Tea production cycle monitoring technology based on remote sensing image

Abstract

The invention discloses a tea production cycle monitoring technology based on remote sensing images, particularly relates to the field of remote sensing image calculation, and comprises a monitoring area division module, a monitoring data acquisition module, a monitoring data analysis module, a comprehensive analysis module and an early warning module. According to the invention, through a tea production period monitoring technology based on remote sensing images, accurate monitoring and management of the whole production process of the tea garden are realized, through monitoring area division, data acquisition, analysis, comprehensive evaluation, early warning and other modules, the accuracy and real-time performance of monitoring data are improved, the scientificity and efficiency of tea garden management are enhanced, and in addition, the tea production period monitoring system is suitable for popularization and application. Potential problems can be found and early warned in time, powerful decision support is provided for tea garden managers, the quality and benefits of tea production are improved, and sustainable development of the tea industry is promoted.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image technology, and more specifically, to a technology for monitoring the tea production cycle based on remote sensing imagery. Background Technology

[0002] In the tea industry, monitoring the growth cycle of tea trees is a key link in ensuring the quality and yield of tea. Traditionally, this process mainly relies on manual on-site observation and recording, which is not only inefficient but also difficult to fully cover large areas of tea gardens. In addition, it is greatly limited by natural conditions such as weather and terrain, resulting in the lag and inaccuracy of monitoring data. Furthermore, although ground observation stations can provide relatively detailed data, their construction and maintenance costs are high and their distribution is limited, making it difficult to meet the needs of modern tea production for refined management.

[0003] With the rapid advancement of remote sensing technology, its application in agriculture has shown great potential. Remote sensing technology can use sensors mounted on high-altitude platforms such as satellites and drones to quickly and accurately acquire surface information, including various parameters such as vegetation cover, soil moisture, temperature, and light intensity. This provides a new technical means for monitoring the tea production cycle. However, existing tea production cycle monitoring technologies based on remote sensing images still face many challenges. On the one hand, the technical system is not yet perfect, and most systems only focus on one or a few aspects of the tea tree's growth environment, failing to fully reflect the comprehensive influence of multiple factors on tea tree growth. On the other hand, data processing and analysis methods are not mature enough, resulting in the need to improve the accuracy and practicality of monitoring results. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a tea production cycle monitoring technology based on remote sensing images, which solves the problems mentioned in the background art through the following solutions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a tea production cycle monitoring technology based on remote sensing imagery, comprising:

[0006] Monitoring area division module: used to determine the target tea garden as the target monitoring area, and divide the target monitoring area into monitoring sub-areas by dividing it into equal areas, and label them as 1, 2...n in sequence;

[0007] Monitoring data acquisition module: used to collect tea tree growth environment data, tea tree physiological status data, tea garden management data, and tea yield prediction data in each monitoring sub-region, and transmit the collected data to the monitoring data analysis module;

[0008] The monitoring data analysis module includes a tea tree growth environment data analysis unit, a tea tree physiological state data analysis unit, a tea garden management data analysis unit, and a tea yield prediction data analysis unit. It is used to analyze the data transmitted by the monitoring data acquisition module and transmit the analyzed data to the comprehensive analysis module.

[0009] Comprehensive Analysis Module: Used to establish a comprehensive analysis model, import the data transmitted from the monitoring data analysis module into the comprehensive analysis model, calculate the comprehensive anomaly index of each monitoring sub-region, and transmit it to the early warning module;

[0010] Early warning module: used to establish a preset value for the comprehensive anomaly index, to judge the comprehensive anomaly index of each monitoring sub-area, and to issue an early warning signal based on the judgment result.

[0011] Preferably, the tea tree growth environment data includes soil moisture, air temperature gradient, light intensity variance, and wind direction change frequency, labeled as Rs, R1, Ra, and Rw, respectively; the tea tree physiological state data includes leaf chlorophyll content, leaf water content ratio, leaf temperature fluctuation range, and photosynthetic efficiency index, labeled as Lc, Lm, Lt, and Lp, respectively; the tea garden management data includes fertilization density difference, irrigation water deviation, average weeding frequency, and pest and disease control input, labeled as Qf, Ql, Qm, and Qp, respectively; and the tea yield prediction data includes tea tree bud density, new leaf growth rate, proportion of harvestable leaves, and tea maturity cycle shortening rate, labeled as Bd, B1, Bp, and Bc, respectively.

[0012] Preferably, the tea tree growth environment data analysis unit is used to establish a tea tree growth environment data analysis model, import the tea tree growth environment data transmitted by the monitoring data acquisition module into the tea tree growth environment data analysis model, and calculate the tea tree growth environment assessment value for each monitoring sub-region, specifically expressed as follows: TR i Rs represents the tea tree growth environment assessment value of the i-th monitoring sub-region. i Rl represents the soil moisture of the i-th monitoring sub-region. i Ra represents the air temperature gradient of the i-th monitoring sub-region. i Rw represents the variance of illumination intensity in the i-th monitoring sub-region. i denoted by , where represents the frequency of wind direction changes in the i-th monitoring sub-region, n represents the number of monitoring regions, and μ1 represents other influencing factors on the tea tree growth environment assessment value.

[0013] Preferably, the tea tree physiological state data analysis unit is used to establish a tea tree physiological state data analysis model, import the tea tree physiological state data transmitted by the monitoring data acquisition module into the tea tree physiological state data analysis model, and calculate the tea tree physiological state assessment value for each monitoring sub-region, specifically expressed as follows: TL i Lc represents the physiological status assessment value of tea trees in the i-th monitoring sub-region. i Lm represents the leaf chlorophyll content value of the i-th monitoring sub-region. i Lt represents the proportion of leaf moisture content in the i-th monitoring sub-region. i Lp represents the leaf temperature fluctuation amplitude in the i-th monitoring sub-region. i denoted as the photosynthetic efficiency index of the i-th monitoring sub-region, n represents the number of monitoring regions, and μ2 represents other influencing factors on the physiological status assessment value of tea trees.

[0014] Preferably, the tea garden management data analysis unit is used to establish a tea garden management data analysis model, import the tea garden management data transmitted by the monitoring data acquisition module into the tea garden management data analysis model, and calculate the tea garden management evaluation value for each monitoring sub-region, specifically expressed as follows: TQ i Let Qf represent the tea garden management assessment value for the i-th monitoring sub-region. i Ql represents the difference in fertilization density in the i-th monitoring sub-region. i Qm represents the deviation of irrigation water volume in the i-th monitoring sub-region. i Qp represents the average weeding frequency in the i-th monitoring sub-region. i denoted as the input for pest and disease control in the i-th monitoring sub-region, n represents the number of monitoring regions, and μ3 represents other influencing factors on the tea garden management evaluation value.

[0015] Preferably, the tea yield prediction data analysis unit is used to establish a tea yield prediction data analysis model, import the tea yield prediction data transmitted by the monitoring data acquisition module into the tea yield prediction data analysis model, and calculate the tea yield prediction evaluation value for each monitoring sub-region, specifically expressed as follows: TB i Bd represents the predicted tea yield assessment value for the i-th monitoring sub-region. i Bl represents the tea bud density value in the i-th monitoring sub-region. i Bp represents the new leaf growth rate of the i-th monitoring sub-region. i Bc represents the proportion of harvestable leaves in the i-th monitoring sub-region. i denoted by , where represents the shortening rate of the tea ripening cycle in the i-th monitoring sub-region, n represents the number of monitoring regions, and μ4 represents other influencing factors on the predicted tea yield.

[0016] Preferably, the comprehensive analysis model is specifically represented as: η i =TR i ×TL i ×TQ i ×TB i×λ,η i TR represents the comprehensive anomaly index of the i-th monitoring sub-region. i TL represents the tea tree growth environment assessment value of the i-th monitoring sub-region. i TQ represents the physiological status assessment value of tea trees in the i-th monitoring sub-region. i TB represents the tea garden management assessment value for the i-th monitoring sub-region. i λ represents the predicted tea yield assessment value for the i-th monitoring sub-region, and λ represents other influencing factors of the comprehensive anomaly index.

[0017] Preferably, the preset value of the comprehensive anomaly index is denoted as η. Def When η Def >η i When η indicates that the preset value of the comprehensive anomaly index is greater than the comprehensive anomaly index of the i-th monitoring sub-region, it means that the tea production quality of the i-th monitoring sub-region is good. Then, the system returns to the monitoring data acquisition module to maintain monitoring of the target tea garden data. Def <η i When the preset value of the comprehensive anomaly index is less than that of the comprehensive anomaly index of the i-th monitoring sub-region, it indicates that the tea production quality of the i-th monitoring sub-region is abnormal, and an early warning signal is sent to the management personnel terminal.

[0018] The technical effects and advantages of this invention are as follows:

[0019] This invention divides the target tea garden into multiple equally sized monitoring sub-regions using a monitoring area division module, marking them sequentially. This makes monitoring more targeted and operable, helps refine the monitoring granularity, improves the accuracy of monitoring data, and facilitates independent assessment and management of different areas. This allows for a more accurate understanding of the overall production status of the tea garden and the differences between regions. The monitoring data acquisition module collects data on various aspects, including the tea tree growth environment, physiological state, tea garden management, and tea yield prediction. This comprehensive and detailed data collection provides a solid foundation for subsequent data analysis, helping to more accurately assess tea production and identify potential problems in a timely manner. The monitoring data analysis module performs in-depth processing and analysis of the collected data, extracting key production indicators and evaluation values. This not only reflects the current production status of the tea garden but also reveals potential problems and trends, providing tea garden managers with scientific decision-making support. The accuracy and depth of data analysis directly impact the effectiveness and efficiency of tea garden management. By comprehensively processing evaluation values ​​from different dimensions to generate a comprehensive anomaly index, a comprehensive assessment of tea garden production status is achieved. This allows managers to examine the tea garden's production status from a more macro and comprehensive perspective. The existence of the comprehensive analysis module enhances the integrity and coordination of the monitoring system, improving the practicality and guidance of the monitoring results. By setting preset values ​​for the comprehensive anomaly index through the early warning module, real-time monitoring and early warning of tea garden production status are realized. When the comprehensive anomaly index of a certain sub-area exceeds the preset value, the early warning module can quickly issue an early warning signal to the management personnel's terminal. This helps tea garden managers to promptly identify and address potential production problems, reduce losses, and improve the overall production efficiency of the tea garden. The introduction of the early warning module enhances the initiative and response speed of the monitoring system, improving the efficiency and level of tea garden management. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] refer to Figure 1 The tea production cycle monitoring technology based on remote sensing imagery shown includes a monitoring area division module, a monitoring data acquisition module, a monitoring data analysis module, a comprehensive analysis module, and an early warning module.

[0023] The monitoring area division module is used to identify the target tea garden as the target monitoring area, and divide the target monitoring area into monitoring sub-areas by dividing it into equal areas, and label them as 1, 2...n in sequence.

[0024] The monitoring data acquisition module is used to collect tea tree growth environment data, tea tree physiological status data, tea garden management data, and tea yield prediction data for each monitoring sub-region, and transmit the collected data to the monitoring data analysis module.

[0025] The tea tree growth environment data includes soil moisture, air temperature gradient, light intensity variance, and wind direction change frequency, labeled as Rs, Rl, Ra, and Rw, respectively. The tea tree physiological state data includes leaf chlorophyll content, leaf water content ratio, leaf temperature fluctuation range, and photosynthetic efficiency index, labeled as Lc, Lm, Lt, and Lp, respectively. The tea garden management data includes fertilizer density variation, irrigation water deviation, average weeding frequency, and pest and disease control input, labeled as Qf, Ql, Qm, and Qp, respectively. The tea yield prediction data includes tea tree bud density, new leaf growth rate, proportion of harvestable leaves, and tea maturity cycle shortening rate, labeled as Bd, B1, Bp, and Bc, respectively.

[0026] The monitoring data analysis module includes a tea tree growth environment data analysis unit, a tea tree physiological state data analysis unit, a tea garden management data analysis unit, and a tea yield prediction data analysis unit. It is used to analyze the data transmitted by the monitoring data acquisition module and transmit the analyzed data to the comprehensive analysis module.

[0027] The tea tree growth environment data analysis unit is used to establish a tea tree growth environment data analysis model. It imports the tea tree growth environment data transmitted from the monitoring data acquisition module into the model and calculates the tea tree growth environment assessment value for each monitored sub-region, specifically as follows: TR i Rs represents the tea tree growth environment assessment value of the i-th monitoring sub-region. i Rl represents the soil moisture of the i-th monitoring sub-region. i Ra represents the air temperature gradient of the i-th monitoring sub-region. i Rw represents the variance of illumination intensity in the i-th monitoring sub-region. i denoted by , where represents the frequency of wind direction changes in the i-th monitoring sub-region, n represents the number of monitoring regions, and μ1 represents other influencing factors on the tea tree growth environment assessment value.

[0028] The tea tree physiological state data analysis unit is used to establish a tea tree physiological state data analysis model. It imports the tea tree physiological state data transmitted from the monitoring data acquisition module into the model and calculates the tea tree physiological state assessment value for each monitoring sub-region, specifically as follows: TL i Lc represents the physiological status assessment value of tea trees in the i-th monitoring sub-region. i Lm represents the leaf chlorophyll content value of the i-th monitoring sub-region. i Lt represents the proportion of leaf moisture content in the i-th monitoring sub-region. i Lp represents the leaf temperature fluctuation amplitude in the i-th monitoring sub-region. i denoted as the photosynthetic efficiency index of the i-th monitoring sub-region, n represents the number of monitoring regions, and μ represents other influencing factors on the physiological status assessment value of tea trees.

[0029] The tea garden management data analysis unit is used to establish a tea garden management data analysis model. It imports the tea garden management data transmitted from the monitoring data acquisition module into the model and calculates the tea garden management evaluation value for each monitoring sub-region, specifically as follows: TQ i Let Qf represent the tea garden management assessment value for the i-th monitoring sub-region. i Ql represents the difference in fertilization density in the i-th monitoring sub-region. i Qm represents the deviation of irrigation water volume in the i-th monitoring sub-region. i Qp represents the average weeding frequency in the i-th monitoring sub-region. i denoted as the input for pest and disease control in the i-th monitoring sub-region, n represents the number of monitoring regions, and μ3 represents other influencing factors on the tea garden management evaluation value.

[0030] The tea production forecasting data analysis unit is used to establish a tea production forecasting data analysis model. It imports the tea production forecasting data transmitted from the monitoring data acquisition module into the model and calculates the tea production forecasting evaluation value for each monitoring sub-region, specifically as follows: TB i Bd represents the predicted tea yield assessment value for the i-th monitoring sub-region. i Bl represents the tea bud density value in the i-th monitoring sub-region. i Bp represents the new leaf growth rate of the i-th monitoring sub-region. i Bc represents the proportion of harvestable leaves in the i-th monitoring sub-region. i denoted by , where represents the shortening rate of the tea ripening cycle in the i-th monitoring sub-region, n represents the number of monitoring regions, and μ4 represents other influencing factors on the predicted tea yield.

[0031] The comprehensive analysis module is used to establish a comprehensive analysis model, import the data transmitted by the monitoring data analysis module into the comprehensive analysis model, calculate the comprehensive anomaly index of each monitoring sub-region, and transmit it to the early warning module.

[0032] The comprehensive analysis model is specifically represented as: η i =TR i ×TL i ×TQ i ×TB i ×λ,η i TR represents the comprehensive anomaly index of the i-th monitoring sub-region. i TL represents the tea tree growth environment assessment value of the i-th monitoring sub-region. i TQ represents the physiological status assessment value of tea trees in the i-th monitoring sub-region. i TB represents the tea garden management assessment value for the i-th monitoring sub-region. i λ represents the predicted tea yield assessment value for the i-th monitoring sub-region, and λ represents other influencing factors of the comprehensive anomaly index.

[0033] The early warning module is used to establish a preset value for the comprehensive anomaly index, to judge the comprehensive anomaly index of each monitoring sub-region, and to issue an early warning signal based on the judgment result.

[0034] The preset value of the comprehensive anomaly index is denoted as η. Def When η Def >η i When η indicates that the preset value of the comprehensive anomaly index is greater than the comprehensive anomaly index of the i-th monitoring sub-region, it means that the tea production quality of the i-th monitoring sub-region is good. Then, the system returns to the monitoring data acquisition module to maintain monitoring of the target tea garden data. Def <η i When the preset value of the comprehensive anomaly index is less than that of the comprehensive anomaly index of the i-th monitoring sub-region, it indicates that the tea production quality of the i-th monitoring sub-region is abnormal, and an early warning signal is sent to the management personnel terminal.

[0035] This invention divides the target tea garden into multiple equally sized monitoring sub-regions using a monitoring area division module, marking them sequentially. This makes monitoring more targeted and operable, helps refine the monitoring granularity, improves the accuracy of monitoring data, and facilitates independent assessment and management of different areas. This allows for a more accurate understanding of the overall production status of the tea garden and the differences between regions. The monitoring data acquisition module collects data on various aspects, including the tea tree growth environment, physiological state, tea garden management, and tea yield prediction. This comprehensive and detailed data collection provides a solid foundation for subsequent data analysis, helping to more accurately assess tea production and identify potential problems in a timely manner. The monitoring data analysis module performs in-depth processing and analysis of the collected data, extracting key production indicators and evaluation values. This not only reflects the current production status of the tea garden but also reveals potential problems and trends, providing tea garden managers with scientific decision-making support. The accuracy and depth of data analysis directly impact the effectiveness and efficiency of tea garden management. By comprehensively processing evaluation values ​​from different dimensions to generate a comprehensive anomaly index, a comprehensive assessment of tea garden production status is achieved. This allows managers to examine the tea garden's production status from a more macro and comprehensive perspective. The existence of the comprehensive analysis module enhances the integrity and coordination of the monitoring system, improving the practicality and guidance of the monitoring results. By setting preset values ​​for the comprehensive anomaly index through the early warning module, real-time monitoring and early warning of tea garden production status are realized. When the comprehensive anomaly index of a certain sub-area exceeds the preset value, the early warning module can quickly issue an early warning signal to the management personnel's terminal. This helps tea garden managers to promptly identify and address potential production problems, reduce losses, and improve the overall production efficiency of the tea garden. The introduction of the early warning module enhances the initiative and response speed of the monitoring system, improving the efficiency and level of tea garden management.

[0036] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0037] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. Tea production cycle monitoring technology based on remote sensing images, characterized in that: include: Monitoring area division module: used to determine the target tea garden as the target monitoring area, divide the target monitoring area into various monitoring sub-areas by equal area division, and mark them as 1, 2...n in sequence; Monitoring data collection module: used to collect tea tree growth environment data, tea tree physiological status data, tea garden management data and tea yield forecast data in each monitoring sub-area, and transmit the collected data to the monitoring data analysis module; Monitoring data analysis module: including tea tree growth environment data analysis unit, tea tree physiological state data analysis unit, tea garden management data analysis unit and tea yield prediction data analysis unit, used to analyze the data transmitted by the monitoring data acquisition module and transmit the analyzed data to the comprehensive analysis module; Comprehensive analysis module: used to establish a comprehensive analysis model, import the data transmitted by the monitoring data analysis module into the comprehensive analysis model, calculate the comprehensive abnormal index of each monitoring sub-area, and transmit it to the early warning module; Early warning module: used to establish preset values ​​of comprehensive abnormality index, to judge the comprehensive abnormality index of each monitoring sub-area, and to issue early warning signals according to the judgment results.

2. The tea production cycle monitoring technology based on remote sensing images according to claim 1 is characterized in that: The tea tree growth environment data include soil moisture, air temperature gradient, light intensity variance and wind direction change frequency, which are marked as Rs, Rl, Ra and Rw respectively. The tea tree physiological state data include leaf chlorophyll content value, leaf water content ratio, leaf temperature fluctuation amplitude and photosynthesis efficiency index, which are marked as Lc, Lm, Lt and Lp respectively. The tea garden management data include fertilization density difference value, irrigation water deviation, weeding frequency mean and pest and disease control input, which are marked as Qf, Ql, Qm and Qp respectively. The tea yield prediction data include tea tree bud density value, new leaf growth rate, pickable leaf ratio and tea maturity cycle shortening rate, which are marked as Bd, Bl, Bp and Bc respectively.

3. The tea production cycle monitoring technology based on remote sensing images according to claim 1 is characterized in that: The tea tree growth environment data analysis unit is used to establish a tea tree growth environment data analysis model, import the tea tree growth environment data transmitted by the monitoring data acquisition module into the tea tree growth environment data analysis model, and calculate the tea tree growth environment evaluation value of each monitoring sub-area, which is specifically expressed as: TR i represents the tea tree growth environment assessment value of the ith monitoring sub-area, Rs i represents the soil moisture in the ith monitoring sub-area, Rl i represents the air temperature gradient of the ith monitoring sub-area, Ra i Rw represents the variance of the light intensity in the ith monitoring sub-area. i represents the frequency of wind direction change in the ith monitoring sub-area, n represents the number of monitoring areas, and μ1 represents other influencing factors of the tea tree growth environment assessment value.

4. The tea production cycle monitoring technology based on remote sensing images according to claim 1 is characterized in that: The tea tree physiological state data analysis unit is used to establish a tea tree physiological state data analysis model, import the tea tree physiological state data transmitted by the monitoring data acquisition module into the tea tree physiological state data analysis model, and calculate the tea tree physiological state evaluation value of each monitoring sub-area, which is specifically expressed as: TL i represents the physiological status evaluation value of tea trees in the ith monitoring sub-area, Lc i Indicates the leaf chlorophyll content value of the i-th monitoring sub-area, Lm i represents the leaf moisture content ratio of the i-th monitoring sub-area, Lt i represents the leaf temperature fluctuation amplitude of the ith monitoring sub-area, Lp i represents the photosynthesis efficiency index of the ith monitoring sub-area, n represents the number of monitoring areas, and μ2 represents other influencing factors of the tea tree physiological status evaluation value.

5. The tea production cycle monitoring technology based on remote sensing images according to claim 1 is characterized by: The tea garden management data analysis unit is used to establish a tea garden management data analysis model, import the tea garden management data transmitted by the monitoring data acquisition module into the tea garden management data analysis model, and calculate the tea garden management evaluation value of each monitoring sub-area, which is specifically expressed as: TQ i represents the tea garden management assessment value of the ith monitoring sub-area, Qf i represents the fertilizer density difference value of the ith monitoring sub-area, Ql i represents the irrigation water deviation of the ith monitoring sub-area, Qm i represents the mean weeding frequency of the ith monitoring sub-area, Qp i represents the input of pest control in the ith monitoring sub-area, n represents the number of monitoring areas, and μ3 represents other influencing factors of the tea garden management evaluation value.

6. The tea production cycle monitoring technology based on remote sensing images according to claim 1 is characterized by: The tea yield prediction data analysis unit is used to establish a tea yield prediction data analysis model, import the tea yield prediction data transmitted by the monitoring data acquisition module into the tea yield prediction data analysis model, and calculate the tea yield prediction evaluation value of each monitoring sub-area, which is specifically expressed as: TB i represents the tea yield prediction evaluation value of the ith monitoring sub-area, Bd i represents the tea tree bud density value of the i-th monitoring sub-area, Bl i represents the growth rate of new leaves in the ith monitoring sub-area, Bp i represents the proportion of harvestable leaves in the ith monitoring sub-area, Bc i represents the shortening rate of tea maturity period in the ith monitoring sub-area, n represents the number of monitoring areas, and μ4 represents other influencing factors of the tea yield prediction evaluation value.

7. The tea production cycle monitoring technology based on remote sensing images according to claim 1 is characterized by: The comprehensive analysis model is specifically expressed as: i =TR i ×TL i ×TQ i ×TB i ×λ,η i represents the comprehensive anomaly index of the ith monitoring sub-area, TR i represents the tea tree growth environment assessment value of the i-th monitoring sub-area, TL i represents the physiological status evaluation value of tea trees in the ith monitoring sub-area, TQ i represents the tea garden management assessment value of the i-th monitoring sub-area, TB i represents the predicted evaluation value of tea yield in the ith monitoring sub-area, and λ represents other influencing factors of the comprehensive abnormality index.

8. The tea production cycle monitoring technology based on remote sensing images according to claim 1 is characterized by: The preset value of the comprehensive abnormality index is marked as η Def , when η Def >η i When η is greater than the preset value of the comprehensive abnormal index of the ith monitoring sub-area, it indicates that the tea production quality of the ith monitoring sub-area is good, and the monitoring data acquisition module is returned to keep monitoring the target tea garden data. Def <η i When , it means that the preset value of the comprehensive abnormality index is less than the comprehensive abnormality index of the ith monitoring sub-area, indicating that the tea production quality in the ith monitoring sub-area is abnormal, and an early warning signal is sent to the management terminal.

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