A smart agricultural monitoring and management method based on the Internet of Things

Abstract

The application discloses a kind of based on Internet of Things's wisdom agricultural monitoring management method, including acquisition data step, first judgment step, first monitoring step, first control step, first calculation step, period calibration step, analysis step, second judgment step, second control step, second monitoring step, third control step, third monitoring step and feedback step.The application quantifies the growth of crops by calculating model, controls ventilation and light supplement time, reduces the error caused by relying on manual experience, improves the accuracy and reliability of crop growth evaluation, improves the practicability and regional adaptability of the method, and further improves the precision of crop growth regulation.

Description

Technical Field

[0001] This invention relates to the technical field of monitoring and management, specifically to a smart agriculture monitoring and management method based on the Internet of Things. Background Technology

[0002] The growing environment of crops has a certain impact on crop growth, disease incidence, and yield, among other production indicators. Currently, the Internet of Things (IoT) technology has been widely applied in agriculture, construction, and water conservancy. By utilizing IoT technology, sensor networks are established within the crop growing environment to acquire data on crop growth conditions, thereby enabling real-time perception, intelligent analysis, precise decision-making, and remote management of the agricultural environment and crop status.

[0003] Existing crop growth environment monitoring technologies mainly involve setting up multiple sensors near farmland to detect data such as temperature, humidity, and gas concentrations. These sensors collect parameters such as temperature, humidity, and gas concentrations in real time within crop greenhouses, providing immediate data on the crop growth environment to assess crop growth and predict environmental trends.

[0004] Currently, Chinese patent CN119065311A proposes a remote monitoring system for intelligent information in facility agriculture. It collects microclimate data through a microclimate monitoring module, performs deviation analysis on the microclimate data through a comprehensive analysis module, and determines whether an assessment mechanism is triggered. The soil microbial monitoring module collects microbial activity data, the microbial assessment module generates a quality assessment value when the assessment mechanism is triggered, and the remote management module issues anomaly warnings based on the assessment value.

[0005] Regarding the above-mentioned technical solution, the method assesses growth by monitoring indicators such as climate data and microbial data, and makes adjustments based on the assessment values ​​after fusion analysis. However, this method has a large systematic error and is difficult to translate into effective management decisions. As a result, it is difficult to accurately control the growth of crops and reduces the precision of crop growth regulation. Summary of the Invention

[0006] The purpose of this invention is to provide a smart agriculture monitoring and management method based on the Internet of Things to solve the problems mentioned in the background art.

[0007] This invention provides a smart agriculture monitoring and management method based on the Internet of Things, which achieves its purpose through the following technical solution:

[0008] A smart agriculture monitoring and management method based on the Internet of Things includes the following steps:

[0009] Data Acquisition: Real-time acquisition of oxygen concentration, temperature, and light intensity in the greenhouse environment; calculation of the rate of change in oxygen concentration, the average daily light intensity I, and the average daily temperature T; and acquisition of the preset lighting time for the current planting cycle. and planting area S;

[0010] First judgment: If the crop is currently under sunlight, execute the first monitoring step; otherwise, execute the first calculation step.

[0011] First monitoring: When crops are under sunlight and the rate of change in oxygen concentration is less than the first baseline rate of change. At that time, the first regulatory step shall be executed;

[0012] First control: Turn on the ventilation equipment and ventilate according to the standard ventilation volume. After ventilation is completed, proceed with the first judgment step.

[0013] First calculation: Crop oxygen production rate The calculation model is as follows: ,in For the maximum photosynthetic rate, Let be the half-saturation light intensity constant, and α be the light energy utilization efficiency constant. The range of values ​​for α is: Oxygen consumption rate of crops The calculation model is as follows: ,in As the reference temperature, For crops at the reference temperature The respiratory rate at which the respiratory rate is reduced The coefficient of temperature rise. The range of values ​​for is: ;

[0014] Analysis: The net primary productivity (NPP) of crops is calculated using the following model: ,in The length of a day;

[0015] Second judgment: If NPP is less than the target net primary productivity NPB, then the crop accumulation is deemed insufficient, and the second regulation step is executed;

[0016] Second adjustment: Supplement the lighting time for the day, the supplementary lighting time is as follows: .

[0017] By adopting the above technical solution, the oxygen concentration change rate is used as a factor to judge the photosynthetic efficiency of crops. The first benchmark change rate Y1 is used to accurately identify the photosynthetic inhibition state of crops. When an abnormal oxygen concentration change rate, i.e. oxygen production rate, is detected, a ventilation compensation mechanism can be triggered immediately to reduce the probability of crops being in an inefficient photosynthetic state for a long time. Thus, in cases such as cloudy days or equipment failure, the photosynthetic efficiency of crops can be compensated. Compared with traditional methods that rely solely on thresholds such as carbon dioxide concentration or light intensity, this method can accurately regulate the growth of crops and the rate of biomass accumulation, reduce errors caused by reliance on human experience, and thus improve the accuracy of crop growth regulation. By calculating the oxygen production rate and oxygen consumption rate of crops, the net primary productivity (NPP) of crops can be further calculated. For the oxygen consumption rate caused by crop respiration, a temperature-driven calculation model is used to determine the oxygen consumption trend, reducing errors caused by reliance on human experience. The NPP dynamically quantifies the biomass of crops. Compared to the experience-based threshold judgments in traditional methods, this method can accurately diagnose crop growth lag, thereby further adjusting the lighting time for crops, compensating for growth gaps, and reducing ineffective lighting energy consumption. This provides data support for subsequent adjustments to lighting strategies, ultimately improving the accuracy of crop growth regulation.

[0018] Optionally, a periodic calibration step is also provided between the first calculation step and the analysis step;

[0019] Periodic calibration: Based on the current crop planting cycle, the average leaf area G and root and stem dry weight W of the crop are measured according to a specified measurement cycle. The preset value for leaf area in the current planting cycle is... The preset value for the dry weight of roots and stems in the current planting cycle is ;

[0020] In the analysis steps, the calculation model for net primary productivity (NPP) was also revised. The revised NPP calculation model is as follows: ,in The first leaf area index, The second leaf area index, The specific calculation model for the value is as follows: , The specific calculation model for the value is as follows: .

[0021] By adopting the above technical solution and introducing the first leaf area index The second leaf area index is introduced as a compensation coefficient for the photosynthetic efficiency of crops. As a respiration consumption compensation coefficient for crops, it can effectively solve the problem that fixed parameters in basic calculation models cannot be applied to different crop varieties or different growth stages of crops. For example, it can reduce the respiration consumption of crops during the seedling stage when the leaf area is small. Weighting is increased during the peak flowering period when crops have a large leaf area. Weights are assigned to ensure that the NPP calculation model accurately matches the crop growth cycle and the characteristics of each growth stage. Leaf area index reflects the relationship between canopy light distribution and photosynthetic efficiency; when crops are planted at high density... When the value approaches 1, the system can improve... The calculated weights are used to reflect the photosynthetic efficiency advantage of the population, while simultaneously improving... The value is increased The calculation weights are used to reflect the increased respiration consumption caused by overlapping leaves and shading, reducing the quantitative error of actual crop growth. Furthermore, in extreme environments such as high temperatures, some crops may reduce transpiration by decreasing leaf area. Traditional calculation methods can easily misjudge this as growth decline, leading to overcompensation for light exposure. The calculation model that dynamically corrects net primary productivity using the first and second leaf area indices can reduce potential systematic errors, protect the crop's self-regulation mechanisms, and improve the method's practicality and regional adaptability, especially enhancing its applicability to perennial crops or high-density planting scenarios. This reduces the quantitative error of actual crop growth and improves the accuracy of crop growth regulation. By periodically measuring the actual leaf area of ​​crops and comparing it with preset leaf area values, the first leaf area index is dynamically calibrated, further reducing the quantitative error of actual crop growth, for example, when crops encounter pests or diseases. The value of the second leaf area index is reduced to match the decrease in photosynthetic efficiency caused by leaf damage. Compared with the method of excessive supplemental lighting based on a healthy crop model, this can reduce the probability of energy waste caused by the system. Simultaneously, if the actual leaf area consistently exceeds the preset leaf area value, but net primary productivity does not increase synchronously, the calculation model can provide early warnings of potential crop growth problems, such as nutrient deficiency or light intensity mismatch, allowing for timely optimization of the model's preset value. This further improves the practicality and reliability of the method, reduces potential systemic errors, and thus improves the accuracy of crop growth regulation. By periodically measuring the dry matter weight of crop roots and stems and comparing it with the preset dry matter weight value, the second leaf area index is dynamically adjusted. Since there is a certain correlation between the respiration efficiency of crop roots and stems and leaf area, compensating for crop respiration consumption with root and stem dry matter weight reduces the quantitative error of actual crop growth. For example, in high-temperature seasons, some crops actively reduce dry matter accumulation to reduce respiration consumption. The value is reduced to dynamically lower Compared to traditional computational models that rely on unnecessary cooling interventions, this method reduces the probability of energy waste in the system. It can also provide early warnings of potential crop growth problems, such as water and fertilizer imbalances, through computational models, enabling timely optimization of model presets. This further improves the practicality and reliability of the method, reduces potential system errors, and ultimately enhances the accuracy of crop growth regulation.

[0022] Optionally, a second monitoring step and a third control step may also be included;

[0023] In the second judgment step, if NPP is greater than or equal to the target net primary productivity NPB, the crop accumulation is judged to be qualified, and the second monitoring step is executed.

[0024] Second monitoring: When it is detected that the crops are not under light conditions and the rate of change in oxygen concentration is greater than the second baseline rate of change. At that time, the third regulatory step will be implemented;

[0025] Third control: Turn on the ventilation equipment and ventilate according to the standard ventilation volume. After ventilation is completed, proceed to the second monitoring step.

[0026] By adopting the above technical solution, when it is detected that the crops are not under light conditions and the rate of change in oxygen concentration is greater than the second baseline rate of change... When ventilation is complete, turn on the ventilation equipment and ventilate according to the standard ventilation rate. After ventilation is complete, repeatedly monitor whether the rate of change of oxygen concentration is greater than the second baseline rate of change. By dynamically capturing changes in crop respiration efficiency at night, it is beneficial to accurately identify changes in crop accumulation at night. When the rate of change in oxygen concentration exceeds the second baseline rate of change... When the accumulation of anaerobic metabolites in greenhouse crops is vigorous, triggering ventilation intervention can reduce the accumulation rate of metabolites and decrease the probability of damage caused by root respiration stress, such as persistent soil hypoxia caused by cloudy days. This allows for timely protection of crop yields and facilitates precise energy delivery. Compared to traditional methods that rely on fixed-time ventilation, this method can reduce post-harvest losses, provide early warning of potential crop growth problems, improve the practicality and reliability of the method, and ultimately enhance the precision of crop growth regulation.

[0027] Optionally, the analysis steps also include revising the calculation model for net primary productivity (NPP). The revised NPP calculation model is as follows: ,in For soil microbial oxygen consumption rate, The calculation model is as follows: , where k is the soil microbial respiration rate, and the value of k is determined by the experiment.

[0028] By adopting the above technical solution, and by calculating the oxygen consumption rate of soil microorganisms and correcting the calculation model, the temperature-dependent oxygen consumption of microorganisms can be quantified. Traditional methods for calculating net primary productivity only consider plant respiration and ignore the fact that microorganisms consume a lot of oxygen at suitable temperatures, especially in the early stages of organic fertilizer application. This allows the corrected calculation model to reduce the probability of misjudging crop growth conditions and the probability of supplemental lighting decisions failing due to microbial oxygen consumption. The calculation model is also compatible with different types of planting soils, which helps to solve the model adaptation problem for cross-regional planting of the same crop. The model can be optimized in a timely manner, further improving the practicality and reliability of the method, reducing potential systematic errors, and thus improving the accuracy of crop growth regulation.

[0029] Optionally, the soil moisture θ in the greenhouse environment may also be acquired during the data acquisition step.

[0030] The analysis also included the oxygen consumption rate of soil microorganisms. The calculation model was revised, and after revision The computational model was updated to Where γ is the humidity coefficient. Standard soil moisture.

[0031] By adopting the above technical solution, and by monitoring soil moisture data and further correcting the oxygen consumption rate of soil microorganisms, compared with the linear model calculation method in traditional methods, it is possible to dynamically calibrate the trend of microbial oxygen consumption based on soil moisture conditions. When θ deviates from the standard soil moisture, When the soil microbial oxygen consumption rate drops sharply, it can accurately reflect the value of soil microbial oxygen consumption rate, thereby reducing the probability of false or delayed triggering of ventilation decisions. This makes the method applicable to precise water management scenarios such as tidal irrigation or hydroponics, allowing the model to be optimized in a timely manner, further improving the practicality and reliability of the method, and thus improving the accuracy of crop growth regulation.

[0032] Optionally, a third monitoring step and a feedback step may also be included;

[0033] Third monitoring: Record the number of times the cumulative crop yield is deemed insufficient within a specified monitoring period. When the number of times the cumulative crop yield is deemed insufficient exceeds the preset number, a feedback step is executed.

[0034] Feedback: Generate an early warning of abnormal crop growth and send it to the management terminal.

[0035] By adopting the above technical solution, the gradual stagnation of crop growth is recorded by statistically analyzing the number of times NPP fails to meet the standard within a specified monitoring period, and a buffer threshold is set to reduce the probability of over-response. This provides an early warning of the accumulation of abnormal crop growth, offering a quantitative basis for crop yield risk management, thereby providing early warning of potential crop growth problems and improving the practicality and reliability of the method.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. By using the rate of change in oxygen concentration as a factor in determining crop photosynthetic efficiency, this method accurately identifies the photosynthetic inhibition state of crops using a first baseline rate of change, Y1. When an abnormality in the rate of change in oxygen concentration, i.e., the oxygen production rate, is detected, a ventilation compensation mechanism can be immediately triggered to reduce the probability of crops being in a state of inefficient photosynthesis for a long time. By calculating the crop's oxygen production rate and oxygen consumption rate, the net primary productivity of the crop can be further calculated. For the oxygen consumption rate caused by crop respiration, a temperature-driven calculation model is used to calculate the oxygen consumption trend, thereby reducing errors caused by relying on human experience. By adjusting ventilation in real time under light conditions and adjusting supplemental lighting time under non-light conditions, the oxygen consumption trend can be determined, reducing errors caused by relying on human experience. Compared with the experience threshold judgment in traditional methods, this method can accurately diagnose the growth lag of crops, thereby further adjusting the lighting time of crops, reducing ineffective lighting energy consumption, providing data support for subsequent lighting strategy adjustments, and thus improving the accuracy of crop growth regulation.

[0038] 2. By introducing the first leaf area index The second leaf area index is introduced as a compensation coefficient for the photosynthetic efficiency of crops. As a respiration consumption compensation coefficient for crops, it can effectively solve the problem that fixed parameters in basic calculation models cannot be applied to different crop varieties or different growth stages of crops. For example, it can reduce the respiration consumption of crops during the seedling stage when the leaf area is small. Weighting is increased during the peak flowering period when crops have a large leaf area. The weights ensure that the NPP calculation model accurately matches the growth cycle of crops and the characteristics of each growth stage.

[0039] 3. By periodically measuring the actual leaf area and root-stem dry matter weight of crops, the first and second leaf area indices are dynamically calibrated, reducing the quantitative error of actual crop growth. Compared with traditional calculation models, this method can reduce the probability of energy waste caused by the system, optimize the model preset value in a timely manner, and provide early warning of potential crop growth problems through the calculation model, further improving the practicality and reliability of the method, reducing potential system errors, and thus improving the accuracy of crop growth regulation.

[0040] 4. When it is detected that the crops are not under light conditions and the rate of change in oxygen concentration is greater than the second baseline rate of change. When ventilation is complete, turn on the ventilation equipment and ventilate according to the standard ventilation rate. After ventilation is complete, repeatedly monitor whether the rate of change of oxygen concentration is greater than the second baseline rate of change. By dynamically capturing changes in crop respiration efficiency at night, it is beneficial to accurately identify changes in crop accumulation at night. When the rate of change in oxygen concentration exceeds the second baseline rate of change... When the accumulation of anaerobic metabolites in greenhouse crops is vigorous, triggering ventilation intervention can reduce the accumulation rate of metabolites and decrease the probability of damage caused by root respiration stress, such as persistent soil hypoxia caused by cloudy days. This allows for timely protection of crop yields and facilitates precise energy delivery. Compared to traditional methods that rely on fixed-time ventilation, this method can reduce post-harvest losses, provide early warning of potential crop growth problems, improve the practicality and reliability of the method, and ultimately enhance the precision of crop growth regulation. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings:

[0042] Figure 1 This is a flowchart illustrating a smart agriculture monitoring and management method based on the Internet of Things (IoT) according to an embodiment of the present invention. Detailed Implementation

[0043] The following will be based on embodiments of the present invention. Figure 1 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0044] This embodiment discloses a smart agriculture monitoring and management method based on the Internet of Things, referring to... Figure 1 This includes the following steps:

[0045] S1. Data Acquisition Steps: Real-time acquisition of oxygen concentration, temperature, and light intensity in the greenhouse environment; calculation of the oxygen concentration change rate, the daily average light intensity I, and the daily average temperature T; acquisition of the preset lighting time for the current planting cycle. Given the planting area S, obtain the soil moisture θ in the greenhouse environment.

[0046] S2. First judgment step: If the crop is currently under light conditions, execute the first monitoring step S3; otherwise, execute the first calculation step S5.

[0047] S3, First Monitoring Step: When it is detected that the crop is under light conditions and the rate of change in oxygen concentration is less than the first baseline rate of change. At that time, the first control step S4 is executed.

[0048] S4. First control step: Turn on the ventilation equipment and ventilate according to the standard ventilation volume. After ventilation is completed, repeat the first judgment step S2.

[0049] S5. First Calculation Step: Crop Oxygen Production Rate The calculation model is as follows: ,in For the maximum photosynthetic rate, Let be the half-saturation light intensity constant, and α be the light energy utilization efficiency constant. The range of values ​​for α is: The value of α is determined based on crop experiments, and the crop oxygen consumption rate The calculation model is as follows: ,in As the reference temperature, For crops at the reference temperature The respiratory rate at which the respiratory rate is reduced The coefficient of temperature rise. The value can be: .

[0050] S6. Periodic Calibration Steps: Based on the current planting cycle of the crop, measure the average leaf area G and the dry weight W of the crop root and stem according to the specified measurement cycle. The preset value for the leaf area in the current planting cycle is... The preset value for the dry weight of roots and stems in the current planting cycle is .

[0051] S7. Analysis Steps: Calculate the net primary productivity (NPP) of crops. The calculation model for NPP is as follows: ,in The length of a day, The first leaf area index, The range of values ​​is , The specific calculation model for the value is as follows: , The second leaf area index, The range of values ​​is , The specific calculation model for the value is as follows: , For soil microbial oxygen consumption rate, The calculation model is as follows: Where k is the soil microbial respiration rate, the value of which is determined based on soil microbial experiments, and γ is the humidity coefficient. Standard soil moisture.

[0052] S8. Second judgment step: If NPP is less than the target net primary productivity (NPB), the crop accumulation is judged to be insufficient, and the second regulation step S9 is executed. Conversely, if NPP is greater than or equal to the target net primary productivity (NPB), the crop accumulation is judged to be qualified, and the second monitoring step S10 is executed.

[0053] S9. Second adjustment step: Supplement the day's lighting time according to the supplementary lighting time, which is as follows: Then, perform the third monitoring step S12.

[0054] S10, Second Monitoring Step: When it is detected that the crops are not under light conditions and the rate of change in oxygen concentration is greater than the second baseline rate of change. At that time, the third control step S11 is executed.

[0055] S11. Third control step: Turn on the ventilation equipment and ventilate according to the standard ventilation volume. After ventilation is completed, execute the second monitoring step S10.

[0056] S12, Third monitoring step: Record the number of times the cumulative amount of crops is judged to be insufficient within the specified monitoring period. When the number of times the cumulative amount of crops is judged to be insufficient is greater than the preset number, execute the feedback step S13.

[0057] S13. Feedback Step: Generate an early warning of abnormal crop growth and send it to the management terminal.

[0058] The implementation principle of this embodiment of a smart agriculture monitoring and management method based on the Internet of Things is as follows:

[0059] Multiple IoT sensors are installed in the crop greenhouse to acquire real-time data on oxygen concentration, temperature, light intensity, and soil moisture (θ) in the greenhouse environment. The sensors also calculate the rate of change in oxygen concentration, the average daily light intensity (I), and the average daily temperature (T) to determine the preset lighting time for the current crop's growing cycle. And the planting area S.

[0060] To determine whether the crops are currently under sunlight, if so, monitor whether the rate of change in oxygen concentration in the greenhouse is less than the first baseline rate of change. If less than If ventilation is not possible, turn on the ventilation equipment and ventilate according to the standard ventilation volume. After ventilation is completed, repeatedly monitor whether the oxygen concentration change rate is less than [a certain value]. If the crops are not currently under sunlight, it means the day's sunlight hours have ended. Calculate the crop's oxygen production rate. This allows for the further calculation of net primary productivity (NPP) of crops.

[0061] By using the rate of change in oxygen concentration as a factor in determining crop photosynthetic efficiency, this method accurately identifies the photosynthetic inhibition state of crops using a first baseline rate of change, Y1. When an abnormality in the rate of change in oxygen concentration, i.e., the oxygen production rate, is detected, a ventilation compensation mechanism can be immediately triggered, reducing the probability of crops being in a state of inefficient photosynthesis for a long time. Thus, it can compensate for the photosynthetic efficiency of crops in situations such as cloudy days or equipment failure. Compared with traditional methods that rely solely on thresholds such as carbon dioxide concentration or light intensity, this method can precisely regulate the growth status and biomass accumulation rate of crops, reduce errors caused by reliance on human experience, and thereby improve the accuracy of crop growth regulation.

[0062] Crop oxygen production rate The calculation model is as follows: Oxygen consumption rate of crops The calculation model is as follows: This method calculates crop oxygen production and consumption rates to further determine net primary productivity (NPP). For oxygen consumption caused by crop respiration, a temperature-driven calculation model is used to determine oxygen consumption trends, reducing errors caused by reliance on human experience. By dynamically quantifying crop biomass through NPP, this method can accurately diagnose crop growth lag compared to traditional methods that rely on empirical thresholds. This allows for adjustments to lighting time, compensating for growth gaps and reducing ineffective lighting energy consumption, providing data support for subsequent lighting strategy adjustments, and ultimately improving the accuracy of crop growth regulation.

[0063] Based on the current crop growing cycle, the average leaf area G and root and stem dry weight W of the crop are measured according to a specified measurement cycle. The preset value for leaf area in the current growing cycle is... The preset value for the dry weight of roots and stems in the current planting cycle is This allows for the calculation of the first leaf area index and the second leaf area index.

[0064] The calculation model for net primary productivity of crops is as follows: The calculation model for the first leaf area index is as follows: The calculation model for the second leaf area index is as follows: The calculation model for soil microbial oxygen consumption rate is as follows: .

[0065] By introducing the first leaf area index The second leaf area index is introduced as a compensation coefficient for the photosynthetic efficiency of crops. As a respiration consumption compensation coefficient for crops, it can effectively solve the problem that fixed parameters in basic calculation models cannot be applied to different crop varieties or different growth stages of crops. For example, it can reduce the respiration consumption of crops during the seedling stage when the leaf area is small. Weighting is increased during the peak flowering period when crops have a large leaf area. Weights are assigned to ensure that the NPP calculation model accurately matches the crop growth cycle and the characteristics of each growth stage. Leaf area index reflects the relationship between canopy light distribution and photosynthetic efficiency; when crops are planted at high density... When the value approaches 1, the system can improve... The calculated weights are used to reflect the photosynthetic efficiency advantage of the population, while simultaneously improving... The value is increased The calculation weights are used to reflect the increased respiration consumption caused by overlapping leaves and shading, thereby reducing the quantitative error of the actual growth of crops.

[0066] Furthermore, in extreme environments such as high temperatures, some crops may reduce transpiration by decreasing leaf area. Traditional calculation methods can easily misjudge crop growth decline, leading to excessive light compensation. By dynamically correcting the net primary productivity calculation model using the first and second leaf area indices, potential systematic errors can be reduced, protecting the crop's self-regulation mechanism and improving the method's practicality and regional adaptability. In particular, the model's applicability in perennial crops or high-density planting scenarios is enhanced, reducing the quantitative error of actual crop growth and thus improving the accuracy of crop growth regulation.

[0067] By periodically measuring the actual leaf area of ​​crops and comparing it with preset leaf area values, the first leaf area index is dynamically calibrated, reducing the quantitative error in the actual growth of crops. For example, this is useful when crops encounter pests or diseases. Lowering the value to match the decline in photosynthetic efficiency caused by leaf damage reduces the probability of energy waste compared to excessive supplemental lighting based on healthy crop models. Furthermore, if the actual leaf area consistently exceeds the preset value without a corresponding increase in net primary productivity, the computational model can provide early warnings of potential crop growth problems, such as nutrient deficiency or light intensity mismatch. This allows for timely optimization of the model's preset value, further improving the method's practicality and reliability, reducing potential systematic errors, and ultimately enhancing the accuracy of crop growth regulation.

[0068] By periodically measuring the dry matter weight of crop roots and stems and comparing it with a preset value, the second leaf area index is dynamically adjusted. Since there is a certain correlation between the respiration efficiency of crop roots and stems and leaf area, the dry matter weight of roots and stems is used to compensate for the respiration consumption of crops, thereby reducing the quantitative error of the actual growth of crops. For example, in the hot season, some crops actively reduce dry matter accumulation to reduce respiration consumption. The value is reduced to dynamically lower Compared to traditional computational models that rely on unnecessary cooling interventions, this method reduces the probability of energy waste in the system. It can also provide early warnings of potential crop growth problems, such as water and fertilizer imbalances, through computational models, enabling timely optimization of model presets. This further improves the practicality and reliability of the method, reduces potential system errors, and ultimately enhances the accuracy of crop growth regulation.

[0069] By calculating the oxygen consumption rate of soil microorganisms and modifying the calculation model, the temperature-dependent oxygen consumption of microorganisms can be quantified. Traditional methods for calculating net primary productivity only consider plant respiration and ignore the fact that microorganisms consume a lot of oxygen at suitable temperatures, especially in the early stages of organic fertilizer application. This allows the modified calculation model to reduce the probability of misjudging crop growth conditions and the probability of supplemental lighting decisions failing due to microbial oxygen consumption. The model is also compatible with different types of planting soils, which helps solve the model adaptation problem for cross-regional planting of the same crop. The model can be optimized in a timely manner, further improving the practicality and reliability of the method, reducing potential systematic errors, and thus improving the accuracy of crop growth regulation.

[0070] By monitoring soil moisture data and further correcting the oxygen consumption rate of soil microorganisms, compared to the linear model calculation method in traditional approaches, this method can dynamically calibrate the trend of microbial oxygen consumption based on soil moisture conditions. When θ deviates from the standard soil moisture level... When the soil microbial oxygen consumption rate drops sharply, it can accurately reflect the value of soil microbial oxygen consumption rate, thereby reducing the probability of false or delayed triggering of ventilation decisions. This makes the method applicable to precise water management scenarios such as tidal irrigation or hydroponics, allowing the model to be optimized in a timely manner, further improving the practicality and reliability of the method, and thus improving the accuracy of crop growth regulation.

[0071] If net primary productivity (NPP) is less than the target net primary productivity (NPB), then the crop accumulation is deemed insufficient, and supplementary lighting time for the day is added. The supplementary lighting time is as follows: It records the number of times the cumulative crop yield is deemed insufficient within a specified monitoring period. When the number of times the cumulative crop yield is deemed insufficient exceeds the preset number, an abnormal crop growth warning is generated and sent to the management terminal.

[0072] By adjusting ventilation in real time under illumination and supplemental lighting time under non-illumination conditions, this method can determine oxygen consumption trends and reduce errors caused by reliance on human experience. Compared to traditional methods that rely on experience-based thresholds, this method can accurately diagnose crop growth lag, thereby further adjusting crop lighting time, reducing ineffective lighting energy consumption, providing data support for subsequent lighting strategy adjustments, and improving the accuracy of crop growth regulation. By statistically analyzing the number of times NPP (Natural Producer Product) fails to meet standards within a specified monitoring period, this method records the gradual stagnation of crop growth and sets buffer thresholds to reduce the probability of over-response. It provides early warning of accumulated crop growth abnormalities, offering quantitative evidence for crop yield risk management, thus identifying potential crop growth problems and improving the practicality and reliability of the method.

[0073] When it is detected that crops are not under light conditions and the rate of change in oxygen concentration is greater than the second baseline rate of change. When ventilation is complete, turn on the ventilation equipment and ventilate according to the standard ventilation rate. After ventilation is complete, repeatedly monitor whether the rate of change of oxygen concentration is greater than the second baseline rate of change. By dynamically capturing changes in crop respiration efficiency at night, it is beneficial to accurately identify changes in crop accumulation at night. When the rate of change in oxygen concentration exceeds the second baseline rate of change... When the accumulation of anaerobic metabolites in greenhouse crops is vigorous, triggering ventilation intervention can reduce the accumulation rate of metabolites and decrease the probability of damage caused by root respiration stress, such as persistent soil hypoxia caused by cloudy days. This allows for timely protection of crop yields and facilitates precise energy delivery. Compared to traditional methods that rely on fixed-time ventilation, this method can reduce post-harvest losses, provide early warning of potential crop growth problems, improve the practicality and reliability of the method, and ultimately enhance the precision of crop growth regulation.

[0074] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A smart agriculture monitoring and management method based on the Internet of Things, characterized in that, Includes the following steps: Data Acquisition: Real-time acquisition of oxygen concentration, temperature, and light intensity in the greenhouse environment; calculation of the rate of change in oxygen concentration, the average daily light intensity I, and the average daily temperature T; and acquisition of the preset lighting time for the current planting cycle. and planting area S; First judgment: If the crop is currently under sunlight, execute the first monitoring step; otherwise, execute the first calculation step. First monitoring: When crops are under sunlight and the rate of change in oxygen concentration is less than the first baseline rate of change. At that time, the first regulatory step shall be executed; First control: Turn on the ventilation equipment and ventilate according to the standard ventilation volume. After ventilation is completed, proceed with the first judgment step. First calculation: Crop oxygen production rate The calculation model is as follows: ,in For the maximum photosynthetic rate, Let be the half-saturation light intensity constant, and α be the light energy utilization efficiency constant. The range of values ​​for α is: Oxygen consumption rate of crops The calculation model is as follows: ,in As the reference temperature, For crops at the reference temperature The respiratory rate at which the respiratory rate is reduced The coefficient of temperature rise. The range of values ​​for is: ; Analysis: The net primary productivity (NPP) of crops is calculated using the following model: ,in The length of a day; Second judgment: If NPP is less than the target net primary productivity NPB, then the crop accumulation is deemed insufficient, and the second regulation step is executed; Second adjustment: Supplement the lighting time for the day, the supplementary lighting time is as follows: .

2. The smart agriculture monitoring and management method based on the Internet of Things according to claim 1, characterized in that: A periodic calibration step is also included between the first calculation step and the analysis step; Periodic calibration: Based on the current crop planting cycle, the average leaf area G and root and stem dry weight W of the crop are measured according to a specified measurement cycle. The preset value for leaf area in the current planting cycle is... The preset value for the dry weight of roots and stems in the current planting cycle is ; In the analysis steps, the calculation model for net primary productivity (NPP) was also revised. The revised NPP calculation model is as follows: ,in The first leaf area index, The second leaf area index, The specific calculation model for the value is as follows: , The specific calculation model for the value is as follows: .

3. The smart agriculture monitoring and management method based on the Internet of Things according to claim 1, characterized in that: It also includes a second monitoring step and a third control step; In the second judgment step, if NPP is greater than or equal to the target net primary productivity NPB, the crop accumulation is judged to be qualified, and the second monitoring step is executed. Second monitoring: When it is detected that the crops are not under light conditions and the rate of change in oxygen concentration is greater than the second baseline rate of change. At that time, the third regulatory step will be implemented; Third control: Turn on the ventilation equipment and ventilate according to the standard ventilation volume. After ventilation is completed, proceed to the second monitoring step.

4. The smart agriculture monitoring and management method based on the Internet of Things according to claim 1, characterized in that: In the analysis steps, the calculation model for net primary productivity (NPP) was also revised. The revised NPP calculation model is as follows: ,in For soil microbial oxygen consumption rate, The calculation model is as follows: , where k is the soil microbial respiration rate, and the value of k is determined by the experiment.

5. The smart agriculture monitoring and management method based on the Internet of Things according to claim 4, characterized in that: In the data acquisition step, the soil moisture θ in the greenhouse environment is also acquired; The analysis also included the oxygen consumption rate of soil microorganisms. The calculation model was revised, and after revision The computational model was updated to Where γ is the humidity coefficient. Standard soil moisture.

6. The smart agriculture monitoring and management method based on the Internet of Things according to claim 1, characterized in that: It also includes a third monitoring step and a feedback step; Third monitoring: Record the number of times the cumulative crop yield is deemed insufficient within a specified monitoring period. When the number of times the cumulative crop yield is deemed insufficient exceeds the preset number, a feedback step is executed. Feedback: Generate an early warning of abnormal crop growth and send it to the management terminal.

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