11083 results about "Tea leaf" patented technology

The invention discloses a tea processing auxiliary method and system based on intelligent decision making, and the method comprises the steps: carrying out the tea processing monitoring, obtaining tea processing monitoring data, carrying out the preprocessing of the obtained tea processing monitoring data, and obtaining the cross-modal tea processing monitoring information; analyzing a cross-modal association relationship of each monitoring data based on the cross-modal tea processing monitoring information, and constructing a multi-modal fusion feature to obtain a multi-modal tea processing fusion feature; constructing a tea leaf processing quality sensing model, inputting the multi-modal tea leaf processing fusion features into the trained tea leaf processing quality sensing model to sense the processing quality of the tea leaves of the current processing batch, and obtaining tea leaf processing quality sensing information; and judging whether the tea quality of the current processing batch meets the expectation or not based on the tea processing quality sensing information, and if not, optimizing the current processing scheme to carry out tea processing assistance. The sensing ability of the tea processing process is enhanced; and the final tea processing quality is improved.

The invention relates to the technical field of tea leaf detection, and discloses a tea leaf quality grade detection method and system.The method comprises the steps that a tea leaf sample is collected and pretreated; extracting chemical component indexes and physical properties of the pretreated tea leaves, and performing fusion and analysis; and establishing a tea quality grade classification model, inputting characteristics of a tea sample to be detected, outputting a detection result, and generating a tea quality grade report. Through combination of the support vector machine, the gradient boosting decision tree and the improved logistic regression algorithm, the precision of the tea quality classification model is optimized. The dynamic threshold function and the segmented excitation function are introduced to further solve the boundary fuzzy problem of the high-grade tea leaves and the low-grade tea leaves, and accurate judgment of tea leaf grade classification is achieved. And data restoration is carried out through weighted compensation correction and a recheck process, so that the output tea grade report is ensured to have high accuracy and reliability.

The invention provides a composition beneficial to improvement of brain cognition, memory and sleep and prevention and improvement of Alzheimer's disease, and belongs to the technical field of health food. The composition comprises L-alpha-glycerophosphorylcholine (GPC), phosphatidylserine (PS), docosahexaenoic acid (DHA), tea leaf theanine (L-theanine), vitamin B groups, vitamin D3 and the like, and is prepared into a health care product, a dietary supplement, a functional food, a medical food or a nutritional supplement by adopting the existing technology. The traditional Chinese medicine composition has the effects of effectively nourishing nerve cells, improving the brain sensitivity and improving the cognitive function. The composition is particularly directed at age-related cognitive decline and neurodegenerative diseases. Repair and regeneration of nerve cells can be promoted, the transmission efficiency of neural signals is enhanced, and the overall working efficiency of the brain is improved. Daily life challenges caused by cognitive decline can be relieved, and individual life quality can be improved.

The invention relates to the technical field of tea quality detection, in particular to a tea quality detection and analysis method and system. The method comprises the following steps: performing hyperspectral image acquisition on a tea sample, and performing RGB image acquisition to obtain an original hyperspectral image and an RGB image; performing spatial information registration on the original hyperspectral image according to the RGB image to obtain a registered hyperspectral image; performing spectrum-space data fusion according to the registered hyperspectral image and the RGB image to obtain spectrum-space fusion data; performing endoplasm and appearance feature analysis on the spectrum-space fusion data to obtain a multi-modal feature vector; and performing feature vector decoupling on the multi-modal feature vector to obtain an endoplasmic decoupling feature and an appearance decoupling feature. According to the method, the internal quality evaluation and the appearance evaluation of the tea leaves are organically combined, a unified evaluation system is constructed, and the comprehensive, objective and refined evaluation of the tea leaf quality is realized.

The invention discloses a tea disease detection method and system, and belongs to the technical field of image processing, and the method comprises the steps: constructing a tea disease detection model, inputting an obtained to-be-detected tea disease image into the tea disease detection model, and carrying out the feature extraction of the tea disease image through introducing a residual block of ResNet as a backbone of a backbone network, obtaining a multi-scale feature map of the tea disease image; a self-attention mechanism is introduced into the extracted feature map, multi-scale feature maps of the tea disease image are input into the self-attention mechanism in parallel, and global and local feature maps of the tea disease image are acquired by capturing global and local information of the tea disease image; and fusing the multi-scale, global and local feature maps of the tea disease image to obtain a fused tea disease representation, detecting the tea disease, and outputting a tea disease detection result. The method improves the accuracy of tea disease detection.

Provided in the present invention are a creation method and apparatus for a tea leaf disease recognition model, and a device and a storage medium. The creation method comprises: acquiring a plurality of images of tea leaves, and disease and insect pest information corresponding to each image; generating a tea leaf disease recognition model, wherein the tea leaf disease recognition model includes a MobileNetV3 model, an ultra-lightweight attention model and a fully connected layer; and on the basis of the plurality of images of tea leaves and the disease and insect pest information corresponding to each image, training the tea leaf disease recognition model. The tea leaf disease recognition model can automatically recognize tea leaf diseases and insect pests.

The invention discloses an intelligent tea identification and grading method and system based on a neural network, and relates to the technical field of intelligent tea identification and grading, in-depth learning and feature extraction are performed on tea images through a neural network classification module and a convolutional neural network, and fine features such as color, shape and size of tea can be identified. And high-precision grading is carried out. Compared with traditional artificial grading, the neural network system eliminates interference of artificial deviation and subjective factors, and ensures consistency and reliability of grading results. Through adaptive filtering and image enhancement algorithms in the image preprocessing module, the system can automatically carry out illumination correction, noise removal, background removal, image sharpening and other processing in the image acquisition stage, and the definition and quality of the image are greatly improved. According to the system, through automatic image acquisition, processing, feature extraction and grading processes, manual intervention and operation can be greatly reduced, and the tea grading efficiency and speed are improved.

The invention discloses a tea garden soil improvement method based on microbial communities. The tea garden soil improvement method aims at improving soil fertility and tea tree yield by means of precisely screening beneficial microorganisms, optimizing culture conditions and the like. Firstly, soil samples are collected from a tea garden area, gene high-throughput sequencing is carried out, soil microbial community composition information is obtained, and beneficial microorganisms are screened out. Then, the breeding speed and biomass of the beneficial microorganisms are optimized by adjusting a culture medium formula. When the bio-fertilizer is prepared, besides concentrated bacterial liquid, an organic carrier, inorganic nutrient elements and humic acid serving as a stabilizer are also added, and a proper amount of trace elements and a plant growth regulator are added. In spring or autumn, according to the physicochemical property of the soil, the bio-fertilizer is uniformly spread and ploughed into the soil layer. After fertilization, soil physicochemical properties and tea tree growth conditions are regularly detected to evaluate the soil improvement effect and adjust the fertilization strategy according to the soil improvement effect. The method is beneficial for improving the tea garden soil health condition and improving the tea yield and quality.

The invention discloses a lightweight tea disease target detection method based on TeaDisease LiteNet, and the method comprises the following steps: collecting and marking tea disease images in a real environment, and constructing a tea disease data set; the method comprises the following steps of: dividing a main network into a training set, a verification set and a test set, performing data enhancement, using a MobileNetV3 module in the main network, using a Slim-neckAKConv module in the neck network, adding an iRMBEMA attention mechanism, using a Shape-IoU regression loss function, and using a SlideLossEMA classification loss function; the optimized target detection model is obtained through training, the model is used for tea disease detection, and the method has the technical characteristics that the problems that an existing tea disease detection model is high in calculation complexity, large in model parameter quantity, difficult to efficiently operate on equipment with limited resources and the like can be solved.

The invention relates to a production monitoring management method and system for tagatose sugar-free tea. The method comprises the following steps: carrying out correlation analysis on key parameters in the production process of the tagatose sugar-free tea to obtain a correlation production parameter set containing temperature, stirring speed, pH value, solubility, tagatose addition amount, tea extraction temperature and tea extraction time; performing quality index prediction through the quality index prediction model to obtain a quality index prediction value; predicting by taking the quality index predicted value and the temperature-stirring speed data subset as the input of a two-stage learner-evaluator multi-task sequential neural model, and creating a temperature and stirring speed control strategy; a temperature and stirring speed control strategy is applied to the production process of the tagatose sugar-free tea, a quality related characteristic set of the production process is collected, quality evaluation and fusion analysis are carried out, and a comprehensive quality evaluation result is obtained. The implementation of the invention improves the production efficiency and quality stability of the tagatose sugar-free tea.

The invention discloses a tea quality data safety management monitoring method and system, and particularly relates to the field of intelligent agriculture, and the method comprises the following steps: I, collecting various tea quality data of tea planting, processing, warehousing and logistics processes in real time, and integrating historical quality evaluation data as a comparison reference source; iI, calculating the reliability weight of each piece of tea quality data in the current environment in real time, performing weighted fusion processing on each piece of data, and performing homomorphic encryption on the fused data; according to the method, privacy leakage is effectively prevented, the adaptability of a detection model is enhanced, the real-time response speed in the detection process is increased, the detection accuracy and the automation level are improved, the reliability and stability of multi-source heterogeneous data fusion are improved, the robustness and the intelligent error correction capability of the whole monitoring system are enhanced, and the monitoring efficiency is improved. And multi-chain linkage, data consistency verification and non-tampering evidence storage are realized.

The invention relates to the technical field of tea garden disease and insect pest monitoring, in particular to a tea garden disease and insect pest monitoring method and system based on photovoltaic light supplement. The method comprises the following steps that photovoltaic light supplementing equipment is arranged, and tea garden environment data are obtained; carrying out photovoltaic light supplement operation according to the tea garden environment data, and carrying out tea tree image acquisition through camera equipment to obtain tea tree image data; tea tree trunk feature extraction and tea tree tea feature extraction are carried out according to the tea tree image data, and tea tree trunk feature data and tea tree tea feature data are obtained; performing pest assessment according to the tea tree trunk feature data and the tea tree tea feature data to obtain pest assessment data; and performing pest distribution generation according to the pest evaluation data, the tea tree trunk feature data and the tea tree tea feature data to obtain pest distribution data. According to the invention, an intelligent and automatic tea garden disease and insect pest monitoring and prevention and control system is formed by combining photovoltaic light supplement and disease and insect pest simulation.

The invention discloses a method and a system for adjusting a tea leaf fixation process and a medium, and aims to realize cooperative regulation and control of enzyme activity and aroma substances in a fixation process. The method comprises the following steps: collecting multi-modal sensing information in a fixation process, and carrying out feature extraction on the multi-modal sensing information to obtain multi-dimensional features; dynamically constructing an LSTM prediction model based on the multi-dimensional features, and outputting an enzyme activity prediction value sequence; the enzyme activity predicted value sequence is input into a fuzzy PID controller for reinforcement learning optimization, a temperature-rotating speed adjusting instruction is generated, and adjusting parameters of the controller are dynamically updated through a reward function of the chlorophyll retention rate and the energy consumption ratio; according to the temperature-rotating speed adjusting instruction, adjusting the hot air temperature of each temperature area of the enzyme deactivation chamber, the rotating speed of the enzyme deactivation cylinder and the axial segmentation wind speed; and collecting fixation end point judgment information, and terminating fixation when a preset condition is met. According to the invention, the problems of unstable quality and overhigh energy consumption caused by parameter solidification in the traditional enzyme deactivation process are solved, and dynamic optimization and accurate control of the enzyme deactivation process are realized.

The invention discloses a tea storage bin management system and method, and particularly relates to the field of tea intelligent storage. Comprising a storage environment modeling module, an information input classification module, a structure identification and authentication module, an environment gradient sensing module, a breathing parameter monitoring module, a tea quality sensing module, a storage environment regulation and control module, a storage opening and closing management module, a quality decay modeling module, a storage dynamic early warning module and a storage strategy generation module. According to the invention, environment blind areas can be effectively avoided, the accuracy and responsiveness of regulation and control are improved, quality deterioration signals can be extracted in real time, quality abnormity early warning can be given out in advance, manual detection errors and delay are greatly reduced, the storage safety is improved, dynamic gas interaction evaluation between tea leaves and the environment in the warehouse is realized, and the quality of the tea leaves is improved. The scientificity and effectiveness of regulation and control are improved, excessive storage or early failure is effectively avoided, differentiated and intelligent management is realized, and the automation and intelligent level is remarkably improved.

The invention provides a tea tree fresh leaf identification method and system based on YOLOv8 in the technical field of tea leaf detection, and the method comprises the following steps: S1, obtaining a large number of historical images of tea tree fresh leaves, carrying out the preprocessing of data annotation and data expansion of each historical image, and constructing a data set; s2, creating a tea tree fresh leaf recognition model based on a YOLOv8 network, and setting a loss function of the tea tree fresh leaf recognition model as an I nner-S I oU function; s3, dividing the data set into a training set, a verification set and a test set based on a preset proportion, and training, verifying and testing the tea tree fresh leaf recognition model through the training set, the verification set and the test set; and S4, deploying the tea tree fresh leaf recognition model passing the test, and performing tea tree fresh leaf recognition by using the deployed tea tree fresh leaf recognition model. The method has the advantage that the identification accuracy of the fresh tea leaves is greatly improved.

The invention discloses an impurity sorting and removing system based on machine vision, and the system comprises a data collection and preprocessing layer which is responsible for obtaining multi-dimensional physical characteristics of tea leaves and impurities, and constructing three-dimensional scene representation; the feature analysis and decision-making layer is used for extracting light field fusion features by utilizing a ResNeSt-50 backbone network, directly outputting a preliminary classification confidence coefficient through a full connection layer, forming a first layer of decision-making candidates, simulating branches by combining rigid body dynamics to predict a blade movement track, performing Bayesian correction on the preliminary classification confidence coefficient after a shielding probability graph is sampled and calculated through Monte Carlo Dropout, and forming a second layer of decision-making candidates; meanwhile, a Cook-Torrent BRDF model is integrated to carry out material reflection correction, a feature map after material correction is output, and a correction value is converted into decision threshold offset; and the knowledge storage and update layer is used for constructing a bimodal memory pool composed of a gradient direction matrix and an LRU elastic cache, and generating a pseudo sample supplement long-tail category in combination with the four-layer full-connection pulse neural network.

The invention discloses an intelligent regulation and control method for a tea fermentation process and a regulation and control system based on microbial activity monitoring, and relates to the technical field of tea processing. According to the method, microbial activity and environmental parameters are monitored in real time, and a microbial metabolism model and a neural network algorithm are combined, so that dynamic optimization and accurate regulation and control of the tea leaf fermentation process are realized, firstly, microbial activity data and environmental parameters in a fermentation environment are collected by utilizing a sensor array; the method comprises the following steps: firstly, calculating a metabolite generation rate and a concentration change trend through a microbial metabolism model, accurately identifying abnormal fluctuation in a fermentation process, secondly, adjusting fermentation conditions in real time based on the metabolite concentration change trend and a dynamic regulation and control algorithm to ensure the stability of the fermentation process, and finally, carrying out real-time fermentation. Tea quality parameters after fermentation condition adjustment are predicted through a neural network algorithm, a regulation and control strategy is optimized, and stable improvement of tea quality is achieved.

The invention discloses a tea fertilizer preparation method based on microbial fermentation, and particularly relates to the field of tea compost, tea waste is crushed and wetted, and a nitrogen source and trace elements are added to optimize the carbon nitrogen ratio, so that a foundation is laid for the fermentation process; then, selected microbial strains are proliferated and purified, and the treated waste materials are uniformly inoculated with the selected microbial strains; in the fermentation process, the stability of fermentation conditions is ensured by controlling the temperature and the humidity; the tannin concentration and the pH value are monitored in real time, when the tannin concentration exceeds a preset value and the pH deviates from a normal range, the abnormal state of tannin degradation is evaluated and identified through a reaction activity index, an interference source is positioned through a sparse principal component analysis algorithm, and noise signals are removed; and after the interference source is positioned, the fermentation condition is adjusted in a targeted manner, so that the tannin degradation efficiency reaches the expectation, and the compost is maturely fermented and meets the fertilizer use standard.

Present invention teaches the method of using a keratin hydrolysis peptide (“KHP”) solution to enhance the yield and quality of tea leaves. By selectively choosing specific weights of feathers and water, and treating the mixture to a high-temperature high-pressure hydrolysis process, the resulting solution is confirmed to contain at least 253 peptides and then apply to the tea trees either by infusing to the soil or by leaf spray at certain growth stages. Optionally, the KHP solution can be diluted by water, as disclosed in the specification, for soil infusing or leaf spraying.

The invention provides a tea hyperspectral image ROI region screening method and computer equipment, and the method comprises the steps: calculating the reflectivity of a main vein image and M-type mesophyll ROI, and obtaining M + 1-type reflectivity. And carrying out image segmentation on the preprocessed image, and removing a main leaf vein image from the leaf image to obtain a mesophyll image. And calculating a correlation coefficient between the target component of the tea leaf and each reflectivity, constructing a reflectivity evaluation model according to the correlation coefficient, and selecting an optimal reflectivity and a corresponding final ROI region by using the reflectivity evaluation model. The correlation coefficient can reflect the relation between local image features of mesophyll and main leaf veins and the reflectivity, and the fitting degree and prediction precision of the reflectivity evaluation model to the hyperspectral image of the tea leaf can be comprehensively measured in combination with different evaluation modes. According to the method, the mesophyll region and the main vein region of the tea leaf are separated, and the optimal reflectivity and the corresponding final ROI region are screened out by using the reflectivity evaluation model.

The invention discloses a tea variety and quality classification method based on deep learning Teformer, and relates to the field of spectral analysis and the field of artificial intelligence algorithms. The construction of the classification method is based on a PCA dimension reduction method and a Transform neural network model, and quality evaluation is carried out on the tea leaves through a lossless, rapid and efficient means. By collecting visible-near infrared spectrum data of the tea leaves and performing feature extraction and classification through Teaformer, accurate classification and quality evaluation of the tea leaves are realized. Comprising the following steps: collecting visible-near infrared spectrum data of a certain number of tea samples, and preprocessing the data, including de-noising and standardizing; training the spectral data by using a Teammer model, and learning internal characteristics of the spectral data; unknown samples are predicted and classified through the model, and the quality of the tea leaves is evaluated; and in combination with actual application requirements, model parameters are optimized, so that the accuracy and stability of detection are improved.

As the withering process of tea leaves takes a long time to reach a desired moisture level, estimating when it is time to move to the next step to reach the target tea leaf moisture is difficult and inefficient. Method and system disclosed herein provide an approach for withering schedule prediction of tea leaves. The system, by performing a spectral data analysis on an image of a plurality of tea leaves, estimates the moisture percentage in the plurality of tea leaves, for a selected time stamp. Based on the predicted moisture level, a current temperature value, a current relative humidity value, and a current time stamp, the system generates a withering schedule for the plurality of tea leaves. The generated withering schedule is fine-tuned based on a course correction of an impact of deviation in one or more ambient parameters on the prediction of the withering schedule.

The invention discloses a negative-pressure tea-leaf picker and a tea-leaf picking method. The negative-pressure tea-leaf picker comprises a frame body, the air duct is obliquely arranged on the frame body and is used for transporting the sheared tea leaves; the shearing tool is arranged at the lower end of one side, close to the advancing direction of the frame body, of the air duct; an adsorption opening for adsorbing tea leaves is formed in the lower end of the air duct; an air blowing opening is formed in the lower end of one side, far away from the advancing direction of the frame body, of the air duct and is connected with a fan through an air pipe; the air blowing opening comprises an adjusting plate arranged on the side close to the adsorption opening, the adjusting plate is rotationally arranged on the air channel, and the adsorption force at the adsorption opening is adjusted by adjusting the inclination angle between the adjusting plate and the air channel. According to the tea picking machine, the cut tea leaves are adsorbed and conveyed by forming negative pressure at the adsorption opening, the effectiveness of adsorbing and collecting the cut tea leaves is improved, and waste and loss caused by the fact that the tea leaves fall out of an air duct are prevented.

The invention relates to the technical field of tea quality evaluation, and particularly discloses a tea quality digital comprehensive evaluation system based on multi-source data fusion, and the system comprises a multi-source data collection module which is used for obtaining multi-dimensional data of tea; the multi-dimensional feature extraction module is used for extracting chemical quality parameters, physical characteristic parameters and process correlation parameters and generating a comprehensive quality embedding vector; the digital evaluation model construction module is used for generating a quality grade division rule and a quality defect traceability path; and the tea quality comprehensive evaluation module is used for generating a multi-dimensional quality evaluation report according to the quality grade division rule and the quality defect traceability path. Through three technical breakthroughs of multi-source heterogeneous data fusion, intelligent algorithm collaborative analysis and dynamic feedback control, the method has remarkable advantages in the aspects of accuracy, efficiency, traceability, industrial application value and the like, and an innovative solution is provided for standardized production and quality control of tea leaves.

The invention relates to the technical field of image data processing, in particular to a multispectral monitoring and optimizing method and system for tea tree growth in a tea garden. The method comprises the following steps: acquiring tea tree growth spectrum data; performing tea tree growth spectral feature extraction according to the tea tree growth spectral data to obtain tea tree growth spectral feature data; performing tea tree growth index calculation according to the tea tree growth spectral feature data to obtain tea tree growth index data; and performing growth state monitoring according to the tea tree growth index data to obtain tea tree growth state monitoring data so as to perform tea tree growth and cultivation optimization operation. By continuously monitoring the growth state of the tea trees and performing dynamic optimization, the tea trees can be ensured to be always in the optimal growth environment, so that the yield and quality of the tea leaves are improved to the maximum extent.

The utility model relates to the technical field of tea leaf screening, in particular to a screening box with a tea leaf layered collection function, which comprises a base, a support frame is rotatably arranged on the surface of the base, a screening cylinder is rotatably connected to the surface of the support frame, and a driving mechanism for driving the screening cylinder to rotate is arranged on the surface of the support frame. And a feeding pipe is arranged at the end part of the screening cylinder. According to the tea leaf screening device, the screening cylinder is arranged to be the first filter cylinder, the second filter cylinder and the outer cylinder which are coaxial, and then the driving mechanism is used for driving the screening cylinder to rotate, so that the first filter cylinder and the second filter cylinder in the screening cylinder rotate synchronously, and tea leaves can be screened into large, small and crushed tea leaves; the tea leaves can be conveniently collected in a layered mode, the tea leaves can be protected through the rotating mode, and compared with a traditional mode that a sieve plate and stirring are directly utilized, the phenomenon that large tea leaves are damaged is avoided.

Provided are a multi-layer fermentation tank for tea leaves and a use method thereof. The multi-layer fermentation tank for tea leaves includes a tank body, a placement assembly, a heater, and an oxygen supply machine. The placement assembly includes a support frame, and a tray. The support frame includes a turnover plate, laminate groups located at upper and lower sides of the turnover plate, and a top cover located above the laminate group at the upper side. A telescopic connection mechanism is arranged among the turnover plate, the laminate and the top cover, an upper surface of each of the turnover plate and the laminate is provided with a placement groove, the tray is placed in the placement groove, and a positioning member is arranged between the tray and the placement groove. Gas holes are formed in each of the turnover plate, the laminate, the tray, and the top cover.

The invention discloses a tea leaf fixation segmented conveying device and method, and relates to the technical field of tea leaf fixation. A uniform smearing mechanism comprises a rotating rod, a U-shaped plate, a door blocking plate, a rotating shaft, a sector plate, a telescopic rod, an L-shaped rod, a telescopic buckle, a round head column, a U-shaped rod, a push plate, a rotating column, a sliding block and a connecting rod; the U-shaped plate is fixedly installed on the circumferential face of the rotating rod, the door blocking plate is fixedly installed at the bottom of the feeding port, the side, away from the feeding port, of the upper side of the rectangular frame rotationally penetrates through the rotating shaft, and one end of the telescopic rod is fixedly installed on the circumferential face of the rotating shaft. And meanwhile, the influence on the transportation of the tea leaves due to the fact that the tea leaves are scattered and accumulated towards the two sides is avoided.

The invention discloses tea leaf production equipment based on tea leaf fermentation, and particularly relates to the technical field of tea leaf processing.The tea leaf production equipment comprises a conveying cylinder, a supporting shaft, a rotating part, a first lantern ring, a second lantern ring and a spiral auger, and the two ends of the spiral auger are fixedly connected to the peripheries of the first lantern ring and the second lantern ring respectively; when the first lantern ring moves towards the second lantern ring, the spiral packing auger generates telescopic deformation in the axial direction of the supporting shaft, and the spiral face of the spiral packing auger is fixedly connected with a protruding strip. When a second lantern ring moves towards a first lantern ring, spiral augers are in a compressed state, then tea leaves located between the spiral augers are gathered, a supporting shaft is driven by a rotating part to rotate, the spiral augers stir-fry the tea leaves, and the tea leaves are stir-fried more conveniently. And meanwhile, the convex strips arranged on the spiral surface of the spiral auger have a rolling effect on the tea leaves, so that the tea leaves do not need to be rolled after being discharged, the operation steps are simplified, and large-scale production of tea leaf processing can be realized.

The invention discloses an intelligent control system and method for a tea tender shoot picking robot, and the method comprises the steps: obtaining scene image information through a visual analysis module, and accurately recognizing a to-be-picked tea tender shoot region based on a preset neural network model; a task decomposition module is used for dividing a tea tender shoot area, each task in a large number of picking tasks is actually distributed to a corresponding mechanical arm, and a shortest safe working route of the mechanical arm is formulated; through the motion control module, it is ensured that the picking range of each mechanical arm of the robot can completely cover the area where the tea tender shoots suitable for being picked are located, and the missing picking rate of the tea tender shoots is effectively reduced. Multiple mechanical arms of the picking robot can be coordinated to pick tea tender shoots, mechanical picking has the advantages of being controllable in cost, accurate in collection and excellent in efficiency, data calculation and control of the whole system are completely conducted locally, smoothness and high efficiency of the system can be guaranteed, and popularization of the tea picking robot technology is facilitated.