7518 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.

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 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 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 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.

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.

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 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 machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention discloses a tea leaf fermentation degree determination method with image recognition, which comprises the following steps: shooting tea leaves through a high-definition camera and a camera in different tea leaf processing stages according to different tea leaves to obtain high-definition images of the tea leaves, preprocessing the collected tea leaf images, and determining the fermentation degree of the tea leaves according to the preprocessed tea leaf images. Through denoising, contrast enhancement and color correction technologies, an acquired image is preprocessed, the image is converted into a proper color space for extracting color features in the tea image, and dominant hue and color distribution on the surface of tea are analyzed. The invention relates to the technical field of tea leaf detection, and the method can realize non-destructive, real-time and accurate tea leaf fermentation degree monitoring, and automatically judges the fermentation state of tea leaves by shooting tea leaf images and analyzing visual features of colors, forms and textures of the tea leaves in combination with a machine learning algorithm. Therefore, the automation level and the production efficiency of the tea processing process are improved.

The invention discloses a microenvironment constant temperature and humidity system for tea storage, and relates to the technical field of tea storage, the system comprises a sensor array module used for performing real-time data acquisition on temperature, humidity and volatile component change in a tea storage microenvironment by deploying a high-precision sensor array, the data acquisition module is used for acquiring an original data set for storing environment parameters and tea state feedback to obtain initial monitoring information, and the data analysis module is used for performing feature extraction on the collected environment parameters and the tea state feedback by adopting a pre-established data analysis model according to the initial monitoring information, analyzing a quality change rule and sending the quality change rule to the server. Determining the chemical component change trend of the tea in the current storage stage; according to the microenvironment constant-temperature and constant-humidity system for tea storage, the constant-temperature and constant-humidity system integrating dynamic monitoring, data analysis and parameter self-adaptive adjustment is designed, a tea quality change curve is formed, and closed-loop management is achieved.

The invention relates to the technical field of food, and particularly discloses a compound prebiotics embedded probiotic microcapsule and a preparation method and application thereof.The green tea leaves serve as main raw materials, water extraction is conducted on the green tea leaves, meanwhile, ultrasonic-assisted treatment is adopted, active ingredients in the green tea leaves can be more effectively released, the extraction time is shortened, and the content of probiotics in the green tea leaves is increased; the extraction rate of active ingredients in the green tea leaves is improved; then, lactobacillus acidophilus is adopted to ferment the extract liquid, so that the green tea fermentation liquid contains more antioxidant active ingredients, combined polyphenol in the extract liquid can release micromolecular polyphenol in the fermentation process, the content of substances such as flavone and polyphenol in the green tea fermentation liquid is increased, and the antioxidant activity is further improved; and then the modified dietary fiber is used as a main wall material of the microcapsule to embed the green tea fermentation liquor and the probiotics, so that the stability of active substances and the probiotics in the green tea fermentation liquor is improved, and the green tea fermentation liquor can play a role in protection and slow release in the simulated digestion process.

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 provides bacillus megaterium and application thereof in preparation of a nano-selenium organic fertilizer and improvement of the selenium content of tea leaves, and belongs to the technical field of microorganisms, the bacillus megaterium NT2 is preserved in the China Center for Type Culture Collection with the preservation number of CCTCC NO: M 20251806, and the bacillus megaterium NT2 is preserved in the China Center for Type Culture Collection with the preservation number of CCTCC NO: M 20251806. The bacillus megatherium NT2 has relatively strong resistance to reduction on selenium element, can survive in high-concentration inorganic selenium, can reduce the inorganic selenium into nano-selenium, and has relatively weak capability of enriching arsenic, cadmium and lead; the selenium source of the nano-selenium organic fertilizer can be prepared by mixed fermentation with high-selenium rock powder which is calcined and activated at high temperature, so that the content of heavy metals does not exceed the national standard while the selenium content of the tea leaves is increased. The bacillus megaterium provided by the invention not only can reduce the toxicity of inorganic selenium in high-selenium rock and convert the inorganic selenium into nano-selenium, but also can stably, safely and standardly produce selenium-rich tea leaves, the heavy metal content of the tea leaves does not exceed the standard, and the bacillus megaterium has a wide market prospect.

The invention relates to the technical field of tea fermentation, and particularly discloses a temperature and humidity controllable black tea fermentation device which comprises an outer box, a top cover is clamped to the top end of the outer box, a fermentation assembly is arranged in an inner cavity of the outer box, ventilation assemblies are arranged on the two sides of the outer box, the fermentation assembly comprises a supporting frame, and a lifting mechanism is arranged in the middle of the supporting frame. According to the tea leaf fermentation device, black tea can be uniformly turned over through the fermentation assembly, meanwhile, tea leaves located at the stacking bottom or tightly attached to the fermentation container are shaken up and down, the bottom of the tea leaves is shaken and pushed in a small range, the tea leaves are dispersed and stirred, and the tea leaves are uniformly stirred; according to the tea leaf fermentation device, all parts of tea leaves can be in uniform contact with air, the fermented black tea can be uniformly ventilated through the ventilation assembly, a wider air flowing path is generated in the fermentation container, the uniformity of air flowing is kept, and therefore the temperature and humidity during fermentation are controlled.

The invention discloses a machine vision-based tea leaf segmented heating fixation system and method, and relates to the technical field of tea leaf processing and automatic control. Comprehensive state information such as color, moisture, temperature and three-dimensional stacking thickness of tea leaves is obtained in real time through a multi-mode visual perception module; the central control and processing module carries out fusion processing on the data, dynamically divides a tea processing area into a plurality of micro-areas, and independently carries out self-adaptive precise segmented heating and physical parameter cooperative regulation and control on each micro-area in combination with a machine learning prediction model. And the advanced prediction model can perform continuous learning and self-evolution based on the evaluation result of the final finished product quality, so that the tea fixation quality and uniformity can be remarkably improved, the production efficiency and the automation level are improved, the process adaptability and flexibility to different raw materials and processing requirements are enhanced, and the method is suitable for large-scale popularization and application. And intelligent and traceable process control and continuous optimization oriented to final sensory quality are realized.

The invention provides an improved YOLOv11 model-based tea plant disease and insect pest detection method in a complex environment, which comprises the following steps of: acquiring an original image through field shooting, performing data amplification by using a data enhancement technology, and realizing bounding box labeling by using a labeling tool label Img to form a training data set; the method comprises the following steps: constructing a network model of tea plant diseases and insect pests, improving an original model structure of YOLOv11n, and improving a Head module by using an MSDAF module; improving a loss function; performing end-to-end training on the network architecture by using the training set and the test set, and adjusting hyper-parameters; and performing field detection on the tea leaf scab area by using the trained network model. The invention provides a novel tea disease and insect pest detection algorithm based on a YOLOv11 target detection algorithm, and the average precision of tea disease and insect pest detection in a complex environment can be effectively improved.

The invention relates to a method for identifying the fermentation degree of oolong tea based on machine learning, and belongs to the technical field of intelligent manufacturing of tea leaves. According to the method, characteristic component factors obtained from oolong tea are preprocessed, a training set and a test set for oolong tea fermentation type discrimination are constructed, and different fermentation types of oolong tea are discriminated by adopting a machine method combining statistics and ensemble learning. The method can accurately distinguish different fermentation types of oolong tea.

The invention discloses a method for preparing tea wine by using a tea leaf gradient extraction technology. The method comprises the following steps: detecting the content of main flavor / taste substances in tea leaves; performing gradient extraction on the tea leaves; detecting the content of alcoholic strength, polysaccharide, total phenols and total acids of the rice wine; and blending a leaching solution, and the like. Through gradient extraction of the tea leaves, different flavor substance components in the tea leaves are obtained, and perfect fusion of the flavors of the tea and the wine can be realized through proper proportioning and use in tea wine processing. The use of food additives, especially seasonings, is avoided, and the requirements of consumers for enjoying pure and natural tea wine flavor can be met.

The utility model relates to a multifunctional combined clamp assisting in cake pressing, which is arranged on a mechanical arm and comprises a support, a clamping and dumping device, an inner flying paper suction device, a tea grabbing device and a tea smoothing device. The support is U-shaped, and a mounting hole is formed in the bottom of the support and used for being mounted on a mechanical arm. The inner flying paper suction device, the tea leaf grabbing device and the tea leaf smoothing device are arranged on the outer surfaces of side arms on the two sides of the support respectively; the clamping and dumping device is installed in the support and can clamp a tea barrel with tea leaves and pour the tea leaves in the tea barrel into the cake pressing mold through movement of the mechanical arm. The functions of barrel pouring, smoothing, tea grabbing, tea placing and inner flying paper sucking and placing are integrated on the clamp, the clamp is driven by the mechanical arm to move to be matched with all the functions on the clamp, automation of the tea cake pressing process is completed, and the situation that due to non-uniform manual intervention operation, the quality of processed tea cakes is not uniform is avoided. And the overall quality of the product can be improved.

The invention discloses a tea drying equipment dynamic control method based on deep reinforcement learning, comprising the following steps: S1, collecting parameters of each control unit of tea drying equipment, and generating a state observation data set; s2, constructing an equipment graph structure, and outputting equipment graph structure information; s3, constructing an action value calculation network containing a dried graph neural network structure based on an improved MADDPG algorithm, and generating a global joint feature embedding set; s4, constructing a strategy network set, and generating a control action set; s5, inputting the global joint feature embedded set and the control action set into an action value prediction module, and outputting an action value prediction result; s6, the improved MADDPG algorithm is used for executing action value loss function calculation and parameter reverse updating; and S7, deploying the trained strategy network set to a tea drying equipment control system. According to the method, the MADDPG and drying graph neural network is improved, and dynamic closed-loop regulation and control of the tea leaf drying equipment are realized.

The invention belongs to the technical field of health-care products, and particularly relates to a formula helpful for improving sleep and a preparation method thereof. Comprising the following raw material components in parts by weight: 90-110 parts of spina date seed powder, 45-55 parts of casein hydrolysate peptide powder, 15-22 parts of gamma-aminobutyric acid, 18-22 parts of lucid ganoderma powder, 15-22 parts of lily powder, 18-20 parts of poria cocos powder, 12-15 parts of tea leaf theanine and 1-4 parts of lactobacillus plantarum JYLP-326. According to the formula for improving sleep provided by the invention, the limitation of an existing insomnia treatment means is broken through through a three-dimensional synergistic network of traditional Chinese medicines, probiotics and functional components, and the formula realizes systematic improvement on insomnia core symptoms through multi-channel synergy of nerve regulation, intestinal brain axis intervention and immune balance, namely rapid induction of sleep.

The present invention belongs to the technical field of fiber materials, and relates to a composite fiber containing nano natural tea fibers, and a preparation method and application thereof. The raw materials for preparing the composite fiber include nano natural tea fibers and basic fiber pulp, and the nano natural tea fibers are mate nano fiber microemulsions. The present invention integrates nanotechnology into the preparation of natural tea fibers, and adopts a microemulsion method to simultaneously and evenly disperse a variety of effective ingredients in tea in the mate nano fiber microemulsion, so that the multifunctionality of the fabric can be effectively exerted; the microemulsion system has high uniformity and long-term stability, and during the spinning process, the nano tea fibers can be highly dispersed in the spinning solution without the need for additional surfactants, and the effect of the functional fabric finally obtained can be long-lasting. In addition, mate tea is rich in tea polyphenols and unique yerba mate alkaloids, which can further improve the comprehensive performance of the composite fiber.

The application discloses an Aspergillus niger strain and a culture method and application thereof, the Aspergillus niger strain is preserved in China Center for Type Culture Collection, and the preservation number is CCTCC NO: M 20241727. The fermented tea product obtained by fermenting tea leaves or tea leaf extracts by using the Aspergillus niger strain CCTCC NO: M 20241727 has prominent sweet and floral fragrance, and the astringency is obviously reduced; the contents of tea polyphenols, free amino acids and flavones are higher than those of a fermented tea product of an existing Aspergillus niger strain; the antioxidant activity is enhanced; the tea soup color is orange red and bright; and the color, fragrance and taste qualities are all superior to those of the fermented tea product of the existing Aspergillus niger strain.

The invention discloses a functional probiotic composition and application thereof, and relates to the technical field of microbial engineering. Bifidobacterium longum, lactobacillus acidophilus and enterococcus faecalis are combined to serve as a microbial agent, the microbial agent and active substances of the tea leaves form a composition, then the composition is combined with prebiotics, it is ensured that viable bacteria directly reach the intestinal tract through a microencapsulation technology, and the effect of improving the immune function is achieved; tea polyphenol in the active substances of the tea leaves can serve as a carbon source of prebiotics to stimulate proliferation of the bifidobacterium longum and the lactobacillus acidophilus, so that the acid resistance and intestinal colonization capacity of the microbial inoculum are enhanced, and meanwhile, theanine in the active substances of the tea leaves, the bifidobacterium longum and the lactobacillus acidophilus synergistically promote secretion of gamma-aminobutyric acid, so that the effect of improving the intestinal colonization is achieved. And the tea polyphenol can inhibit the large intestine rod from adhering to the intestinal mucosa, and the probiotic agent secretes antibacterial peptide, so that the integrity of the intestinal barrier is maintained together.

The invention relates to the field of tea disease detection, in particular to a tea disease detection method based on a lightweight YOLOv8 model. According to the technical scheme, the method comprises the steps of collecting various types of tea disease images; the method comprises the following steps: preprocessing multiple types of collected tea disease images, including illumination normalization, geometric enhancement, illumination enhancement, adaptive histogram equalization, a multi-scale Retinex algorithm, dynamic Gamma correction and image noise removal, and marking scab regions in different types of tea disease images to obtain a training data set; a lightweight YOLOv8 model is constructed; using the training data set to train the lightweight YOLOv8 model; and inputting a collected tea disease image into the trained lightweight YOLOv8 model, and outputting a tea disease type through the trained lightweight YOLOv8 model. The method is suitable for tea disease detection.