Photovoltaic tea garden pest and disease occurrence forecasting system and method

Abstract

The invention relates to the technical field of pest control, and discloses a photovoltaic tea garden pest occurrence forecasting system and method. According to the system and the method, by integrating a tea garden environment data acquisition unit, a data preprocessing unit, a tea garden environment data analysis unit, a pest and disease damage forecasting unit and a result visualization unit, data of various environment factors including illumination, wind speed, rainfall, temperature, soil humidity, soil PH value, nutrient content and the like can be automatically acquired and analyzed; dependence on manual inspection and experience judgment is reduced, farmers do not need to have professional tea tree planting knowledge any more and do not need to carry out tedious detailed inspection on each tea tree, and therefore labor cost and time cost are greatly reduced, occurrence of diseases and insect pests can be detected and early warned in time through an advanced processing and analysis algorithm, and the disease and insect pests are prevented from being damaged. Accurate and comprehensive monitoring of disease conditions of all tea trees in a tea garden is ensured, the efficiency of disease and pest management is improved, and the loss risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of pest control, and in particular to a photovoltaic tea garden pest occurrence monitoring and reporting system and method. Background Art

[0002] With the rapid development of modern agriculture and the continuous advancement of science and technology, the application of intelligent agricultural systems has gradually penetrated into tea garden management. Many emerging technologies have been applied to agricultural pest and disease monitoring, such as sensor technology, the Internet of Things (IoT), big data analysis, and artificial intelligence. These technologies can achieve fast and accurate environmental data collection and analysis, helping farmers monitor the climate and soil conditions in tea gardens in real time, thereby improving the early warning capabilities of pests and diseases.

[0003] Traditional pest and disease monitoring methods mainly rely on manual inspections and experience-based judgments. Tea garden managers are required to have professional knowledge of tea tree planting and to conduct detailed inspections of the disease conditions of each tea tree. This method is not only time-consuming, labor-intensive and costly, but it is also difficult to manually ensure timely and accurate monitoring and reporting of the disease conditions of all tea trees in the tea garden. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a photovoltaic tea garden pest and disease occurrence monitoring and reporting system and method. By integrating a tea garden environmental data acquisition unit, a data preprocessing unit, a tea garden environmental data analysis unit, a pest and disease monitoring and reporting unit and a result visualization unit, the system can automatically collect and analyze data on various environmental factors including light, wind speed, rainfall, temperature, soil moisture, soil pH value and nutrient content, thereby reducing dependence on manual inspections and empirical judgments. The application of this technology means that farmers no longer need to have professional tea tree planting knowledge, nor do they need to conduct tedious and detailed inspections of each tea tree, thereby greatly reducing labor and time costs. At the same time, the system uses advanced processing and analysis algorithms to detect and warn of the occurrence of pests and diseases in a timely manner, ensuring accurate and comprehensive monitoring of the disease conditions of all tea trees in the tea garden. This not only improves the efficiency of pest and disease management and reduces the risk of losses, but also provides strong technical support for the sustainable development of tea gardens, thereby solving the above-mentioned problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: a photovoltaic tea garden pest and disease occurrence monitoring and reporting system, comprising a tea garden environmental data collection unit, a tea garden environmental data preprocessing unit, a tea garden environmental data analysis unit, a pest and disease monitoring and reporting unit, and a result visualization unit;

[0008] The tea garden environment data collection unit is used to collect tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data every day and send them to the data preprocessing unit in a unified manner;

[0009] The tea garden environment data preprocessing unit filters and removes the tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data, and then sends the data to the tea garden environment data analysis unit;

[0010] The tea garden environmental data analysis unit calculates the tea garden average temperature, tea garden light intensity, tea garden relative humidity, tea garden leaf growth rate, tea leaf yellowing index, tea tree comprehensive resistance, tea tree insect population density and tea garden pest and disease occurrence probability according to the processed tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data;

[0011] The pest and disease monitoring unit performs a threshold analysis on the probability of occurrence of pests and diseases in the tea garden. When the probability of occurrence of pests and diseases in the tea garden exceeds the threshold of the probability of occurrence of pests and diseases in the tea tree set by the pest and disease monitoring unit, an early warning of the occurrence of pests and diseases in the tea garden is issued, and a prediction report of pests and diseases in the tea garden is generated and sent to the result visualization unit;

[0012] The result visualization unit displays the specific analysis and predicted values ​​in the pest prediction report on the tea garden pest monitoring screen.

[0013] Preferably, the calculation formula of the average temperature of the tea garden is as follows:

[0014]

[0015] In the formula, T avg represents the average temperature of the tea garden, N represents the total number of observation days, Cywd i represents the temperature of the i-th day, i represents the serial number of each day, It means to sum the temperatures monitored on the first day to the Nth day, and then divide it by the total number of observation days to get the average temperature of the tea garden.

[0016] Preferably, the calculation formula for the light intensity in the tea garden is as follows:

[0017]

[0018] In the formula, Gzqd represents the light intensity in the tea garden, M represents the total number of days of light observation, and Gplk j represents the illumination data in the jth period, j represents the serial number of the recorded period, It means adding up M light data values ​​and dividing the result by the total number of light observation days to calculate the light intensity in the tea garden.

[0019] Preferably, the calculation formula of the relative humidity of the tea garden is as follows:

[0020]

[0021] In the formula, Rhsd represents the relative humidity of the tea garden, Sjsy represents the actual water vapor pressure, and T avg represents the average temperature of the tea garden, represents the saturated water vapor pressure calculated based on the average temperature of the tea garden, where 6.11 represents the correction constant for the actual humidity value. Indicates the calculation of the change in saturated water vapor pressure at a specific temperature, 7.5*T avg represents the effect of temperature on saturated water vapor pressure, T avg +237.3 means the temperature is normalized. Index Used to maintain effective computational precision.

[0022] Preferably, the calculation formula for the growth rate of tea leaves is as follows:

[0023]

[0024] In the formula, Rk g Indicates the growth rate of tea leaves, Lt f represents the total area of ​​leaves at the end of the observation period, Lp i represents the leaf area at the beginning of the observation period, and t represents the length of time for recording the growth rate of tea leaves.

[0025] Preferably, the calculation formula of the tea leaf yellowing index is as follows:

[0026]

[0027] In the formula, Hhzs represents the tea leaf yellowing index, L yellow Indicates the total area of ​​yellowing leaves actually measured, L total Represents the observed area of ​​all leaves on the tea tree.

[0028] Preferably, the calculation formula of the comprehensive resistance of tea trees is as follows:

[0029]

[0030] In the formula, Czkx represents the comprehensive resistance of tea trees, Zrkx represents the natural resistance of tea tree varieties planted in tea gardens, Qhqd represents the intensity of insect infestation of tea trees under experimental conditions, Hjyl represents the soil moisture under the planted tea trees, and Qhqd*Hjyl represents the result of the interaction between the resistance and vulnerability of tea trees.

[0031] Preferably, the calculation formula of the tea tree insect population density is as follows:

[0032]

[0033] In the formula, Dp c represents the tea tree insect population density, Nq c It indicates the number of pests specifically recorded in the tea tree planting monitoring area, and Gcmj indicates the total area of ​​the monitored tea garden.

[0034] Preferably, the calculation formula for the probability of occurrence of tea garden pests and diseases is as follows:

[0035] P test =q1*T avg +q2*Gzqd+q3*Rhsd+q4*Rk g +q5*Hhzs-q6*Czkx+q7*Dp c

[0036] In the formula, P test represents the probability of occurrence of diseases and insect pests in tea gardens, q1, q2, q3, q4, q5, q6, and q7 represent the weight coefficients represented by their respective parameters, and the weight coefficients are qualitatively obtained through the hierarchical analysis method.

[0037] A photovoltaic tea garden pest and disease occurrence prediction method, characterized in that it comprises the following steps:

[0038] S1, collect tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environment data collection unit and send them to the data preprocessing unit;

[0039] S2, filtering and denoising the tea garden illumination data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environment data preprocessing unit, and then sending them to the tea garden environment data analysis unit;

[0040] S3, calculating the tea garden average temperature, tea garden light intensity, tea garden relative humidity, tea leaf growth rate, tea leaf yellowing index, tea tree comprehensive resistance and tea tree insect population density according to the processed tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environmental data analysis unit, and comprehensively calculating the probability of occurrence of tea garden diseases and insect pests;

[0041] S4. The pest and disease monitoring unit performs a threshold analysis on the probability of occurrence of pests and diseases in the tea garden. When the probability of occurrence of pests and diseases in the tea garden exceeds the threshold of the probability of occurrence of pests and diseases in the tea tree set by the pest and disease monitoring unit, an early warning of the occurrence of pests and diseases in the tea garden is issued, and a prediction report of pests and diseases in the tea garden is generated and sent to the result visualization unit;

[0042] S5. The specific analysis and predicted values ​​in the pest and disease prediction report are displayed on the tea garden pest monitoring screen through the result visualization unit.

[0043] Compared with the prior art, the present invention provides a photovoltaic tea garden pest and disease occurrence detection and reporting system and method, which has the following beneficial effects:

[0044] The present invention integrates a tea garden environment data acquisition unit, a data preprocessing unit, a tea garden environment data analysis unit, a pest and disease monitoring unit and a result visualization unit, so as to automatically collect and analyze data on various environmental factors including light, wind speed, rainfall, temperature, soil moisture, soil pH value and nutrient content, thereby reducing dependence on manual inspections and empirical judgments. The application of this technology means that farmers no longer need to have professional tea tree planting knowledge, nor do they need to conduct tedious detailed inspections on each tea tree, thereby greatly reducing labor costs and time costs. At the same time, the system uses advanced processing and analysis algorithms to detect and warn of the occurrence of pests and diseases in a timely manner, ensuring accurate and comprehensive monitoring of the disease conditions of all tea trees in the tea garden, which not only improves the efficiency of pest and disease management and reduces the risk of losses, but also provides strong technical support for the sustainable development of tea gardens. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the system flow of the present invention;

[0046] Figure 2 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The traditional pest and disease monitoring method mainly relies on manual inspection and experience judgment. This method is not only time-consuming, labor-intensive and costly, but also difficult to ensure timely and accurate monitoring of the disease conditions of all tea trees in the tea garden. Therefore, a photovoltaic tea garden pest and disease monitoring system is proposed. Please refer to Figure 1 ,The system includes a tea garden environmental data collection unit, a tea garden environmental data preprocessing unit, a tea garden environmental data analysis unit, a pest and disease monitoring unit, and a result visualization unit;

[0049] The tea garden environmental data acquisition unit is the core component of the photovoltaic tea garden pest and disease occurrence monitoring system. It is responsible for comprehensive and real-time collection of multiple environmental data closely related to the occurrence of pests and diseases. The unit uses a high-precision sensor network and combines the Internet of Things (IoT) technology to efficiently obtain important environmental parameters such as light data, wind speed, rainfall, temperature, soil moisture, soil pH value, and soil nutrient content in the tea garden. Specifically, light data is obtained through light sensors installed in the tea garden, wind speed is measured by wind speed sensors, rainfall is monitored with the help of rain gauges, and temperature and humidity are provided by meteorological sensors. These sensors are wirelessly connected to the central data processing unit and realize real-time data transmission through the wireless network Zigbee, ensuring the timeliness and accuracy of data collection. The monitoring of soil moisture, pH value and nutrient content is carried out through multi-parameter sensors buried in the soil. These sensors can feed back the real-time status of the soil to the data preprocessing unit. In addition, the system's built-in calibration and self-test mechanism can ensure the accuracy of sensor data, thereby providing a reliable basis for subsequent data analysis and pest and disease prediction.

[0050] The tea garden environmental data preprocessing unit filters and denoises the tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data, removes abnormal data, and then sends it to the tea garden environmental data analysis unit.

[0051] The data filtering formula is as follows:

[0052]

[0053] In the formula, SJLB trepresents the result after filtering the collected data at time t, K represents the size of the moving window, the larger the filter window, the smoother the filtering effect, X t-i It represents the data value collected at the i-th moment before the current time t. i ranges from 0 to K-1, which means K moments before the current time t. This filtering method can effectively remove noise and random interference from the collected tea garden environmental data, extract the real signal and trend, and thus provide more reliable and accurate basic data for subsequent data analysis and pest and disease prediction;

[0054] The tea garden environmental data analysis unit conducts in-depth analysis on a number of pre-processed tea garden environmental data to obtain indicators closely related to tea tree growth and the occurrence of pests and diseases. The unit uses relevant formulas to calculate the average temperature of the tea garden, sums up the temperature data of each time period and calculates the average value to reflect the changing trend of the overall climate conditions of the tea garden. The calculation of light intensity integrates the data collected by the light sensor, considers the changes in sunshine duration and intensity, and combines a specific light formula to evaluate the photosynthesis efficiency of tea leaves.

[0055] In addition, the calculation of relative humidity in the tea garden is based on the data of temperature and humidity sensors. The standard formula of relative humidity is used to quantify the relationship between actual humidity and saturated humidity. The tea garden environmental data analysis unit also uses the growth rate formula to evaluate the growth rate of tea leaves. It usually monitors the correlation between physiological indicators of tea tree growth and environmental factors, such as temperature and moisture. At the same time, the calculation method of tea leaf yellowing index relies on spectral feature data. The spectral response of leaves is analyzed by the ratio method to evaluate the health of vegetation.

[0056] When comprehensively considering soil data, the tea garden environmental data analysis unit will also calculate the comprehensive resistance of tea trees, evaluate the growth conditions and stress resistance of tea trees based on factors such as soil moisture, nutrient content and pH value, and calculate the insect population density of tea trees, that is, the number of insects per unit area, by analyzing statistical calculation numbers and density, combined with the data of pest monitoring equipment. Finally, all calculation results will be integrated through probability formulas to comprehensively evaluate the probability of pests and diseases in the tea garden, providing a scientific basis for subsequent management decisions;

[0057] The calculation formula for the average temperature of the tea garden is as follows:

[0058]

[0059] Temperature is one of the important factors affecting the occurrence of pests and diseases. The change of average temperature can help predict the possible occurrence of pests and diseases. By analyzing the temperature data, tea garden managers can take preventive measures in advance to reduce the impact of pests on tea. In the formula, T avg represents the average temperature of the tea garden, N represents the time period of temperature observation, Cywdi represents the temperature of the i-th day, i represents the serial number of each day, It means that the average temperature of the tea garden is obtained by adding up the temperatures observed every day from i=1 to N and then dividing it by the time period of temperature observation. By calculating the average temperature of the tea garden, we can better understand the growth environment of tea trees and adjust management measures such as fertilization, irrigation and pruning, thereby improving the growth and yield of tea trees.

[0060] The calculation formula for light intensity in tea garden is as follows:

[0061]

[0062] Light intensity directly affects the photosynthesis capacity of tea trees. Calculating light intensity can help managers understand light conditions and take measures to improve light in tea gardens (such as proper crown pruning) to promote healthy growth of tea trees. In the formula, Gzqd represents the light intensity in the tea garden, M represents the time period of light observation, and Gplk represents the time period of light observation. j represents the illumination data in the jth period, j represents the serial number of the recorded period, It means that each light data value is added and the result is divided by M to calculate the light intensity of the tea garden. Appropriate light intensity helps to improve the quality of tea. Insufficient light may cause poor growth of leaves and affect the aroma and taste of tea. By monitoring the light intensity, the planting conditions can be adjusted in time to improve the market value of tea.

[0063] The calculation formula for relative humidity in tea garden is as follows:

[0064]

[0065] Relative humidity is one of the important environmental factors that affect the occurrence of pests and diseases. By calculating the relative humidity, managers can predict the possible risk of diseases and take measures to prevent and control them in advance to reduce the losses caused by pests and diseases. In the formula, Rhsd represents the relative humidity of the tea garden, Sjsy represents the actual water vapor pressure, and T avg represents the average temperature of the tea garden, represents the saturated water vapor pressure calculated based on the average temperature of the tea garden, where 6.11 represents the correction constant for the actual humidity value. Indicates the calculation of the change in saturated water vapor pressure at a specific temperature, 7.5*T avg represents the effect of temperature on saturated water vapor pressure, T avg +237.3 means that the temperature is normalized. Monitoring relative humidity can guide the irrigation strategy of the tea garden, avoid soil salinization and root rot caused by over-irrigation, and ensure that the water demand of the tea trees is reasonably met;

[0066] The calculation formula of tea leaf growth rate is as follows:

[0067]

[0068] Monitoring leaf growth rate can provide growth change information, help managers adjust fertilization and water management strategies for different growth stages, and optimize the growth environment of tea trees. In the formula, Rk g Indicates the growth rate of tea leaves, Lt f represents the total area of ​​leaves at the end of the observation period, Lp i represents the leaf area at the beginning of the observation period, and t represents the length of time for recording the growth rate of tea leaves. By calculating the growth rate of tea leaves, tea garden managers can quantitatively evaluate the growth status of tea trees and help to timely determine whether the tea trees are in a healthy growth state and whether their growth is restricted;

[0069] The calculation formula of tea leaf yellowing index is as follows:

[0070]

[0071] The leaf yellowing index can effectively reflect the physiological state and nutritional status of tea trees, timely identify the lack of nutrition or damage by pests and diseases, and achieve early diagnosis and intervention. In the formula, Hhzs represents the leaf yellowing index of tea trees, L yellow Indicates the total area of ​​yellowing leaves actually measured, L total It means observing the area of ​​all leaves on the tea tree. A high yellowing index usually means that the tea tree is in poor health. Timely treatment of yellowing problems can ensure stable tea quality and protect the economic benefits of the tea garden.

[0072] The calculation formula of tea tree comprehensive resistance is as follows:

[0073]

[0074] Understanding the comprehensive resistance of tea trees will help develop targeted pest and disease control and management measures, thereby improving the overall resistance of tea gardens. In the formula, Czkx represents the comprehensive resistance of tea trees, Zrkx represents the natural resistance of tea tree varieties planted in tea gardens, Qhqd represents the intensity of insect damage to tea trees under experimental conditions, and Hjyl represents the soil moisture under which tea trees are planted. By calculating the comprehensive resistance of tea trees, managers can evaluate the performance of tea trees in the face of adverse conditions such as pests and diseases, drought and climate change, and choose more suitable tea tree varieties for planting;

[0075] The calculation formula of tea tree insect population density is as follows:

[0076]

[0077] Monitoring the insect population density of tea trees can timely capture changes in pests, provide a scientific basis for early warning of pests and diseases, and reduce the damage of insect disasters to tea leaves through rapid response. In the formula, Dp c represents the tea tree insect population density, Nq c It indicates the number of pests recorded in the tea tree planting monitoring area, and Gcmj indicates the total area of ​​the monitored tea garden. The real-time insect population density information helps managers formulate targeted prevention and control measures, choose the appropriate method and time to apply pesticides, so as to achieve effective control of pests and reduce the frequency and amount of chemical drugs used;

[0078] The calculation formula for the probability of occurrence of tea garden pests and diseases is as follows:

[0079] P test =q1*T avg +q2*Gzqd+q3*Rhsd+q4*Rk g +q5*Hhzs-q6*Czkx+q7*Dp c

[0080] By comprehensively calculating the probability of occurrence of diseases and insect pests in tea gardens, we can have a more comprehensive understanding of potential threats, implement overall prevention and control strategies, and ensure the healthy growth of tea gardens. In the formula, P test represents the probability of occurrence of diseases and insect pests in tea gardens, q1, q2, q3, q4, q5, q6, q7 represent the weight coefficients represented by their respective parameters, and the weight coefficients are qualitatively obtained through the hierarchical analysis method. The risk of diseases and insect pests can be identified in advance, and timely measures can be taken to prevent the spread of pests, which can effectively improve the yield and quality of tea gardens, thereby achieving higher economic benefits;

[0081] The steps for obtaining the weight coefficient are as follows:

[0082] A. Organize the various aspects of the problem into a hierarchical structure, which usually includes the following three layers:

[0083] The first layer: target layer, the probability of occurrence of pests and diseases in tea gardens;

[0084] The second layer: the principle layer, the main factors affecting the occurrence of pests and diseases, such as temperature, humidity, etc.;

[0085] The third level: sub-criteria level, further refines each main factor;

[0086] B. Design a comparison matrix. Compare each pair of factors to generate a comparison matrix. The comparison matrix is ​​a square matrix in which each factor is compared with other factors to determine its relative importance. The importance can be scored on a scale of 1 to 9:

[0087] 1: Equally important;

[0088] 3: Slightly important;

[0089] 5: Important;

[0090] 7: Very important;

[0091] 9: Absolutely important;

[0092] C. Use the comparison matrix to calculate the weight vector. The steps are as follows:

[0093] 1. Divide each element in the comparison matrix by the sum of the column;

[0094] 2. Average the normalized values ​​of each row to get the weight value of each factor;

[0095] D. Summarize the weight coefficients of each factor to form a final weight list for calculating the probability of occurrence of pests and diseases in tea gardens;

[0096] The pest and disease monitoring unit sets the temperature in the tea garden to a suitable growth range of 18-28 degrees Celsius. At the same time, if the light intensity is continuously lower than 300 μmol / m 2 / s, and the relative humidity is higher than 85%, the system will automatically identify the potential high risk of pests and diseases. When the above parameters are calculated and the probability of occurrence of pests and diseases in the tea garden exceeds the warning threshold of 50%, the system will immediately trigger the warning mechanism. In addition, by monitoring the insect density and setting the insect density threshold, when the insect density per unit area exceeds 5, the pest prevention measures will be further strengthened. Once an early warning occurs, a detailed tea garden pest and disease forecast report will be generated. The report includes a risk assessment of pest and disease occurrence, a map of the affected area, the predicted main pest types and response measures, and prevention suggestions, such as changes in fertilization strategies and the timing of pesticide application. The report will then be automatically sent to the result visualization unit, and the prediction results will be displayed using the data visualization tool Tableau, including risk heat maps, trend charts, and key indicator dashboards, to help managers intuitively understand the dynamic changes in the occurrence of pests and diseases in the tea garden.

[0097] See also Figure 2 A photovoltaic tea garden pest and disease occurrence prediction method, characterized in that it comprises the following steps:

[0098] S1, collect tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environment data collection unit and send them to the data preprocessing unit;

[0099] S2, filtering and denoising the tea garden illumination data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environment data preprocessing unit, and then sending them to the tea garden environment data analysis unit;

[0100] S3, calculating the tea garden average temperature, tea garden light intensity, tea garden relative humidity, tea leaf growth rate, tea leaf yellowing index, tea tree comprehensive resistance and tea tree insect population density according to the processed tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environmental data analysis unit, and comprehensively calculating the probability of occurrence of tea garden diseases and insect pests;

[0101] S4. The pest and disease monitoring unit performs a threshold analysis on the probability of occurrence of pests and diseases in the tea garden. When the probability of occurrence of pests and diseases in the tea garden exceeds the threshold of the probability of occurrence of pests and diseases in the tea tree set by the pest and disease monitoring unit, an early warning of the occurrence of pests and diseases in the tea garden is issued, and a prediction report of pests and diseases in the tea garden is generated and sent to the result visualization unit;

[0102] S5. The specific analysis and predicted values ​​in the pest and disease prediction report are displayed on the tea garden pest monitoring screen through the result visualization unit.

[0103] Through the comprehensive application of the above-mentioned systems and methods, the system can automatically collect and analyze data on various environmental factors including light, wind speed, rainfall, temperature, soil moisture, soil pH and nutrient content, reducing reliance on manual inspections and empirical judgments. Farmers no longer need to have professional knowledge of tea tree planting, nor do they need to conduct tedious and detailed inspections of each tea tree, thereby greatly reducing labor and time costs. At the same time, the system uses advanced processing and analysis algorithms to detect and warn of the occurrence of pests and diseases in a timely manner, thereby improving the accuracy of prediction of pests and diseases in tea gardens.

[0104] Embodiment 1:

[0105] In this experiment, data was collected through temperature sensors, humidity sensors, and light sensors. The temperature on the first day was 24 degrees Celsius, the temperature on the second day was 26 degrees Celsius, and the temperature on the third day was 25 degrees Celsius;

[0106] Substitute the above collected data into the calculation formula of the average temperature of the tea garden To verify:

[0107] Celsius, the maximum set temperature of the tea garden is 30, in order to calculate the standardization,

[0108] Light intensity in tea garden Gzqd = 0.6;

[0109] Relative humidity in tea garden Rhsd = 70% = 0.7;

[0110] The average weight gain of tea leaves per week is 5 grams, which is converted into a weekly growth rate of Tea leaf yellowing index Hhzs = 0.4, tea comprehensive resistance Czkx = 0.6, tea insect population density Dp c =0.2;

[0111] Among them, the weight coefficient obtained by the hierarchical analysis method is as follows:

[0112] q1=0.2, q2=0.25, q3=0.15, q4=0.1, q5=0.15, q6=0.1, q7=0.05;

[0113] The calculation formula for the probability of occurrence of tea garden pests and diseases is as follows:

[0114] P test =q1*T avg +q2*Gzqd+q3*Rhsd+q4*Rk g +q5*Hhzs-q6*Czkx+q7*Dp c =0.2*0.833+0.25*0.6+0.15*0.7+0.1*0.5+0.15*(1-0.4)+0.1*0.6+0.05*0.2=0.5336;

[0115] That is, the probability of occurrence of tea garden pests and diseases is 53.36%. Since the probability of occurrence of tea garden pests and diseases exceeds 50%, the early warning mechanism of tea garden pests and diseases is triggered at this time;

[0116] Embodiment 2:

[0117] In this experiment, data is collected through devices such as temperature sensors, humidity sensors, and light sensors:

[0118] T avg = 22 degrees Celsius, the maximum set temperature of the tea garden is 30, in order to calculate the standardization,

[0119] Light intensity in tea garden Gzqd = 0.45;

[0120] Relative humidity in tea garden Rhsd = 60% = 0.6;

[0121] The average weight gain of tea leaves per week is 2 grams, which is converted into a weekly growth rate of Tea leaf yellowing index Hhzs = 0.7, tea comprehensive resistance Czkx = 0.4, tea insect population density Dp c =0.04;

[0122] Among them, the weight coefficient obtained by the hierarchical analysis method is as follows:

[0123] q1=0.2, q2=0.25, q3=0.15, q4=0.1, q5=0.1, q6=0.1, q7=0.1;

[0124] The calculation formula for the probability of occurrence of tea garden pests and diseases is as follows:

[0125] P test =q1*T avg +q2*Gzqd+q3*Rhsd+q4*Rk g +q5*Hhzs-q6*Czkx+q7*Dp c =0.2*0.733+0.25*0.45+0.15*0.6+0.1*0.2+0.1*(1-0.7)+0.1*0.4+0.1*0.04=0.4461;

[0126] That is, the probability of occurrence of diseases and insect pests in tea gardens is 44.61%. Since the probability of occurrence of diseases and insect pests in tea gardens does not exceed 50%, it means that the growth environment of the tea garden is in a suitable state.

[0127] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A photovoltaic tea garden pest and disease occurrence detection and reporting system, characterized by: It includes tea garden environmental data collection unit, tea garden environmental data preprocessing unit, tea garden environmental data analysis unit, pest and disease monitoring unit and result visualization unit; The tea garden environment data collection unit is used to collect tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data every day and send them to the data preprocessing unit in a unified manner; The data preprocessing unit filters and removes the tea garden illumination data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data, and then sends the data to the tea garden environment data analysis unit; The tea garden environmental data analysis unit calculates the tea garden average temperature, tea garden light intensity, tea garden relative humidity, tea garden leaf growth rate, tea leaf yellowing index, tea tree comprehensive resistance, tea tree insect population density and tea garden pest and disease occurrence probability according to the processed tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data; The pest and disease monitoring unit performs a threshold analysis on the probability of occurrence of pests and diseases in the tea garden. When the probability of occurrence of pests and diseases in the tea garden exceeds the threshold of the probability of occurrence of pests and diseases in the tea tree set by the pest and disease monitoring unit, an early warning of the occurrence of pests and diseases in the tea garden is issued, and a prediction report of pests and diseases in the tea garden is generated and sent to the result visualization unit; The result visualization unit displays the specific analysis and predicted values ​​in the pest prediction report on the tea garden pest monitoring screen.

2. A photovoltaic tea garden pest and disease occurrence detection and reporting system according to claim 1, characterized in that: The calculation formula of the average temperature of the tea garden is as follows: In the formula, T avg represents the average temperature of the tea garden, N represents the total number of observation days, Cywd i represents the temperature of the i-th day, i represents the serial number of each day, It means to sum the temperatures monitored on the first day to the Nth day, and then divide it by the total number of observation days to get the average temperature of the tea garden.

3. A photovoltaic tea garden pest and disease occurrence detection and reporting system according to claim 2, characterized in that: The calculation formula of the light intensity in the tea garden is as follows: In the formula, Gzqd represents the light intensity in the tea garden, M represents the total number of days of light observation, and Gplk j represents the illumination data in the jth period, j represents the serial number of the recorded period, It means adding up M light data values ​​and dividing the result by the total number of light observation days to calculate the light intensity in the tea garden.

4. A photovoltaic tea garden pest and disease occurrence detection and reporting system according to claim 3, characterized in that: The calculation formula of the relative humidity of the tea garden is as follows: In the formula, Rhsd represents the relative humidity of the tea garden, Sjsy represents the actual water vapor pressure, and T avg represents the average temperature of the tea garden, represents the saturated water vapor pressure calculated based on the average temperature of the tea garden, where 6.11 represents the correction constant for the actual humidity value. Indicates the calculation of the change in saturated water vapor pressure at a specific temperature, 7.5*T avg represents the effect of temperature on saturated water vapor pressure, T avg +237.3 means the temperature is normalized. Index Used to maintain effective computational precision.

5. A photovoltaic tea garden pest and disease occurrence detection and reporting system according to claim 4, characterized in that: The calculation formula of the tea leaf growth rate is as follows: In the formula, Rk g Indicates the growth rate of tea leaves, Lt f represents the total area of ​​leaves at the end of the observation period, Lp i represents the leaf area at the beginning of the observation period, and t represents the length of time for recording the growth rate of tea leaves.

6. A photovoltaic tea garden pest and disease occurrence detection and reporting system according to claim 5, characterized in that: The calculation formula of the tea leaf yellowing index is as follows: In the formula, Hhzs represents the tea leaf yellowing index, L yellow Indicates the total area of ​​yellowing leaves actually measured, L total Represents the observed area of ​​all leaves on the tea tree.

7. A photovoltaic tea garden pest and disease occurrence detection and reporting system according to claim 6, characterized in that: The calculation formula of the comprehensive resistance of tea tree is as follows: In the formula, Czkx represents the comprehensive resistance of tea trees, Zrkx represents the natural resistance of tea tree varieties planted in tea gardens, Qhqd represents the intensity of insect infestation of tea trees under experimental conditions, Hjyl represents the soil moisture under the planted tea trees, and Qhqd*Hjyl represents the result of the interaction between the resistance and vulnerability of tea trees.

8. A photovoltaic tea garden pest and disease occurrence monitoring and reporting system according to claim 7, characterized in that: The calculation formula of the tea tree insect population density is as follows: In the formula, Dp c represents the tea tree insect population density, Nq c It indicates the number of pests specifically recorded in the tea tree planting monitoring area, and Gcmj indicates the total area of ​​the monitored tea garden.

9. A photovoltaic tea garden pest and disease occurrence monitoring and reporting system according to claim 8, characterized in that: The calculation formula for the occurrence probability of tea garden pests and diseases is as follows: P test =q1*T avg +q2*Gzqd+q3*Rhsd+q4*Rk g +q5*Hhzs-q6*Czkx+q7*Dp c In the formula, P test represents the probability of occurrence of diseases and insect pests in tea gardens, q1, q2, q3, q4, q5, q6, and q7 represent the weight coefficients represented by their respective parameters, and the weight coefficients are qualitatively obtained through the hierarchical analysis method.

10. A method for detecting and reporting the occurrence of pests and diseases in a photovoltaic tea garden, characterized in that: The following steps are involved: S1, collect tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environment data collection unit and send them to the data preprocessing unit; S2, filtering and denoising the tea garden illumination data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environment data preprocessing unit, and then sending them to the tea garden environment data analysis unit; S3, calculating the tea garden average temperature, tea garden light intensity, tea garden relative humidity, tea leaf growth rate, tea leaf yellowing index, tea tree comprehensive resistance and tea tree insect population density according to the processed tea garden light data, wind speed data, rainfall data, temperature data, soil moisture data, soil pH value and soil nutrient content data through the tea garden environmental data analysis unit, and comprehensively calculating the probability of occurrence of tea garden diseases and insect pests; S4. The pest and disease monitoring unit performs a threshold analysis on the probability of occurrence of pests and diseases in the tea garden. When the probability of occurrence of pests and diseases in the tea garden exceeds the threshold of the probability of occurrence of pests and diseases in the tea tree set by the pest and disease monitoring unit, an early warning of the occurrence of pests and diseases in the tea garden is issued, and a prediction report of pests and diseases in the tea garden is generated and sent to the result visualization unit; S5. The specific analysis and predicted values ​​in the pest and disease prediction report are displayed on the tea garden pest monitoring screen through the result visualization unit.

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