too low), and corrects the threshold value range in the preset standard database 202 through iterative algorithm. This mechanism can adapt to different regional soil characteristics (such as adjusting the pH related threshold value in acid soil in south China), variety differences and soil changes caused by long-term planting, avoiding the accumulation of deviation between threshold value and actual demand.

[0019] III. Intelligent fertilization decision: based on the analysis result, the intelligent fertilization decision unit 3 calculates the fertilization scheme combined with environmental conditions to ensure that the fertilizer type, concentration and amount adapt to the current demand of crops: Fertilizer type matching: After receiving the "growth stage code" and "nutrient deficiency type code" output by the comparative analysis circuit 203, the decision processor 301 retrieves the matching fertilizer type (such as potassium dihydrogen phosphate for phosphorus deficiency during flowering) from the fertilizer type code table in the ROM chip 302; Concentration correction coefficient calculation: The decision processor 301 converts the analog signals (temperature, humidity, and light) from the environmental parameter acquisition module 102 into digital signals through the A / D conversion module, and transmits them to the FPGA module 303. The concentration calculation algorithm solidified in the FPGA module 303 includes three independent sub-modules for seedling stage, flowering stage, and fruiting stage, which calculate the concentration correction coefficient according to the environmental characteristics of the corresponding growth stage: for example, in a high-temperature and high-light environment, the coefficient is reduced to avoid high fertilizer concentration due to rapid water evaporation; in a low-temperature and high-humidity environment, the coefficient is appropriately increased to ensure nutrient absorption efficiency; Fertilizer application amount calculation: The decision processor 301 inputs the "nutrient difference data" into the fertilizer application amount operation circuit 304, combines the concentration correction coefficient, and calculates the single fertilizer application amount (Q) according to the formula Q = (S0-S1) x K x V implemented by the hardware logic circuit, S0 is the nutrient standard value of the current growth stage in the preset standard database, S1 is the measured value of the soil nutrient sensor, K is the soil conversion coefficient (preset in the circuit register according to soil texture (such as clay, sandy soil), reflecting the adsorption ability of soil to nutrients), V is the preset root soil volume parameter (determined according to crop variety and growth stage, such as small root system for seedling stage, small V value), and finally outputs the control signal containing "single fertilizer application amount" and "concentration parameter"; Stability optimization and emergency treatment: The data buffer stores multiple sets of comparative analysis data, and when the absolute value of the difference between adjacent two sets of data is less than the preset threshold (such as ±5%), the decision processor calls the average value as the input parameter to avoid decision deviation caused by short-term data fluctuation; if the soil nutrient sensor detects that the nutrient value is lower than the critical voltage value (such as nitrogen element close to the critical point of nutrient deficiency), the emergency treatment circuit 305 sends a trigger signal to the decision processor to control the electromagnetic valve to extend the opening time and shorten the fertilizer application interval (such as from 24 hours to 12 hours), to prevent crops from being damaged due to nutrient deficiency.

[0020] Four, fertilizer execution: automatic mixing and precise delivery The fertilizer execution unit 4 completes the mixing, delivery, and application of fertilizer according to the decision instruction, ensuring that the fertilizer program is implemented: Fertilizer retrieval and mixing: Each tank body of the fertilizer storage tank group 401 corresponds to a type of fertilizer (such as a nitrogen fertilizer tank, a potassium fertilizer tank, etc.), and the decision processor 301 controls the opening of the corresponding tank body's electromagnetic valve according to the matching fertilizer type code to release the fertilizer to the mixing and stirring device 402 in proportion, while controlling the water input according to the concentration parameter, and the stirring motor is started under the control of the drive circuit to mix the fertilizer and water uniformly (the stirring time is pre-set according to the type of fertilizer, such as extending the stirring time for slow-release fertilizer); Precise delivery and application: The mixed fertilizer is delivered to the fertilizer application terminal 404 through the delivery pipeline 403, the flow sensor on the pipeline monitors the actual delivery amount in real time and feeds back the signal to the decision processor 301, if the actual flow deviates from the target fertilizer supplement amount by more than 5%, the decision processor compensates by adjusting the power of the pressure pump or the opening degree of the valve, the angle adjustment motor of the fertilizer application terminal 404 adjusts the spraying angle according to the growth height of the crop (such as low-angle spraying of the root at the seedling stage and high-angle spraying of the leaves and root combination at the fruiting stage), and the flow control valve controls the final application amount according to the fertilizer supplement amount to complete the precise fertilizer supplement.

[0021] Five, closed-loop feedback: After the whole-process dynamic optimization of fertilizer supplement is completed, the system collects soil, environmental and plant data again at a preset time (such as 24 hours later) through the data acquisition unit 1, and compares the changes before and after the fertilizer supplement through the data analysis processing unit 2: If the nutrients have reached the standard value, record the current scheme as an effective case; if not, correct the threshold or fertilizer supplement amount calculation parameter (such as adjusting the soil conversion coefficient K) through the self-learning circuit 204.

[0022] The closed-loop mechanism ensures that the system continuously adapts to the growth dynamics of crops, realizing the whole-process intelligentization of "monitoring-analysis-decision-execution-correction", and the system can dynamically respond to the nutrient needs of fruits and vegetables at different growth stages, and realize precise fertilizer supplement combined with soil, environmental and plant conditions, solving the problems of strong subjectivity, fixed threshold and lack of closed loop in traditional technology, and improving the efficiency and quality of fruit and vegetable planting.

[0023] Example 2: In view of the above example 1, further description is made, please refer to Figures 1-5 In order to make the skilled in the art more clearly understand the present application, the following will be described in detail in combination with a typical scene to highlight the precision of the fruit and vegetable nutrient dynamic monitoring and intelligent fertilizer supplement system in the nutrient supplement of fruit and vegetable crops: Taking the spring planting of sunlight greenhouse cherry tomato (variety "Qianxi") as an example, the demand for nitrogen, phosphorus and potassium of this variety shows significant stage difference at the seedling stage (3-4 leaf stage), flowering and fruit setting stage (from the first inflorescence fruit setting to the third inflorescence flowering), and fruiting and bulking stage (fruit diameter 2-3 cm to maturity), and it is sensitive to calcium, magnesium and other trace elements. This system realizes whole-cycle precise fertilizer supplement through multi-dimensional monitoring and dynamic decision-making, and the specific application process is as follows: I. System deployment and initial configuration 1. Hardware fine-grained deployment Soil parameter acquisition module 101: "Tetrahedral" point distribution (every 5m 2 1 group), bury three-in-one sensor (detect nitrogen, phosphorus, potassium concentration, accuracy ±2mg / kg), soil humidity sensor (range 0-100%, accuracy ±1%) and pH sensor (range pH4-8, accuracy ±0.1) in the root system concentrated layer (15-20cm depth), each group of sensors is connected with data bus by shielded wire to avoid greenhouse electromagnetic interference.

[0024] Environmental parameter acquisition module 102: install temperature and humidity integrated sensor (monitor air temperature 10-50℃, humidity 0-100%) at the top of greenhouse (2.5m from ground), set up light sensor (range 0-2000μmol / m 2 ·s, sampling frequency 1 / minute) 50cm above plant canopy, data is transmitted wirelessly to processing unit through 4G module.

[0025] Image acquisition module 103: set up 2 multi-spectral imaging cameras (resolution 1280x960) between the ridges, take leaf image once at 7:00 and 14:00 each day, lens focal length is adjusted dynamically according to plant height (focal length 8mm at seedling stage, 12mm at result stage), ensure leaf proportion ≥60%.

[0026] Fertilizer execution unit 4: configure 6 fertilizer storage tanks (capacity 500L, store calcium ammonium nitrate, ammonium dihydrogen phosphate, potassium sulfate, magnesium sulfate, chelated iron and complex microbial fertilizer respectively), install weighing sensor (accuracy ±0.1kg) at the bottom of tank body; pH adjusting module (can add citric acid / potassium hydroxide solution) is built-in in mixing and stirring device 402; fertilization terminal 404 adopts "drip irrigation + foliage spraying" dual mode, drip irrigation head spacing is 30cm (corresponding to single plant root system range), foliage spray head can rotate 360°, covering the front and back of leaves.

[0027] 2. Database parameter customization setting Pre-set standard database 202 is entered for "Millennium" cherry tomato characteristics:

[0028] II. Seedling stage (3 leaf 1 heart stage) monitoring and fertilizer execution 1. Multi-dimensional data acquisition and abnormality identification Soil parameters: nitrogen 105mg / kg (lower than threshold lower limit 120), phosphorus 75mg / kg (normal), potassium 90mg / kg (normal), humidity 28% (suitable range 25-30%), pH 6.5 (suitable).

[0029] Environmental parameters: air temperature 22℃ (suitable), humidity 70% (high), light intensity 650 μmol / m 2 ·s (800-1000 lower than the demand of seedling stage).

[0030] Image analysis: The multispectral image is processed by the decoding circuit, the SPAD value is 38 (lower than the lower limit of 40), the leaf pixel shows slight yellowing (the proportion of red light in the RGB channel increases), and it is determined that there is a lack of nitrogen.

[0031] 2. Data analysis and decision making The output of the comparative analysis circuit 203: growth stage code "G01", nutrient deficiency type code "N", difference ΔN = 15 mg / kg (120-105).

[0032] The FPGA module 303 calls the seedling concentration algorithm, combines high humidity (70%) and low light (650 μmol), and generates a concentration correction coefficient of 0.7 (the concentration needs to be reduced to prevent overgrowth when the humidity is high).

[0033] The fertilizer supplement amount calculation circuit 304 calculates: Q = (120-105) x 1.0 (soil K value) x 0.03 (seedling V value) = 0.45 kg; combined with the concentration correction coefficient 0.7, the single fertilizer supplement amount is determined to be 0.45 kg, and the concentration is 0.35%.

[0034] The data buffer retrieves the last three soil nitrogen data (108 mg / kg, 103 mg / kg, 105 mg / kg), the average value is 1-05.3 mg / kg, and it is confirmed that the nitrogen deficiency trend is stable and there is no data fluctuation interference.

[0035] 3. Precise fertilizer supplement execution process The decision processor 301 sends an instruction to the calcium ammonium nitrate storage tank, the electromagnetic valve is opened (opening degree 30%), and the weighing sensor feedbacks that the discharge amount reaches 0.45 kg when the valve is closed.

[0036] The mixing and stirring device 402 adds 1-28.6 L of water (calculated according to the concentration of 0.35%), starts the stirring motor (speed 300 r / min), and at the same time, the pH adjusting module detects that the solution pH is 7.2, and automatically adds 0.1 L of citric acid solution to adjust the pH to 6.5 (adapted to the absorption preference of seedling roots).

[0037] The flow sensor of the conveying pipeline 403 monitors in real time (accuracy ±0.5 L / min), and when the cumulative flow reaches 1-29.05 kg (including fertilizer), the decision processor issues a stop pump instruction, and the fertilizer terminal 404 switches to "drip irrigation mode", the flow of the drip head is controlled at 2 L / h, and the process lasts for 30 minutes (to ensure that the solution slowly penetrates the root zone and avoids washing away the seedlings).

[0038] 4. Feedback and threshold correction 72 hours after the first monitoring: Soil nitrogen rises to 132 mg / kg, SPAD value 42 (reaches the lower limit), leaf yellowing subsides, self-learning circuit 204 analysis finds that the actual absorption efficiency is higher than expected, and the seedling nitrogen threshold is corrected to 115-145 mg / kg, and the concentration correction coefficient algorithm is updated synchronously (the coefficient is fine-tuned from 0.7 to 0.75 under high humidity).

[0039] Three, emergency fertilization and dynamic adjustment during flowering and fruit setting period (the second inflorescence flowering) 1. Sudden nutrient deficiency monitoring and emergency response Soil parameters: calcium content 45 mg / kg (lower than the emergency threshold 50), phosphorus 85 mg / kg (slightly lower than the threshold 90), and the rest normal.

[0040] Image acquisition: multispectral image shows that the edges of new leaves are curled (blue band reflectance increases), and the decoding generates a calcium deficiency characteristic code "Ca".

[0041] Emergency processing circuit 305 detects that calcium < 50 mg / kg, immediately sends a trigger signal (high-level pulse) to the decision processor.

[0042] 2. Emergency decision and execution Decision processor 301 responds to emergency signals first: calls the fertilizer code "F04" (calcium nitrate + magnesium sulfate compound fertilizer) matched with "G02+Ca" in ROM chip 302, instructs the electromagnetic valve to extend the opening time to 1.5 times the normal time (from 30 seconds to 45 seconds), and shortens the fertilization interval from 72 hours to 48 hours.

[0043] FPGA module combines the current environment (temperature 26℃, humidity 60%, light 1200μmol), generates concentration correction coefficient 1.0 (suitable concentration during flowering), and fertilization amount calculation circuit calculates Q= (70-45) ×1.0 ×0.08=2.0kg, concentration 0.6%.

[0044] Fertilization terminal switches to "foliar + drip irrigation" dual mode: foliar spray nozzle rotation angle is adjusted to 45° (covering the back of new leaves), and mist droplet diameter is controlled at 50μm (improving leaf adhesion rate); drip irrigation is carried out synchronously, with a flow rate of 1.5L / h for 40 minutes.

[0045] 3. Multiple adjustments and stabilization 48 hours after the first emergency fertilization: Calcium rises to 58 mg / kg, still below the lower limit of the standard 70. Decision processor calls the cached 3 sets of data (45→52→58 mg / kg), judges that the absorption is linear growth, and again fertilizes 1.5 kg (concentration 0.5%). 72 hours later, calcium reaches 75 mg / kg, the emergency is lifted, and the fertilization interval is restored to 72 hours. The self-learning circuit corrects the calcium threshold for flowering and fruiting period to 65-85 mg / kg (adapted to the calcium reserve characteristics of this batch of soil).

[0046] Four, the results of the expansion period (fruit diameter 2.5 cm) synergistic fertilization optimization 1. Multi-parameter collaborative decision-making Soil parameters: potassium 160 mg / kg (lower than the threshold 180), nitrogen 140 mg / kg (normal), humidity 22% (lower than the suitable range 25-30%).

[0047] Environmental parameters: temperature 28℃, light 1800 μmol / m 2 ·s (strong light), humidity 50%.

[0048] Decision logic: Fertilizer type: "G03+K" matched potassium sulfate (code "F03") is retrieved from the ROM chip.

[0049] Concentration correction: The FPGA module calls the results period algorithm, the crop transpiration is fast under strong light (1800 μmol), and the concentration correction coefficient is increased to 1.1 (a slightly higher concentration is needed to offset water loss); At the same time, combined with the soil humidity of 22% (low), an "additional water instruction" is added to the concentration parameter (50L of water is added synchronously for every 1kg of fertilizer).

[0050] Fertilizer amount calculation: Q= (180-160) ×1.0 ×0.12=2.4 kg, concentration 1.0% (1.1 × basic concentration 0.9%), synchronous water 120L.

[0051] 2. Mixed application and precise control Potassium sulfate is mixed with water at a concentration of 1.0% (2.4 kg + 240 L of water), 120 L of water is added by stirring device, the total solution amount is 360 L, and stirring is carried out for 5 minutes (to ensure that the potassium fertilizer is completely dissolved).

[0052] The fertilization terminal switches to "directed drip irrigation" mode, the drip head is aimed at the soil area 20 cm below the fruit (the root distribution characteristics of Millennium tomato during the fruiting period), the frequency of the pressure pump is adjusted to 50Hz (flow rate 5L / min), and the flow rate sensor feeds back data every 10 seconds, with a cumulative error control within ±1%.

[0053] Five, the effect verification system runs 150 days (from planting to pulling seedlings): Data level: A total of 32 fertilizations were carried out (including 4 emergency fertilizations), and the self-learning circuit completed 12 threshold corrections (covering nitrogen, phosphorus, potassium and calcium elements). The fertilization accuracy was improved from the initial ±8% to ±3%.

[0054] Crop performance: Seedling survival rate 98.5% (conventional planting 90%), flowering and fruiting rate 82% (conventional 70%), number of fruits per plant 35 (conventional 28), fruit soluble solids content 9.2% (conventional 8.0%), fruit cracking rate (caused by calcium deficiency) reduced from 15% to 3%.

[0055] Through the refined application of the entire cycle of cherry tomatoes, the system has been verified to be effective in dynamic monitoring, intelligent decision-making, precise execution and self-learning correction. In particular, it demonstrates the core advantage of the closed-loop management of "monitoring-analysis-decision-making-execution-feedback" when dealing with periodic demand differences, sudden nutrient deficiencies and environmental interference.

[0056] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any person skilled in the art who, within the technical scope disclosed by the present invention, makes equivalent substitutions or modifications based on the technical solutions and improved concepts of the present invention shall be covered by the scope of protection of the present invention.

Claims

1. A fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system, characterized in that: include: The data acquisition unit consists of a soil parameter acquisition module, an environmental parameter acquisition module, and an image acquisition module; A data analysis and processing unit includes a data receiving interface, a preset standard database, a comparison and analysis circuit, and a self-learning circuit. The data receiving interface is connected to the data acquisition unit via a data bus. The preset standard database is stored in a solid-state hard disk, and the preset standard database contains a table of fruit and vegetable nutrient standard thresholds divided by growth stage. The intelligent fertilizer supplementation decision unit includes a decision processor, a ROM chip storing a fertilizer type code table, an FPGA module with a concentration calculation algorithm, and a fertilizer supplementation amount calculation circuit. The decision processor is connected to the data analysis and processing unit via a PCI-E interface; The fertilization execution unit includes a fertilizer storage tank group, a mixing and stirring device, a conveying pipeline and a fertilization terminal. Each tank outlet of the fertilizer storage tank group is provided with an electromagnetic valve connected to the decision processor. The stirring motor in the mixing and stirring device is connected to the decision processor through a drive circuit. A flow sensor is provided on the conveying pipeline, and its signal output end is connected to the input pin of the decision processor. The angle adjustment motor, flow control valve and pressure pump of the fertilization terminal are respectively connected to the decision processor through control lines.

2. The fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system according to claim 1, characterized in that: The soil parameter acquisition module acquires soil data through soil moisture sensors, soil pH sensors, and soil nutrient sensors embedded in the soil at the roots of fruits and vegetables. The environmental parameter acquisition module acquires environmental data through temperature and humidity sensors and light sensors deployed above the plants. The image acquisition module is a multispectral imaging camera with its lens facing the fruit and vegetable plants. The comparative analysis circuit performs a quantitative comparison between the received data and the standard threshold table, and outputs an electrical signal containing a growth stage code, a nutrient deficiency type code, and difference data. The self-learning circuit receives the collected data after fertilization through the feedback data interface and modifies the threshold range in the preset standard database. After receiving the electrical signal output by the comparison and analysis circuit, the decision processor performs the following operations: Retrieving a fertilizer type code that matches the growth stage code and the nutrient deficiency type code from the ROM chip; The analog signal output by the environmental parameter acquisition module is received by the A / D conversion module, transmitted to the FPGA module, and generated into a concentration correction coefficient after being calculated by the concentration calculation algorithm; The nutrient difference data is input into the fertilizer amount calculation circuit, and the control signal containing the single fertilizer amount and concentration parameters is output in combination with the concentration correction coefficient.

3. The fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system according to claim 1, characterized in that: The concentration calculation algorithm solidified in the FPGA module includes three independent operation submodules for the seedling stage, the flowering stage, and the fruiting stage. The operation formula of each submodule corresponds to the preset basic concentration parameters.

4. The fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system according to claim 1, characterized in that: The fertilizer amount calculation circuit is implemented based on a hardware logic circuit, and the calculation formula is: Q = (S0-S1) × K × V, where S0 is the nutrient standard value of the current growth stage in the preset standard database, S1 is the measured value of the soil nutrient sensor, K is the soil conversion coefficient stored in the circuit register, and V is the preset root soil volume parameter. The calculation result is output to the decision processor via a digital signal.

5. The fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system according to claim 1, characterized in that: A data buffer is provided between the decision processor and the data analysis processing unit. The data buffer stores a number of consecutive groups of comparative analysis data. When the absolute value of the difference between two adjacent groups of data is less than a preset threshold, the decision processor calls the average value in the buffer as an input parameter.

6. The fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system according to claim 1, characterized in that: The intelligent fertilization decision unit also includes an emergency processing circuit, which is connected to the output end of the soil nutrient sensor. When it is detected that the nutrient value is lower than the critical voltage value, the emergency processing circuit sends a trigger signal to the decision processor, and the decision processor outputs a control instruction to control the electromagnetic valve to extend the opening time and shorten the fertilization interval timer period.

7. The fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system according to claim 1, characterized in that: The decision processor is wirelessly connected to the fertilization terminal, and the decision processor outputs an instruction code field including a fertilizer type code, a concentration parameter, and fertilizer amount data to the fertilization terminal.

8. The fruit and vegetable nutrition dynamic monitoring and intelligent fertilization system according to claim 2, characterized in that: The image signal output end of the multispectral imaging camera is connected to the image decoding circuit of the data analysis and processing unit. The image decoding circuit generates a SPAD value electrical signal corresponding to the chlorophyll content through pixel analysis. When the electrical signal is lower than a preset lower limit voltage, the nitrogen fertilizer control pin of the decision processor outputs a high-level signal.

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