353 results about "Tea garden" patented technology

The invention discloses a smart tea garden resource dynamic scheduling system and method integrated with environment monitoring, and the method comprises the following steps: S1, collecting environment and operation data, processing the environment and operation data, and generating a standardized data set; s2, carrying out discrete modeling and environment field generation, carrying out topological analysis on humidity and pest and disease damage intensity, judging abnormity and triggering local scheduling; s3, generating a task, resource and environment set, constructing a task-resource-environment hypergraph, establishing a time expansion graph and forming constraints; s4, dynamic game solving is introduced, and a scheduling scheme and a control instruction are generated; s5, issuing an instruction to an execution end, collecting an execution state and environment data, and forming a feedback data set; and S6, constructing a double-layer memory scheduling unit, and generating a parameter set of a next period. According to the invention, precise allocation and efficient management and control of tea garden resources are realized by fusing environmental monitoring and dynamic scheduling, so that the production efficiency is improved and ecological sustainability is guaranteed.

The invention relates to the technical field of tea garden fertilization, in particular to a soil loosening, ditching, fertilizing and soil covering all-in-one machine for tea garden land, which comprises a vehicle body, a handrail part fixedly connected to the top of the rear end of the vehicle body in the moving direction, and a controller; the top of the vehicle body is fixedly connected with a fertilizer box; a fertilizing pipe is fixedly connected to the bottom of the vehicle body and communicated with the bottom of the fertilizer box, an electromagnetic valve is communicated in the fertilizing pipe, and the controller is used for controlling operation of the electromagnetic valve; a soil loosening assembly for loosening soil, an adjusting assembly for adjusting the soil loosening depth of the soil loosening assembly and a ditching assembly for ditching the soil after soil loosening are arranged on the fertilization pipe; a V-shaped bracket is fixedly connected to the side wall of the fertilizing pipe, and a soil covering assembly for performing soil covering operation on the furrowed soil is arranged on the bracket. The soil loosening device can adjust the soil loosening operation depth through the adjusting assembly.

The invention relates to the technical field of selenium-rich white tea production, and discloses an intelligent control system and method for automatic production of selenium-rich white tea. The system comprises a selenium-rich white tea growth environment regulation and control module, a tea growth stage recognition module, a selenium content self-adaptive supply module, a production equipment cooperative control module and a quality closed-loop feedback module. The growth environment regulation and control module collects tea garden multi-region environment parameters and establishes a dynamic monitoring set; the growth stage identification module divides growth stage identifiers in combination with the environment parameters, the bud and leaf morphological image sequence and the growth cycle time sequence data; the selenium content self-adaptive supply module generates a supply scheme and an equipment control instruction set according to the growth stage identifier and the soil selenium content data; the production equipment cooperative control module is linked with related equipment to execute a cooperative operation time sequence control process; and the quality closed-loop feedback module generates a correction amount according to the finished product detection data, and feeds the correction amount back to the growth environment regulation and control module for parameter iterative updating.

The invention relates to a tea-picking robot autonomous navigation method based on tea ridge composite boundary identification and driving area adjustment, and belongs to the technical field of robot navigation. The method comprises the steps that tea ridge growth state information is evaluated, and a global path planning strategy is made; a root-ground intersection ground navigation datum line and lateral boundary constraints are established, the ground navigation datum line, the lateral boundary constraints and tea tree growth density and distribution characteristics are integrated, a tea ridge three-dimensional drivable road network model is established, and key geometric characteristics of the model are analyzed to establish an adaptive risk assessment system. Dividing the drivable area into areas with different risk levels; and establishing a multi-objective optimization function in combination with the risk region distribution diagram, fusing the multi-objective optimization function through an exponential weighted fusion mechanism, and generating an optimal navigation path under the risk gradient constraint. Guiding of intelligent navigation of the tea garden is achieved, the tea leaf picking efficiency and quality are improved, and technical support is provided for mechanical and intelligent picking of the tea garden.

The invention discloses a tea bud screening method based on multi-modal information fusion, which is characterized in that on the basis of multi-modal data acquisition, feature information of different modal data is integrated and analyzed through a mode of feature level fusion and decision level fusion by using a mode recognition technology, and an accurate tea bud recognition model is constructed. Finally, in combination with an automatic control technology, screening execution mechanisms such as a mechanical arm are controlled, and automatic screening and picking of the tea buds are achieved. The technology is mainly applied to the tea production process of a modern tea garden, and aims to solve the problems that traditional manual screening is low in efficiency and high in cost, and single machine vision screening is insufficient in accuracy. And an efficient and reliable technical solution is provided for large-scale and automatic production of high-quality tea leaves.

The invention discloses a tea garden ditching and fertilizing all-in-one machine which adopts an electric crawler chassis which is independently driven on two sides, a fertilizing mechanism is located at the top of a whole machine frame, a ditching and earthing mechanism is located at the rear end of the whole machine frame, and the ditching depth can be adjusted by adjusting the length of a hydraulic telescopic rod; the control console is located at the front end of the whole machine frame and used for a driver to control the movement of the whole machine and the movement of all the mechanisms during driving, and meanwhile the ditching and fertilizing all-in-one machine for the tea garden can be controlled by selecting a wireless control mode under the condition that the road condition is simple. The ditching and fertilizing operation of the tea garden is more flexible; a wired and wireless combined remote control operation mode of ditching, fertilizing and earthing in the tea garden is realized; organic combination of the adjustable soil covering plate and the soil-splitting type fertilizing straight pipe is realized, and the soil crushing and covering link is more efficient; the ditching and fertilizing operation of the tea garden can be carried by a driver, and the complicated tea garden environment can be easily and timely adjusted in the operation process.

The present application relates to a kind of methods for mass propagation of tea corner miridae in laboratory, comprising the following technical steps: collecting tea garden tea corner miridae adult, cultivating fall kui seedling, putting seedling and leading insect, tea corner miridae habitat and feeding, mating and oviposition and nymph hatching and feeding.The present application uses fall kui seedling as the main body of feeding raw material, and the leaf of raw material is relatively thick, strong in harm resistance, not easy to dry, which helps to improve the hatching rate of tea corner miridae eggs, reduce the frequency of raw material supply and replacement, at the same time, the present application saves time and effort, can be realized in laboratory low cost and sustainability fast, efficient, mass propagation of tea corner miridae test insect source.

The invention relates to the technical field of intelligent tea garden management, and provides an intelligent tea garden service collaborative management system, which comprises a tourist picking reservation module used for receiving a reservation picking request of a tourist through a terminal and intelligently arranging autonomous picking time and region of the tourist; the picking behavior processing module is used for analyzing, processing and prompting picking behaviors of tourists; and the comprehensive management docking module is used for integrating all module data, generating a multi-dimensional report to be docked with the tea garden enterprise ERP system, generating a collaborative scheme of management personnel, planting personnel and customer service personnel, and completing collaborative management of data integration-role adaptation-decision landing. A full-chain tea garden management system from data acquisition and integration, growth and variety analysis to visual decision making is constructed, comprehensive improvement of yield increase, quality improvement, cost reduction and efficiency improvement of the tea garden is achieved by means of multi-source data fusion and intelligent technologies, economic benefits are enhanced, and the resource utilization efficiency and ecological sustainability are improved.

The invention provides a tea garden drought prediction method and system fusing LightGBM and LSVM in the technical field of smart agriculture. The method comprises the steps of S1, collecting a large amount of historical monitoring data to construct a data set; s2, the data set is analyzed through a Limma algorithm, a COX survival regression model, a GLM generalized linear model and an SHAP, and an environment factor correlation graph is obtained; s3, creating a tea garden drought prediction model; s4, dividing the data set into a training set, a verification set and a test set to train, verify and test the tea garden drought prediction model, and deploying the tea garden drought prediction model passing the test; and S5, collecting real-time monitoring data from the tea garden, inputting the data into the tea garden drought prediction model to obtain a tea garden drought prediction result, and displaying the tea garden drought prediction result on a visual interface. The tea garden drought prediction method has the advantages that the timeliness, accuracy and generalization ability of tea garden drought prediction are greatly improved.

The present invention discloses a method for automatically and highly accurately extracting the vector of tea row boundaries based on UAV images of tea gardens, comprising the following steps: obtaining UAV multispectral images, annotating the region of interest to obtain the tea garden plot ROI mask; performing binary classification processing based on threshold segmentation to obtain the initial classification image of tea trees - soil; extracting the boundary contour and detecting straight lines from the initial classification image of tea trees - soil, and calculating the main direction slope of the tea rows of tea trees; generating parallel sampling paths based on the main direction slope of the tea rows and classifying them; grouping the parallel sampling paths according to the same category to obtain a scan line group of continuous tea tree or soil categories; filling the soil regions therein to complete the highly accurate extraction of the tea row boundaries. The method of the present invention realizes the highly accurate vector extraction of tea row boundaries by optimizing the segmentation boundary and eliminating discrete misclassified patches, and overcomes the problem that the tea row boundaries are unclear in the existing extraction of tea rows in tea gardens when there are missing tea trees, withered branches, and discolored leaves.

The invention relates to the field of agricultural machinery, in particular to multifunctional tea garden weeding, soil loosening and fertilizing integrated mechanical equipment which comprises a mounting frame, a driving module is mounted at the bottom of the mounting frame, a weeding module, a soil loosening module and a fertilizing module are mounted at the top of the mounting frame, and the weeding module comprises a power part which is fixedly connected with the top of the mounting frame; an output shaft of the power part is connected with a roller, the outer side wall of the roller is fixedly connected with a plurality of wave-shaped blades, the soil loosening module is installed in the roller, and an opening is formed in the side wall of the roller and used for loosening tea garden soil based on the centrifugal force effect; the driving module is in signal connection with a controller, the controller is in signal connection with the power piece and the fertilization module, and the controller is used for adjusting the power of the driving module and the rotating speed and the output power of the power piece according to preset terrain information. The method is used for improving the efficiency of weeding, soil loosening and fertilizing operation of the tea garden and enhancing the collaboration of all operation links.

The application provides a tea garden identification method and device based on satellite images, which comprises the following steps: determining a evergreen vegetation area of a to-be-identified area based on Sentinel-2 image data and Landsat image data of the to-be-identified area; and identifying a tea garden area from the evergreen vegetation area through a decision tree model; wherein the decision tree model is constructed based on the following features and the classification threshold values corresponding to the features: a tea leaf phenology feature index, a terrain feature, and a spectral index determined by a separability index, wherein the SI is used to reflect the spectral reflectance separability of the tea garden and other evergreen vegetation; wherein the tea leaf phenology feature index is determined by an enhanced vegetation index in month N and a land surface water index in month M; the SI between the enhanced vegetation index in month N and the land surface water index in month M is the largest compared with the SI between the EVI in any other month and the LSWI in any other month, and N and M are integers greater than 0 and less than 13.

The invention belongs to the technical field of carbon sink evaluation, and discloses a typical weather-oriented tea garden carbon sink dynamic profit and loss evaluation method and system. Historical meteorological information and historical carbon sink monitoring information of the tea garden are collected, and a typical weather type library of the tea garden is constructed; determining a typical weather type corresponding to the current day of the tea garden, and calculating a carbon sink fixed amount and a carbon sink output amount of the tea garden; generating a correction factor for correction according to the management activity information of the tea garden to obtain a net carbon sink value; generating a total score based on comprehensive analysis of the net carbon sink value, and evaluating the carbon sink profit and loss condition of the tea garden; the carbon sink meteorological influence parameters before and after the monitoring time point are collected for comprehensive analysis to calculate the carbon sink fixed quantity and the carbon sink output quantity, calculation deviation caused by parameter fluctuation at a single time point can be avoided, and therefore the method better fits the continuous physiological process of photosynthesis / respiration of the tea trees, and the detection accuracy is improved. And the calculation result of the carbon sink fixed quantity / output quantity is closer to the actual condition.

The invention relates to the technical field of tea garden fog irrigation, in particular to a rotary fog irrigation machine and a use method. According to the technical scheme, the device comprises a conveying frame, a transposition driving assembly used for adjusting the irrigation position is installed at the top of the conveying frame, a water tank is installed at the top of the transposition driving assembly, a conversion assembly is installed at the top of the transposition driving assembly, and the water tank and the conversion assembly are connected through a high-pressure pump. The single-tooth rotating wheel pushes a third gear to rotate along a third auxiliary rotating rod every time the single-tooth rotating wheel rotates by a circle, so that a large difference value is generated between the rotating speed of fan blades and the rotating speed of the rotary mist irrigation assembly, in the process, the rotary mist irrigation assembly rotates to throw water mist to a farther distance through centrifugal force, and the water mist can be sprayed to the farther distance through centrifugal force; and the rotation difference value of the fan blades and the rotary fog irrigation assembly is also utilized, so that wind power can guide water mist to enter a standard area, the irrigation range is expanded, the control force on the irrigation area is improved, and the cost and time cost are also saved.

The invention relates to the technical field of plant ecological carbon sink measurement and calculation, in particular to a tea garden carbon reserve dynamic monitoring and prediction method based on meteorological data, a typical sample plot in a tea garden is selected, meteorological data and basic carbon reserve of the typical sample plot are acquired, and the meteorological data comprise real-time data and forecast data; establishing dynamic monitoring, and performing heat damage identification and freeze damage identification on real-time data; establishing a carbon reserve dynamic correction model; a carbon reserve prediction model is established, and when heat damage and / or freezing damage occur in prediction data, the carbon reserve dynamic correction model is used for prediction to obtain a prediction result; according to the method, the damage of high-temperature stress to the tea trees has a gradual superposition characteristic, high-temperature inhibits photosynthesis, so that calculation is performed in a summation form, cold waves increase respiratory consumption, and the damage mainly depends on the intensity peak value and the cooling rate of a single event; by providing more accurate dynamic measurement and calculation as the supplement of static measurement and calculation, the change condition of the carbon reserves of the tea garden can be reflected in time.

The invention provides a multi-mode remote sensing tea garden automatic identification method and system integrating phenolic indexes and phenological characteristics. According to the method, the growth amplitude GA reflecting tea phenological characteristics is calculated through medium-resolution time sequence radar data, and the normalized vegetation index NDVI, the phenolic compound index PCI and the phenol high-value dominant index PHD are calculated from optical remote sensing images so as to represent the growth condition of the tea tree and the change rule of the phenol content. A double-branch lightweight deep learning model is constructed, a first branch processes a high-resolution remote sensing image, and a second branch processes multi-modal tea garden remote sensing features; and then multi-modal geoscience knowledge of the tea garden is fully extracted and fused through a double-branch fusion module, refined and automatic extraction of large-area tea garden distribution is realized, and key technical support is provided for tea garden resource monitoring, management and the like.

The invention discloses an automatic monitor for environmental meteorology of an organic tea garden, and relates to the technical field of environmental meteorology monitoring of tea gardens. By arranging the adjustable device, namely the lifting assembly arranged at the bottom, the jacking effect of the cam on the roller can be achieved under the driving of the first motor, and the lifting frame connected with the lower end of the front side of the guide plate can be jacked and unfolded along with the upward movement of the roller, so that the flexible adjustment of the monitoring height of the automatic meteorological monitor is met; the range adjusting assembly connected with the upper end of the lifting assembly is matched, multi-directional real-time shooting of the tea garden environment can be met, the connecting arms connected with the four sides of the rotating disc are in butt joint under rotation of the rotating disc, sliding blocks connected with the outer ends can be pushed and pulled, the flexible adjustment of the shooting range of the camera is met, and the shooting effect of the tea garden environment is improved. Clear, large-range and dead-corner-free shooting and recording of the tea garden environment are achieved.

The invention discloses a weeding device suitable for tea garden cultivation in hills and mountains, and relates to the technical field of weeding devices. The machine body part comprises a rack, a controller arranged at the top end of the rack, a scanning piece arranged at the front end of the controller, a signal receiver fixed to the top of the controller, and moving wheels arranged on the side wall of the rack; the weeding component comprises a lifting assembly arranged on the front side of the interior of the rack and a weeding assembly arranged on the lifting assembly; the lifting assembly comprises a lifting plate arranged on the front side of the interior of the rack and an electric push rod fixed to the top of the lifting plate. The surrounding terrain is scanned through the scanning piece, data are transmitted to the corresponding controller, and then the lifting plate is controlled to move, so that the height of the cutting knife is changed, the purpose of adjusting the height of the cutting knife is achieved, weeds in low-lying places can be cleaned conveniently, and the working efficiency is improved. The problem that existing weeds growing in low-lying places cannot be cleaned by a weeding device is solved.

The invention relates to the technical field of tea tree pruning, in particular to an efficient tea tree pruning machine which comprises a supporting plate, a pushing handle is installed on one side of the supporting plate, a cutting mechanism is arranged on the supporting plate, a lifting mechanism is further arranged on the supporting plate, a collecting plate is arranged at the bottom of the supporting plate, and a storage box is arranged on one side of the collecting plate. A conveying mechanism is further arranged on the supporting plate, and wheels are arranged on the two sides of the supporting plate. Waste such as trimmed branches and leaves can be directly collected in time, the workload of manual cleaning is reduced, a tea garden is kept clean, interference of the trimmed waste on the growth environment of tea trees is avoided, the tea garden is kept clean, and environmental pollution is reduced; due to the adjustable function of the cutter, the pruning depth, angle and height can be accurately adjusted according to the growth conditions and pruning requirements of different tea trees, so that the pruning accuracy and the working efficiency are improved, and the situation of uneven pruning is reduced.

The invention discloses a tea garden soil moisture physical simulation and data driving fusion prediction method and device, and relates to the field of soil moisture prediction.The method comprises the steps that based on soil basic attributes and multi-depth soil moisture time sequence observation data, a soil moisture physical baseline sequence is obtained in combination with boundary conditions and initial conditions; respectively extracting data of a target soil layer in a preset time window from the soil water content physical baseline sequence, the multi-source driving data and the multi-depth soil water content time sequence observation data so as to splice the data into an input feature set; obtaining a water content prediction value of the target soil layer on the prediction day based on the input feature set and a preset soil water content prediction model; according to the method, the HYDRUS-1D simulation result is taken as a baseline, the time sequence dependence of soil moisture, especially the lag response of different soil layers, is effectively learned, and the modeling capability of a complex nonlinear relationship is improved.

The invention relates to the technical field of tea garden insect pests, and discloses a tea garden insect pest early warning system based on machine learning, which comprises a multi-mode sensing device, an edge intelligent terminal and a cloud decision platform. According to the tea garden insect pest early warning system based on machine learning, a narrow-band spectrum is dynamically selected through an adjustable spectrum imaging unit, image and sound wave features are synchronously acquired in combination with a vibration wave acquisition module, a heterogeneous calculation module of an edge intelligent terminal processes multi-source data in parallel, and a dynamic feature library management module updates a feature template in real time; the incremental learning mechanism triggers high-resolution imaging when unknown pests are detected, the meta-learning framework supports rapid migration training of a new model, the framework breaks through the limitation of a traditional single spectrum and a fixed model, and through multi-dimensional data fusion and adaptive learning, the pest recognition efficiency and adaptability are remarkably improved. The problems of low manual inspection efficiency and poor adaptability of traditional equipment are solved.

The invention discloses a digital twin sprinkling irrigation optimization system and method for tea and light complementation, and belongs to the technical field of sprinkling irrigation optimization. A digital twinborn mirror image platform in the system is used for constructing a digital twinborn model and a photovoltaic system digital twinborn model, and the digital twinborn model comprises a three-dimensional model of a tea garden and a physical model of the tea garden; the photovoltaic system sub-model in the system is used for simulating the generating capacity and the working efficiency of the photovoltaic system in the tea garden under different weather conditions; a data acquisition module in the system acquires tea garden data based on a sensor deployed in a tea garden; a strategy generation module in the system predicts a tea growth state and irrigation requirements, predicts a power generation trend of a photovoltaic system and determines an irrigation strategy based on tea garden data, a digital twinborn model and a digital twinborn model of the photovoltaic system; a sprinkling irrigation regulation and control module in the system executes an irrigation task based on the irrigation strategy; the interaction module in the system is used for interacting with a user. According to the invention, water saving, emission reduction and intelligent management are realized.

The invention, which relates to the technical field of the tea garden safety monitoring system, discloses a smart tea garden safety monitoring system based on the Internet of Things technology, comprising a sensing layer, a network transmission layer, a platform layer and an application layer. The system constructs a space-air-ground integrated stereoscopic perception network through the environment monitor, the unmanned aerial vehicle cluster and the ground inspection robot, realizes comprehensive acquisition of tea garden multi-source data, introduces a digital twinborn technology, constructs a virtual model synchronized with a physical tea garden in real time, and combines a multi-target game algorithm and a reinforcement learning mechanism to realize comprehensive acquisition of tea garden multi-source data. Meanwhile, the environment monitoring instrument adopts a liftable stand column design and is provided with multiple automatic reinforcing devices, and through linkage mechanisms such as a spring clamping ring, a telescopic supporting rod and a side inserting structure, the stability and the anti-overturning capacity of equipment in a complex environment are remarkably improved; and the safety and the reliability of data acquisition are effectively ensured.

The invention discloses a smart tea garden block chain traceability system and method, and the method comprises the following steps: carrying out the standardization processing and aggregation of data of each link, and generating an event set identifier; performing compact commitment on the event set by adopting an RSA accumulator to generate a staged commitment value; introducing a BLS threshold signature mechanism, and performing joint signature on the event set by a plurality of trusted nodes to generate an aggregation signature; binding and writing the commitment value and the aggregation signature into a block chain; in traceability query, the existence of events is verified through an RSA accumulator, and node consensus is verified through a BLS threshold signature, so that a safe, transparent and verifiable traceability system is constructed. The system covers the whole process information management of tea from planting, processing, storage, transportation to sales.

The invention discloses a tea garden ecosystem health evaluation method and system based on landscape multifunctionality. The method belongs to the technical field of tea garden management and ecological environment evaluation, and comprises the following steps: determining tea garden ecological system health evaluation indexes in four aspects based on an'vitality-organization power-restoring power 'framework and in combination with an ecological system service theory; a three-layer hierarchical structure model is constructed through an analytic hierarchy process, index weights are calculated in combination with an entropy weight method, feature importance sorting is performed on indexes by adopting a random forest algorithm, and an evaluation index system is constructed; carrying out dimension reduction processing on the indexes by utilizing a principal component analysis method and calculating weights; and based on the evaluation index and the weight after dimension reduction processing, calculating a comprehensive health score of the tea garden ecosystem by adopting a weighted summation method, and performing grading. By integrating various theories and methods, the health condition of the tea garden ecosystem is evaluated comprehensively and objectively, a scientific basis is provided for management and protection of the tea garden, and sustainable development of the tea garden ecosystem is promoted.

The invention relates to the technical field of tea intelligent consultation, and discloses a tea expert remote consultation method based on artificial intelligence. The method comprises the following steps: processing growth environment data through a tea tree physiological status dynamic analysis module to generate a tea tree physiological status feature vector; historical agronomic operation nodes are extracted based on management records, and the physiological state feature vectors are associated to construct a tea tree response mode knowledge base; triggering a consultation request according to tea tree phenotypic data collected in real time, and integrating the physiological status feature vector and the response mode knowledge base through a cross-dimension feature fusion engine to generate a multi-factor association decision tree; performing iterative adjustment on the node weight of the decision tree by adopting a self-adaptive decision optimization module, and outputting a tea tree growth abnormality diagnosis result and an agricultural intervention scheme; and transmitting the scheme to terminal equipment through a remote consultation interface, and updating an operation validity mark of the response mode knowledge base. The method realizes remote precise consultation of tea planting and assists scientific management of a tea garden.

The invention discloses a tea garden cultivation intelligent management method and system based on a block chain, and relates to the technical field of intelligent agriculture, and the system distributes a unique Ta-Chain ID for each tea tree through a block chain data storage module, guarantees the non-tampering and traceability of tea production, and verifies the data integrity and non-tampering. The environment and tea tree data are collected in real time through the Internet of Things sensing module, and the processed information is uploaded to the block chain. And the intelligent contract module triggers farming tasks based on the data, such as intelligent irrigation, fertilization and pest control. And through the intelligent market analysis module, tea leaf demands are predicted through an AI model, and a production strategy is optimized. Through combination of an information service platform and a cross-chain technology, tea garden data sharing is promoted, whole-course tracing of tea leaves is provided through a block chain + NFT technology, and market transparency and consumer trust are enhanced.

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.

The invention provides a tea garden drought grading prediction method and system based on machine learning in the technical field of smart agriculture. The method comprises the steps of S1, collecting a large amount of historical monitoring data from a tea garden; s2, preprocessing each historical monitoring data and then constructing a data set; s3, analyzing the data set through an L-imma algorithm, a COX survival regression model and a GLM generalized linear model to obtain an environmental factor correlation graph; s4, creating a tea garden drought prediction model based on the multi-modal feature extraction layer, the feature fusion layer, the soil entropy prediction layer and the drought mapping output layer; s5, training the tea garden drought prediction model through the data set, and deploying the trained tea garden drought prediction model; and S6, predicting the drought of the tea garden through the deployed tea garden drought prediction model. The method has the advantages that the accuracy of tea garden drought prediction and the refinement degree of irrigation decision are greatly improved.

This invention discloses the application of organic household waste biochar in tea garden soil improvement and tea quality enhancement, belonging to the field of soil improvement technology. This application uses organic household waste as raw material, and the biochar prepared through pretreatment, oxygen-limited carbonization, and product collection is applied to tea garden soil to achieve soil improvement and / or tea quality enhancement. During the biochar preparation process, the target temperature for the oxygen-limited carbonization stage is 300℃~500℃, the heating rate is 7~9℃ / min, and the holding time is no less than 60 hours. The biochar is applied by trenching and fertilizing between tea rows followed by landfilling, with an application rate of 0.8~1.2kg / m², which is particularly suitable for tea garden soil. This invention overcomes the limitations of a single resource utilization path for organic household waste and poor adaptability of materials for tea garden soil improvement, achieving synergistic effects of solid waste resource utilization and tea garden quality enhancement. The technology is simple, low-cost, suitable for large-scale promotion, and has both environmental and agricultural application value.