A Precision Cultivation Method for Tea Tree Canopies Based on Environment-Image Data Fusion

Abstract

This invention discloses a method for precise cultivation of tea tree canopies based on environmental-image data fusion, belonging to the field of crop precision management technology. The method includes the following steps: multi-source data acquisition: acquiring topographic data, environmental data, and image data of the tea garden; data fusion calculation: calculating the topographic turbulence enhancement coefficient based on the acquired topographic and environmental data; constructing a canopy transpiration mechanism: setting trigger conditions through the topographic turbulence enhancement coefficient and environmental data, and dynamically correcting canopy transpiration when the trigger conditions are met; determining the control strategy: combining the topographic turbulence enhancement coefficient, the corrected canopy transpiration, and image data to generate a control strategy. This invention establishes a control feedback chain through the fusion processing of multi-dimensional information such as environmental monitoring, infrared images, and visible light images, realizing an automated cycle of perception-judgment-execution-feedback, thus improving the intelligence and real-time performance of canopy structure control.

Description

Technical Field

[0001] This invention relates to the technical field of precision crop management, and in particular to a method for precise cultivation of tea tree canopies based on environmental-image data fusion. Background Technology

[0002] In recent years, with the development of smart agriculture, the application of infrared images, visible light images and environmental meteorological data in tea tree planting and management has gradually increased. Some studies have attempted to use thermal imaging technology to monitor changes in tea tree canopy temperature in order to indirectly assess transpiration intensity and photosynthetic activity. However, there is a lack of a deep fusion mechanism between image data and environmental data, and a stable and effective pruning regulation decision model has not yet been formed.

[0003] Furthermore, traditional methods struggle to quantify and provide feedback on microclimate differences (such as turbulent ventilation capacity) caused by topographical variations in planting areas. Even after pruning, canopy temperatures remain high in some areas, revealing the weakness of existing turbulence enhancement models in adapting to complex terrain and their lagging parameter adjustments. Regarding irrigation strategies, most schemes rely solely on soil moisture or irrigation schedules, lacking targeted irrigation control based on crop thermal response feedback mechanisms. This results in low water use efficiency and a tendency for localized overwatering or resource waste.

[0004] Therefore, there is an urgent need for a precise cultivation method for tea tree canopies that integrates environmental meteorological factors and multi-source image sensing data, and has feedback regulation capabilities and regional difference response capabilities, so as to improve the scientific nature, responsiveness and energy efficiency of canopy structure regulation. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] In view of the problems existing in the prior art, the present invention is proposed.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for precise cultivation of tea tree canopies based on environment-image data fusion, the method comprising the following steps: S1: Multi-source data acquisition: Acquire topographic data, environmental data, and image data of the tea garden respectively; S2: Data fusion calculation: The terrain turbulence enhancement coefficient is calculated based on the acquired terrain data and environmental data; S3: Constructing a canopy evapotranspiration mechanism: Setting trigger conditions based on topographic turbulence enhancement coefficient and environmental data, and dynamically correcting canopy evapotranspiration when the trigger conditions are met; S4: Determine the control strategy: Combine the topographic turbulence enhancement coefficient, the corrected canopy transpiration, and image data to generate the control strategy.

[0008] As a preferred embodiment of the tea tree canopy precision cultivation method based on environment-image data fusion described in this invention, the terrain data is the three-dimensional slope matrix data of the tea garden obtained through terrain scanning equipment, and the environmental data is the wind speed and wind direction data collected through environmental sensors. The image data refers to leaf area index, canopy temperature, and light intensity distribution data extracted from RGB and thermal infrared images of the tea tree canopy obtained through a multispectral camera.

[0009] As a preferred embodiment of the tea tree canopy precision cultivation method based on environment-image data fusion described in this invention, the calculation process of the terrain turbulence enhancement coefficient is as follows: S21: Generate a three-dimensional slope matrix based on the scanning data from the terrain scanning equipment, and extract the slope gradient. and slope normal direction angle ; S22: Update the wind speed (v) and wind direction collected by the environmental sensors every 15-30 minutes. data; S23: Slope gradient The angle between the slope normal direction angle and the wind direction Input to terrain turbulence enhancement coefficient Calculation formula, dynamic calculation The formula is:

[0010] In the above formula, through Describe the potential ability of terrain to disturb airflow, angle Describe the consistency between the slope orientation and the wind direction.

[0011] As a preferred embodiment of the tea tree canopy precision cultivation method based on environment-image data fusion described in this invention, the triggering condition for correcting canopy transpiration is: The terrain turbulence enhancement coefficient μ is greater than the set threshold. And the wind speed v is greater than the set wind speed threshold. And continue for a certain period of time; When this condition is met, the canopy transpiration correction model is automatically activated to dynamically correct canopy transpiration. The expression for the canopy transpiration correction model is: ; in, This represents the sensitivity factor for turbulent evaporation. This represents the baseline evapotranspiration measured under standard conditions.

[0012] As a preferred embodiment of the tea tree canopy precision cultivation method based on environment-image data fusion described in this invention, the regulation strategy includes a tiered drip irrigation strategy, specifically: if the topographic turbulence enhancement coefficient μ satisfies: The slope area is then defined as the light turbulence enhancement zone; The slope area is then defined as the medium turbulence enhancement zone; The slope area is then defined as a high-turbulence enhancement zone; Based on different hierarchical divisions, the final drip irrigation water supply parameters D are generated: D=

[0013] in, > > : These are the water replenishment adjustment coefficients for different regions. To meet the actual evapotranspiration compensation needs of the region.

[0014] As a preferred embodiment of the tea tree canopy precision cultivation method based on environment-image data fusion described in this invention, the control strategy further includes a pruning strategy, generated in the following manner: First, an environmental temperature influence term for plant heat stress is constructed based on canopy temperature. Second, the heat accumulation influence term for plant is determined based on light intensity. Third, the plant's ability to regulate light and heat stress is determined through the plant's leaf area index. Finally, a comprehensive heat stress index (HSI) is quantified by weighted summation to determine whether heat and light combined stress exists. Based on the comprehensive heat stress index HSI and transpiration intensity The interaction results are used to dynamically generate pruning strategies.

[0015] As a preferred embodiment of the tea tree canopy precision cultivation method based on environment-image data fusion described in this invention, the dynamically generated pruning strategy specifically includes: If the comprehensive thermal stress index HSI And regional evaporation intensity If the pruning intensity is reduced by 30-40%, the pruning should prioritize retaining branches and leaves on the shaded side.

[0016] like Comprehensive Thermal Stress Index (HSI) And regional evaporation intensity Then, the second pruning strategy is implemented, which is to reduce the pruning intensity by 15-25% and lightly prune the branches and leaves on the side of the sun.

[0017] If the comprehensive thermal stress index HSI , Less than Then prune normally, and trim the leaves as needed.

[0018] As a preferred embodiment of the tea tree canopy precision cultivation method based on environment-image data fusion described in this invention, wherein: the canopy temperature change after pruning is monitored by thermal infrared image monitoring; if the canopy temperature... If the temperature does not drop below 28°C, the slope gradient weight in the formula for the terrain turbulence enhancement coefficient should be recalibrated.

[0019] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for precise cultivation of tea tree canopies based on environment-image data fusion.

[0020] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for precise cultivation of tea tree canopies based on environment-image data fusion.

[0021] The beneficial effects of this invention are: 1. This invention breaks through the reliance of traditional tea tree management on a single perception dimension and static threshold rules, and proposes a precise control scheme for canopy structure that is adaptive and adjustable for complex terrain. It significantly improves crop growth quality and resource utilization efficiency, and has broad promotion value and industrial application prospects.

[0022] 2. This invention establishes a control feedback chain by fusing multi-dimensional information such as environmental monitoring, infrared images, and visible light images, thereby realizing an automated cycle of perception, judgment, execution, and feedback, and improving the intelligence and real-time performance of canopy structure control. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of the overall process of the tea tree canopy precision cultivation method based on environment-image data fusion proposed in this invention; Figure 2 This is a schematic diagram of the layered drip irrigation strategy process for the tea tree canopy precision cultivation method based on environment-image data fusion proposed in this invention; Figure 3This is a schematic diagram of the pruning strategy process for the tea tree canopy precision cultivation method based on environment-image data fusion proposed in this invention. Detailed Implementation

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0025] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0026] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0027] Example 1, referring to Figure 1-2 As an embodiment of the present invention, a method for precise cultivation of tea tree canopies based on environment-image data fusion is provided, the method comprising the following steps: S1: Multi-source data acquisition: Acquire topographic data, environmental data and image data of the tea garden respectively. Topographic data is obtained by acquiring three-dimensional slope matrix data of the tea garden through topographic scanning equipment. Environmental data is collected by acquiring wind speed and wind direction data through environmental sensors. Image data is extracted from RGB images and thermal infrared images of the tea tree canopy through multispectral cameras, including leaf area index, canopy temperature and light intensity distribution data.

[0028] S2: Data Fusion Calculation: Based on the acquired terrain and environmental data, the terrain turbulence enhancement coefficient is calculated. The calculation process is as follows: S21: A three-dimensional slope matrix is ​​generated based on the scanning data from the terrain scanning device, and the slope gradient is extracted. and slope normal direction angle In layman's terms, by using existing 3D mapping methods (terrain scanning equipment such as LiDAR), combined with techniques such as grid elevation interpolation, slope gradient calculation, and orientation angle extraction, it is possible to obtain the slope characteristic parameters of each location in the tea garden terrain, including the slope gradient and the orientation angle of the slope normal. This process is a conventional technique for those skilled in the art.

[0029] S22: Update the wind speed (v) and wind direction collected by the environmental sensors every 15-30 minutes. data; S23: Slope gradient The angle between the slope normal direction angle and the wind direction Input to terrain turbulence enhancement coefficient Calculation formula, dynamic calculation The formula is:

[0030] In the above formula, through Describe the potential ability of terrain to disturb airflow, angle Describe the consistency between the slope orientation and the wind direction.

[0031] Specifically: Under natural terrain conditions, the steepness of the slope significantly affects the ground adhesion and disturbance intensity of airflow. The formula introduces... The slope gradient, or slope change rate per unit location, is commonly used to measure the inclination of a slope. A large value indicates a steep slope, increasing the likelihood of it forming "obstacles" or "ventilation corridors" for airflow, thus making it easier to induce enhanced local turbulence; therefore, the slope gradient is included as an enhancing factor in the calculation of the turbulence coefficient. The included angle... This represents the angle between the wind direction and the slope orientation, using a sine function with a range of [-1, 1]. When the slope orientation is the same as the wind direction (i.e., ...), the angle between the wind direction and the slope orientation is considered to be... (≈0° or 180°) A value close to 0 indicates that the wind flows parallel to or against the slope, resulting in a weak turbulence enhancement effect; when the slope is perpendicular to the wind direction ( When ≈±90°, When the value reaches the extreme value of ±1, the windward side is most likely to form a wind disturbance zone, and the turbulence is significantly enhanced.

[0032] However, due to the large dimensions and scale of slope gradient, direct use will lead to... The values ​​fluctuate wildly, affecting the stability of the model. Therefore, a constant factor of "10" is introduced as a normalization factor to ensure that the values ​​remain stable under typical slope gradients. The value is within the common engineering range of 0–50, and the μ value is between [1,6], which facilitates the triggering judgment of the subsequent transpiration correction model.

[0033] S3: Constructing a canopy transpiration mechanism: Setting trigger conditions based on topographic turbulence enhancement coefficient and environmental data, and dynamically correcting canopy transpiration when the trigger conditions are met.

[0034] Specifically, the triggering condition for adjusting canopy transpiration is: The terrain turbulence enhancement coefficient μ is greater than the set threshold. And the wind speed v is greater than the set wind speed threshold. And last for a certain period of time (e.g., more than 2 hours); Once this condition is met, the canopy transpiration correction model is automatically activated to dynamically correct canopy transpiration. The expression for the canopy transpiration correction model is: ; in, This represents the sensitivity factor for turbulent evaporation. The above model formula represents the baseline transpiration measured under standard conditions (usually estimated from field observations or the Penman-Monteith model). As the core driving factor, combined with the flow evaporation sensitivity factor This forms a direct mapping of "enhanced evaporation due to the coupling of topography and wind force"; Furthermore, through To represent the "relative turbulence enhancement magnitude", where = 1 indicates no enhancement, while u > 1 indicates enhanced turbulence.

[0035] S4: Determine the control strategy: Combine the topographic turbulence enhancement coefficient, the corrected canopy transpiration, and image data to generate the control strategy.

[0036] The regulation strategy includes a tiered drip irrigation strategy, specifically: if the topographic turbulence enhancement coefficient μ satisfies: The slope area is then defined as the light turbulence enhancement zone; The slope area is then defined as the medium turbulence enhancement zone; The slope area is then defined as a high-turbulence enhancement zone; Based on different hierarchical divisions, the final drip irrigation water supply parameters D are generated: D=

[0037] in, > > : Water replenishment adjustment coefficients for different regions, reflecting the principle of "the more turbulent the flow, the more water-scarce, and the more water available". To meet the actual evapotranspiration compensation needs of the region.

[0038] Through the above-mentioned layered drip irrigation strategy, the logical strategy of μ→drip irrigation map→controlling water replenishment at each point is realized. This is deeply coupled with the original canopy transpiration correction model, truly achieving "wind-water-land" linkage, thus forming high-precision irrigation.

[0039] Example 2, refer to Figure 3 The difference from Example 1 is that the regulation strategy also includes a pruning strategy, which is generated as follows: First, an environmental temperature influence term for plant heat stress is constructed based on canopy temperature. Second, an influence term for heat accumulation on plants is determined based on light intensity. Third, the plant's ability to regulate light and heat stress is determined through the plant's leaf area index. Combining the above three dimensions, a comprehensive heat stress index (HSI) is quantified by weighted summation to determine whether there is heat + light combined stress. Based on the comprehensive heat stress index HSI and transpiration intensity The interaction results are used to dynamically generate pruning strategies.

[0040] The logic behind pruning strategies can be explained in detail as follows: The fundamental cause of heat stress in plants is excessively high environmental temperatures, which lead to increased transpiration and even metabolic disorders. Therefore, pruning should be based on canopy temperature... As a core temperature indicator, higher temperatures imply greater stress. Secondly, light intensity (I(z)) is a crucial factor affecting heat accumulation, especially under strong light but limited canopy transpiration, which can easily lead to increased leaf temperature. Therefore, the potential stress risks caused by insufficient or inadequate light need to be considered. Thirdly, the leaf area index (LAI) represents the total leaf area per unit area of ​​soil surface; a higher LAI value indicates a denser canopy and a stronger ability of the plant to regulate light and heat stress. Therefore, LAI is an important factor in measuring canopy structure and stress resistance.

[0041] In summary, the HSI indicator can be constructed using the following weighted formula:

[0042] , , These represent the weighting factors for each item. Additionally, it should be explained that 28℃ represents the empirical upper limit of the optimal temperature for plant growth, and 6℃ represents the normalized range of the "dangerous temperature rise interval." μmol / m 2 / s represents the standard sufficient light value required for plant growth. This indicates the preset target leaf area index.

[0043] The dynamically generated pruning strategy is as follows: If the comprehensive thermal stress index HSI And regional evaporation intensity (Generally 5mm / day), then the first pruning strategy is implemented, which is to reduce the pruning intensity by 30-40% and prioritize the preservation of branches and leaves on the shaded side. This is because for the stressed area, leaves or branches that provide shade to the direction of direct sunlight (i.e. facing the opposite direction of the sun) are identified and retained without pruning to build a natural "leaf umbrella layer" and shield the core area of ​​the canopy from strong light.

[0044] like Comprehensive Thermal Stress Index (HSI) And regional evaporation intensity If necessary, the second pruning strategy should be implemented, which is to reduce the pruning intensity by 15-25% and lightly prune the branches and leaves on the side of the sunlight (meaning that the branches and leaves on the non-direct sunlight side can be moderately pruned to increase local ventilation and light penetration and reduce the accumulation of moisture and heat). If the comprehensive thermal stress index HSI , Less than Then prune normally, and trim the leaves as needed (including guiding overly vigorous branches and removing weak branches, which helps maintain ventilation and light penetration of the plant and a reasonable canopy structure).

[0045] For example, pruning intensity is defined as the percentage of leaf area removed per unit time relative to the total leaf area. It is usually measured by weekly pruning frequency combined with the percentage of a single pruning session. For example, if the current standard pruning is once a week, removing 10% of the total leaf area each time, then "reducing pruning intensity by 30%" means lowering the percentage of a single pruning session to 70% of the original, that is, from 10% to 7%.

[0046] Normal pruning targets are: Young trees: Three shaping prunings (height gradient: 15cm→30cm→45cm) Mature trees: Light pruning (cut off 5-8cm), deep pruning (cut off 15-20cm) Update pruning: Bench pruning (heavy pruning 15-20cm from the ground).

[0047] Monitoring canopy temperature changes after pruning using thermal infrared imaging; if the canopy temperature... If the temperature does not drop below 28°C, the slope gradient weight in the formula for the terrain turbulence enhancement coefficient should be recalibrated.

[0048] Specifically, to improve the model's adaptability under different terrain conditions, after implementing the pruning control strategy, the system monitors canopy temperature changes in real time using thermal infrared images. When the canopy temperature (T_canopy) is found to have not dropped below a set threshold (e.g., 28℃), the system determines that there are errors in the current model's modeling of terrain factors, particularly that the weighting of slope on local wind field disturbances is set too low or too high. Therefore, the system automatically enters calibration mode, adjusting the weighting term of the slope gradient in the turbulence enhancement coefficient calculation formula, that is, adjusting the term in the original formula... Replace with ,in This is a weighting factor with an initial value of 1.0 and an adjustment step size of 0.1. The weight is adjusted based on the degree of deviation between the canopy temperature and the predicted temperature. If the deviation is >8%, the weight is increased or decreased by 20% to achieve dynamic optimization.

[0049] This embodiment also provides a computer device applicable to the precise cultivation method of tea tree canopy based on environment-image data fusion, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the precise cultivation method of tea tree canopy based on environment-image data fusion as proposed in the above embodiment.

[0050] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0051] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the precise cultivation method for tea tree canopies based on environment-image data fusion as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0052] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for precise cultivation of tea tree canopies based on environment-image data fusion, characterized in that, The method includes the following steps: S1: Multi-source data acquisition: Acquire topographic data, environmental data, and image data of the tea garden respectively; S2: Data fusion calculation: The terrain turbulence enhancement coefficient is calculated based on the acquired terrain data and environmental data; S3: Constructing a canopy evapotranspiration mechanism: Setting trigger conditions based on topographic turbulence enhancement coefficient and environmental data, and dynamically correcting canopy evapotranspiration when the trigger conditions are met; S4: Determine the control strategy: Combine the topographic turbulence enhancement coefficient, the corrected canopy transpiration, and image data to generate a control strategy; The calculation process for the terrain turbulence enhancement coefficient is as follows: S21: Generate a three-dimensional slope matrix based on the scanning data from the terrain scanning equipment, and extract the slope gradient. and slope normal direction angle ; S22: Update the wind speed (v) and wind direction collected by the environmental sensors every 15-30 minutes. data; S23: Slope gradient The angle between the slope normal direction angle and the wind direction Input to terrain turbulence enhancement coefficient Calculation formula, dynamic calculation The formula is: ； In the above formula, through Describe the potential ability of terrain to disturb airflow, angle Describe the consistency between the slope orientation and the wind direction; The triggering condition for the modified canopy transpiration is: The terrain turbulence enhancement coefficient μ is greater than the set threshold. And the wind speed v is greater than the set wind speed threshold. And continue for a certain period of time; When this condition is met, the canopy transpiration correction model is automatically activated to dynamically correct canopy transpiration. The expression for the canopy transpiration correction model is: ； in, This represents the sensitivity factor for turbulent evaporation. This represents the baseline transpiration rate measured under standard conditions. The control strategy includes a tiered drip irrigation strategy, specifically: if the topographic turbulence enhancement coefficient μ satisfies: The slope area is then defined as the light turbulence enhancement zone; The slope area is then defined as the medium turbulence enhancement zone; The slope area is then defined as a high-turbulence enhancement zone; Based on different hierarchical divisions, the final drip irrigation water supply parameters D are generated: ； in, > > : These are the water replenishment adjustment coefficients for different regions. To meet the actual evapotranspiration compensation needs of the region; The regulation strategy also includes a pruning strategy, which is generated as follows: First, an environmental temperature factor affecting plant heat stress is constructed based on canopy temperature. Second, an impact factor on heat accumulation in plants is determined based on light intensity. Third, the plant's ability to regulate light and heat stress is determined using its leaf area index. Finally, a comprehensive heat stress index (HSI) is quantified using a weighted summation method to determine the presence of combined heat and light stress. The HSI calculation method is designed as follows: ； , , Let T represent the weighting factors for each item, I(z) represent the light intensity, and T represent the light intensity. C The temperature represented by LAI is the canopy temperature, and LAI is the leaf area index, which is the total area of ​​leaves per unit area of ​​the ground surface. This indicates the preset target leaf area index; Based on the comprehensive heat stress index HSI and transpiration intensity The interaction results are used to dynamically generate pruning strategies.

2. The method for precise cultivation of tea tree canopies based on environment-image data fusion according to claim 1, characterized in that: The terrain data is the three-dimensional slope matrix data of the tea garden obtained through terrain scanning equipment, and the environmental data is the wind speed and wind direction data collected through environmental sensors. The image data refers to leaf area index, canopy temperature, and light intensity distribution data extracted from RGB and thermal infrared images of the tea tree canopy obtained through a multispectral camera.

3. The method for precise cultivation of tea tree canopies based on environment-image data fusion according to claim 2, characterized in that: The dynamically generated pruning strategy is as follows: If the comprehensive thermal stress index HSI And regional evaporation intensity If so, the first pruning strategy is implemented, which is to reduce the pruning intensity by 30-40%, and prioritize pruning to retain branches and leaves on the shaded side; like Comprehensive Heat Stress Index (HSI) And regional evaporation intensity If necessary, the second pruning strategy will be implemented, which is to reduce the pruning intensity by 15-25% and lightly prune the branches and leaves on the side of the sun. If the comprehensive thermal stress index HSI , Less than Then prune normally, and trim the leaves as needed.

4. The method for precise cultivation of tea tree canopies based on environment-image data fusion according to claim 3, characterized in that: Monitoring canopy temperature changes after pruning using thermal infrared imaging; if the canopy temperature... If the temperature does not drop below the expected level, the slope gradient weights in the terrain turbulence enhancement coefficient formula should be recalibrated.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the precise cultivation method for tea tree canopies based on environment-image data fusion as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the precise cultivation method for tea tree canopies based on environment-image data fusion as described in any one of claims 1 to 4.

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