3268 results about "Tobacco leaf" patented technology

The invention relates to the technical field of tobacco logistics and intelligent control, and discloses an intelligent temperature and humidity regulation and control system and method for tobacco transportation, and the system comprises a multi-dimensional environment sensing module, a self-adaptive strategy generation module and a closed-loop efficiency verification module. The method comprises the following steps: capturing three-dimensional space parameters in a transportation carrier in real time through distributed sensing nodes, outputting a structured environment situation matrix, and providing a space-time reference for a regulation and control decision; after receiving the environmental perception data, executing dynamic strategy optimization, establishing a transportation stage-spatial position two-dimensional regulation and control strategy library, processing sensitivity differences of tobacco leaves in different regions by adopting an asymmetric weighting algorithm, and generating a regulation and control scheme containing a gradient regulation instruction; real-time evaluation and strategy iteration of the regulation and control effect are achieved, microenvironment response detection is deployed, tobacco physical characteristic change data are collected, and a triangular verification model is constructed. The method effectively controls moisture content distribution, inhibits browning and mildew risks, and maximally retains tobacco leaf quality characteristics.

The present invention relates to the field of tobacco breeding, and more particularly to molecular markers for screening for spotted wilt-resistant tobacco plants free of linkage drag and their applications. A method for screening spotted wilt-resistant tobacco plants or germplasm with reduced linkage drag is provided, comprising: (a) screening for spotted wilt-resistant tobacco plants or germplasm and isolating nucleic acid thereof; (b) detecting a first linkage drag locus marker and / or a second linkage drag locus marker in the isolated nucleic acid; the first linkage drag locus marker comprising the NaChr4_2M marker shown in SEQ ID No. 10 and / or the NaChr4_8M marker shown in SEQ ID No. 11; the second linkage drag locus marker comprising the NaChr3_62.6M marker shown in SEQ ID No. 7 and / or the NaChr3_64.6M marker shown in SEQ ID No. 8; and (c) selecting a spotted wilt-resistant tobacco plant or germplasm that does not contain the first linkage drag locus marker and / or the second linkage drag locus marker. This method can be used to obtain spotted wilt-resistant tobacco plants free of linkage drag, providing superior varieties with high spotted wilt resistance for tobacco leaf production.

The invention provides a cigarette station violation operation early warning method and system based on artificial intelligence. The method comprises the following steps: firstly, obtaining a multi-modal monitoring data set of a target cigarette station, wherein the multi-modal monitoring data set comprises tobacco leaf quality, equipment operation logs and environmental parameters; performing time sequence behavior feature extraction on the set to obtain a tobacco processing flow time sequence behavior track sequence, equipment operation compliance and environment abnormal fluctuation feature set, and then calling a pre-trained multi-mode violation identification model to perform dynamic weight distribution on the features to generate a fusion violation feature vector; the method comprises the following steps: obtaining a fusion rule-violation feature vector, determining rule-violation operation probability distribution based on the fusion rule-violation feature vector, further generating a cigarette station rule-violation early warning strategy, finally feeding back the cigarette station rule-violation early warning strategy to a cigarette station supervision terminal, triggering a rule-violation operation interception instruction, and realizing effective early warning and interception of cigarette station rule-violation operation by using multi-modal data and an artificial intelligence technology.

The invention discloses a tea tree CsU3 gene promoter and application thereof, and belongs to the technical field of plant biotechnology and gene engineering. Tea tree RNA polymerase III type promoters-tea tree endogenous U3 gene promoters CsU3a, CsU3b, CsU3c and CsU3d are successfully cloned for the first time, a tea tree U3 gene promoter activity detection carrier is successfully constructed, the transcriptional activities of the four CsU3 promoters are compared through instantaneous tobacco leaf conversion, GUS dyeing and GUS enzyme activity determination, and the CsU3 gene promoter activity detection carrier can be used for detecting the activity of the tea tree U3 gene promoter. CsU3a and CsU6b promoters with strong transcriptional activity are selected to be used for constructing a tea tree gene editing carrier, the feasibility of the promoter applied to a tea tree CRISPR / Cas9 gene editing system is verified in a tea tree protoplast, and CRISPR / Cas9 mediated tea tree genome targeted editing is realized.

The invention relates to the technical field of tobacco leaf storage, in particular to an intelligent regulation and control method and system for a tobacco leaf storage environment, and the method comprises the following steps: based on a tobacco leaf storage bin, fusing temperature, humidity and oxygen concentration sensor data, obtaining environment characteristics, analyzing airflow velocity and stacking density, identifying abnormal regions, counting variety deviation, and carrying out the control grading of associated equipment. And environment regulation and control are optimized. According to the method, multiple types of sensor data are integrated, the environment states of different storage partitions are reflected through space correlation and weighting operation, the sensitive partitions with local abnormity are effectively locked by means of correlation analysis of airflow and stacking density, tobacco variety characteristics and storage stage parameters are combined, grading dynamic recognition of environment sensitive points is achieved, and the method is suitable for large-scale popularization and application. The equipment response sequence is configured in a layered manner, and the regulated and controlled parameter trend adjustment operation is combined, so that the long-term quality stability of various tobacco leaves in the storage space is enhanced, and the quality deviation risk caused by environmental disturbance is reduced.

The invention provides a method for intelligently and accurately judging the color shape state of tobacco leaves in the curing process, and belongs to the technical field of tobacco leaf curing. The method comprises the steps that firstly, near infrared spectrum data and image data of the tobacco leaves are collected to establish a maturity grading standard, and internal chemical component characteristics and external chromaticity morphological characteristics of the tobacco leaves are extracted through preprocessing; respectively carrying out dimensionality reduction on the two types of data by utilizing a principal component analysis method to obtain principal component characteristics of the near infrared spectrum and principal component characteristics of the image; secondly, performing data fusion on the two types of features, performing feature importance weighting by applying a bimodal feature attention model, and learning different modal feature association and mapping relationships through a multi-head self-attention and mutual attention mechanism by the model; and according to the signal-to-noise ratio of the spectral data, the image definition score and the data consistency evaluation value, dynamically adjusting the modal weight, and finally constructing a tobacco state discrimination model based on weighted fusion data to realize accurate discrimination of the maturity grade of the tobacco.

The invention relates to the technical field of image processing, in particular to a tobacco leaf disease spot segmentation method and system based on multi-scale residual cavity convolution, and the method comprises the steps: collecting a plurality of tobacco leaf disease images as original images, and carrying out the data expansion of each collected original image through an image enhancement method, establishing a training set, a verification set and a test set based on the original image and the extended data; a semantic segmentation model is constructed, the semantic segmentation model is formed by stacking two sub-networks, and the two sub-networks are connected through an ROIE + module; in combination with the training set, training the semantic segmentation model by using the constructed loss function; verifying the trained semantic segmentation model by adopting a verification set, and selecting an optimal semantic segmentation model; the test set is adopted to test the optimal semantic segmentation model, and the performance of the optimal semantic segmentation model is evaluated; and inputting a to-be-segmented tobacco leaf disease image into the trained semantic segmentation model to obtain a tobacco leaf disease spot segmentation map. According to the method, the tobacco leaf scab area can be accurately and efficiently segmented.

The invention relates to the technical field of tobacco curing, in particular to a nitrogen preparation and storage oxygen content automatic control system which comprises a purity and pressure joint control module, a humidity regulation and optimization module, an oxygen content dynamic regulation and control module, a moisture response regulation module and a multi-parameter coupling linkage module. According to the system, intelligent dynamic adjustment is achieved through multi-parameter real-time collection and linkage feedback, synchronous judgment and optimal adjustment path formation between the nitrogen outlet purity and the pressure of each compression section, and matching of the airflow temperature, the wet film area and the nitrogen humidity, it is guaranteed that humidification is uniform and controllable in combination with automatic comparison of the outlet humidity and the storage target standard, and the system is suitable for large-scale popularization and application. The nitrogen injection flow rate and the outlet humidity directly participate in the active response process of oxygen content change, self-adaptive balance of the storage oxygen environment is achieved, moisture monitoring penetrates through the surface layer and the interior, temperature setting and the wet film area are finely adjusted along with the moisture state, and the nitrogen humidity state is finely matched with the tobacco leaf requirement.

The invention discloses a tobacco leaf quality prediction method based on prior feature weight optimization and a CNN-LSTM fusion model and a readable storage medium. Comprising steps; the method comprises the following steps: 1, collecting and preprocessing data; step 2, priori feature weight optimization is carried out; step 3, constructing a CNN-LSTM fusion model; 4, evaluating the model; and step 5, generating a quality evaluation result of the to-be-predicted tobacco leaves, inputting historical production data and images of the to-be-predicted tobacco leaves into the trained CNN-LSTM fusion model, and generating the quality evaluation result of the to-be-predicted tobacco leaves. According to the method, efficient processing of multi-dimensional data in the tobacco leaf redrying process is achieved, and the accuracy of tobacco leaf quality prediction and the stability of the model are remarkably improved.

The invention discloses a production motor operation frequency prediction model training method, which comprises the following steps: acquiring a tobacco image group, extracting tobacco image features by using a three-dimensional point cloud model, converting the tobacco image features into a tobacco cross section area change sequence, dividing production stages, and screening out pre-selected stage information of which the fluctuation ratio meets requirements; synchronously acquiring motor operation information and matching to obtain pre-selected motor information and operation frequency thereof; a Gaussian process regression algorithm is adopted to construct a prediction model, trend features and local fluctuation features are obtained through multi-scale feature extraction to serve as input, the preselected operation frequency serves as an output label for training, and hyper-parameters are optimized to obtain a final prediction model; the established dynamic confidence interval evaluation mechanism can realize model increment training. The operation frequency of the motor is dynamically adjusted according to the actual state of the tobacco leaves, and the drying quality and the batch stability are remarkably improved.

The present invention discloses a baking method for the upper leaves of GZ36 flue-cured tobacco, comprising the following steps: S1, judging the baking characteristics of the upper leaves of GZ36 flue-cured tobacco; S2, harvesting fresh tobacco leaves when ripe: harvesting fresh tobacco leaves when ripe according to the ripe characteristics of fresh tobacco leaves in low-altitude areas and high-altitude areas; S3, classifying and stringing fresh tobacco leaves and loading them into the baking barn; S4, baking fresh tobacco leaves: scientifically baking according to the baking characteristics and ripe characteristics of tobacco leaves in different regions, wherein during the yellowing stage, the dry-bulb temperature cyclic rise and fall method is adopted to promote the tobacco leaves to fully soften and wither. By judging the baking characteristics of the upper leaves of GZ36 flue-cured tobacco, formulating key baking strategies, harvesting fresh tobacco leaves when ripe according to the ripe characteristics of fresh tobacco leaves in different regions, classifying and stringing them and loading them into the baking barn, and scientifically baking according to the baking characteristics and ripe characteristics of tobacco leaves under different ecological conditions in different regions, and adopting the dry-bulb temperature cyclic rise and fall method during the yellowing stage to promote the tobacco leaves to fully soften and wither, the internal quality of the upper tobacco leaves after baking of GZ36 can be effectively improved.

The belt-type mobile loader disclosed in the present invention relates to the technical field of transportation equipment; it includes a frame 13, a transportation device, a lifting device and a moving device; the moving device includes a pair of universal wheels 6 and a pair of running wheels 25; the lifting device includes a hydraulic station 16, an oil pipe assembly and two identical lifting cylinder assemblies; A. the universal wheels 6 and the running wheels 25 are respectively installed at the lower position of the front part of the transportation device and the rear end of the frame 13; B. the lower position of the front rear of the transportation device is rotatably hingedly connected to the front end of the frame 13. It has the characteristics of simple and compact structure, convenient use, good stability, safety and reliability, etc. It can be used for the transportation of agricultural and sideline products such as kelp, tobacco leaves, beets, dried hemp, industrial products, semi-finished products and luggage, and is suitable for use in factories, shopping malls, stations, docks and agricultural and sideline products purchasing sites.

The invention discloses an ultra-low illumination tobacco leaf image enhancement method and system based on multi-scale fusion, and belongs to the technical field of tobacco leaf image enhancement. The method comprises the following steps: S1, acquiring extremely low illumination tobacco leaf image data; s2, splitting the colored ultra-low illumination tobacco leaf image into R, G and B color channels; s3, enabling the fuzzy region to be clearer through a histogram equalization method, and obtaining a first image; s4, adapting to different illumination conditions by using a dual Gamma correction algorithm, and improving visual consistency to obtain a second image; s5, using an improved CLAHE algorithm to retain channels and collaterals of the flue-curing barn tobacco leaf image, and improving the image quality to obtain a third image; and S6, using a detail enhancement algorithm to improve specific area details of the curing barn tobacco leaf image, and retaining detail features to finally obtain a curing barn tobacco leaf curing enhancement image. Through the combination of various algorithms, the tobacco leaf image can be effectively enhanced even under the extremely low illumination conditions of serious degradation such as low signal-to-noise ratio, detail information loss and color distortion.

The invention provides a tobacco leaf grade intelligent classification and quality evaluation method and system based on machine vision, and relates to the technical field of grade classification, and the method comprises the steps: collecting a tobacco leaf multi-angle image to obtain three-dimensional point cloud data, calculating the size, shape contour, surface fluctuation, color distribution and texture trend information of a leaf, and constructing a multi-scale feature descriptor; recursive feature screening is adopted to obtain an optimal weight coefficient, and a comprehensive evaluation index is generated; and determining the tobacco quality grade based on a preset grading threshold. According to the method, objective quantitative evaluation of the tobacco leaf quality is realized, the classification precision is improved, and the subjective difference of manual discrimination is reduced.

The invention provides an intelligent precise baking method and system for improving the tobacco leaf quality, and belongs to the technical field of tobacco leaf baking. The method comprises the steps that a basic process parameter set is established, and domain division is conducted according to the tobacco leaf maturity; acquiring a basic process parameter set in real time; constructing a first optimization vector, and determining a weight contribution rate through an intelligent process adaptation model; constructing a second optimization vector through a machine vision and near infrared spectrum fusion technology; establishing a stable change threshold judgment mechanism to start parameter adjustment; performing internal iterative optimization processing on the first optimization vector; calculating a process effect contribution rate; and intelligent and accurate parameter adjustment is realized through a gating weight function of the intelligent process adaptation model. The system comprises a curing barn structure unit, an environment sensing unit, a tobacco leaf information acquisition unit, a data processing unit and a control execution unit, all the units are integrated into a complete system through an industrial bus, and whole-process closed-loop management from environment sensing, tobacco leaf state monitoring to intelligent control is achieved.

The invention provides a tobacco leaf drying kinetic model-based baking process parameter optimization method, and belongs to the technical field of electrical digital data process.The tobacco leaf drying kinetic model-based baking process parameter optimization method comprises the following steps of: firstly, establishing a tobacco leaf drying differential equation model and a curing barn computational fluid mechanics model; temperature and humidity field distribution is obtained through simulation analysis; calculating a tobacco leaf surface heat and mass transfer coefficient, and establishing a moisture migration prediction function; a moisture transfer optimization function is introduced to perform parameter preliminary screening, and a pre-trained quality evaluation neural network model is adopted to evaluate a parameter scheme; respectively optimizing three stages of a yellowing stage, a color fixing stage and a stem drying stage to form a parameter set; and finally, an optimal parameter combination is searched by applying a genetic algorithm, an optimization target is a comprehensive score of tobacco leaf quality consistency and energy consumption, real-time optimization of parameters in the baking process is realized, and the technical problem that tobacco leaf baking process parameters are difficult to accurately optimize aiming at different tobacco leaf characteristics in the prior art is solved.

The invention discloses a baking method suitable for upper leaves of a flue-cured tobacco variety K326. The method comprises the following steps: S1, harvesting upper leaves of mature tobacco plants and loading them into a tobacco loading chamber of a flue-curing barn; and S2, baking the loaded upper leaves, wherein the baking comprises an early yellowing stage, a middle yellowing stage, a late yellowing stage, an early color fixation stage, a middle color fixation stage, a late color fixation stage, and a tendon drying stage. The dry-bulb temperature steadily increases in the seven stages of the early yellowing stage, the middle yellowing stage, the late yellowing stage, the early color fixation stage, the middle color fixation stage, the late color fixation stage, and the tendon drying stage. The wet-bulb temperature exhibits a "V"-shaped change in the five stages of the middle yellowing stage, the late yellowing stage, the early color fixation stage, the middle color fixation stage, and the late color fixation stage. In the middle yellowing stage and the late yellowing stage, the dry-bulb temperature remains unchanged, showing "double 39-40°C and double 41-42°C", respectively, while the wet-bulb temperature decreases by 0.5-1°C in the latter stage of each stage. The method bakes the upper leaves, significantly reducing the ash content and proportion of the upper leaves, increasing the hue of the tobacco leaves, and improving the proportion of tobacco leaf grades, thereby significantly improving the baking quality of the upper leaves and the income of tobacco farmers.

The invention provides a tobacco area nitrogen management and efficiency evaluation system, which comprises a data acquisition module for acquiring meteorological data, soil nitrogen data, crop growth data and historical fertilization data in real time; the data preprocessing module is used for carrying out noise filtering, space-time alignment and multi-source data fusion; the dynamic nitrogen prediction module is coupled with the soil nitrogen migration and crop absorption model to generate a prediction value; the optimization decision module is used for generating an application strategy based on a dynamic programming algorithm and calculating the optimal application amount and the fertilization time; the efficiency evaluation module is used for calculating nitrogen utilization efficiency (NUE) and environmental benefit index (EBI) by combining the application amount, the environmental loss amount and the crop yield; the self-adaptive feedback module is used for dynamically correcting prediction model parameters according to the deviation degree of the NUE and the EBI; and the visual output module is used for generating a multi-dimensional evaluation report and a fertilization decision map. The nitrogen utilization efficiency and the environmental benefit index can be improved, the environmental risk is reduced, and the tobacco yield and quality are improved.

The utility model discloses an on-line separation and classification device for large and medium redried tobacco sheets. The separation and classification device comprises a conveyor (1), a first-stage vibrating screen (4), a conveying plate (5), a first-stage discharge hopper (6), a second-stage vibrating screen (9), a second-stage discharge hopper (10) and a third-stage discharge hopper (11), the first-stage vibrating screen (4) and the second-stage vibrating screen (9) respectively comprise a first-stage screen plate (2) and a second-stage screen plate (7), the surface screening surfaces of the first-stage screen plate (2) and the second-stage screen plate (7) are arranged to be wave-shaped surfaces, and the wave-shaped surfaces are space curved surfaces formed by orthogonality of curved surfaces formed by moving along a first track and curved surfaces formed by moving along a second track; the loose conveying and screening effects are better, large tobacco flakes and medium tobacco flakes can be loosened and screened, for the tobacco shred making and cigarette rolling process, the medium tobacco flake rate is increased, the filament rate of tobacco shred making is controlled, the tobacco shred structure of medium and thin cigarettes is improved, and the cigarette quality is improved.

The invention provides an intelligent and accurate baking layering adjusting method for humidity gradient control, and belongs to the technical field of tobacco leaf baking. Humidity data in the vertical and horizontal directions are obtained through a sensor array, granularity ambiguity is calculated after an initial measurement matrix is established, sharpening preprocessing is performed, granularity stability of each layer is calculated based on a processing result, and the granularity stability of each layer is calculated. Analyzing and establishing a game equilibrium model in combination with a humidity mass transfer kinetic equation, outputting a coupling iteration degree, dynamically adjusting a sequence length parameter of a humidity gradient prediction model to generate a tobacco leaf baking humidity index, and generating a humidity distribution map to identify a key region and a time node according to the index; and constructing a tobacco leaf curing humidity floating matrix to record the humidity change track of each layer, performing trend prediction by adopting the adjusted prediction model, finally adjusting the temperature and humidity parameters of each layer in real time through an intelligent control algorithm to realize layered accurate control, continuously monitoring the humidity gradient change and dynamically optimizing a control strategy until a preset curing quality standard is reached.

The invention discloses a tobacco appearance quality control method and related equipment based on microenvironment intelligent maintenance, a tobacco appearance quality control model is constructed, an appearance feature extraction network is utilized, feature extraction is performed on appearance time sequence data of tobacco based on aligned timestamps, and features reflecting appearance changes of the tobacco are obtained; and performing feature extraction on the time sequence data of the environment where the tobacco leaves are located based on the aligned timestamps through an environment feature extraction network to obtain features of environment change. The appearance change characteristics and the environment change characteristics are mapped into a control network for appearance quality change analysis, the influence of the appearance change characteristics and the environment change characteristics on appearance quality change is found through the change relation between the appearance change characteristics and the environment change characteristics after alignment and time stamping, and the environment parameters are adjusted based on the appearance quality change analysis. And the tobacco appearance quality change speed is accurately regulated and controlled, so that the tobacco is in the best appearance state during use.

The invention provides a tobacco transportation real-time monitoring and management system and method. The system comprises an environment sensor array, a tobacco transportation state analysis model and a state analysis module. The environment sensor collects and preprocesses transportation environment data to generate standardized environment parameters. The tobacco transportation state analysis model is composed of three data processing modules which are connected in sequence, and each module comprises a data fusion unit and a plurality of multi-dimensional parameter analysis units. The data fusion unit generates fusion features from the input data and transmits the fusion features to the multi-dimensional parameter analysis unit. And the multi-dimensional parameter analysis unit generates weighted feature data through the environmental parameter attention module, and then outputs enhanced feature data through the data enhancement module. And the state analysis module performs parameter integration and decision mapping on the model output data to generate a transportation state evaluation result. According to the invention, high-precision dynamic monitoring and state evaluation of environment abnormity in the tobacco transportation process can be realized, and the intelligent level of transportation quality management and control is improved.

The invention relates to the technical field of tobacco leaf curing, and discloses a tobacco leaf curing state discrimination method and system, and the method comprises the steps: obtaining curing state images of different tobacco leaves, carrying out the segmentation fusion of first target regions of each tobacco leaf image based on a preset fusion strategy, and obtaining at least one target tobacco leaf image, generating an initial tobacco image data set; key tobacco leaf pictures in the initial tobacco leaf image data set are extracted, the key tobacco leaf pictures are subjected to automatic degree grade identification, and a target tobacco leaf image data set is generated; constructing a tobacco leaf baking state discrimination model based on the historical tobacco leaf image data set; the real-time tobacco leaf image is input into a tobacco leaf curing state judgment model for judgment, and the tobacco leaf curing degree is recognized; and generating a tobacco leaf curing degree report according to the tobacco leaf curing degree. The system comprises a tobacco leaf curing state obtaining module, a tobacco leaf state judgment model module and a curing state judgment module. According to the method, the tobacco leaf curing state can be accurately judged.

The invention provides a tobacco leaf curing process control method and device based on reinforcement learning, and relates to the field of tobacco leaf manufacturing. The method comprises the steps that environment parameters and tobacco leaf physical and chemical indexes in a baking room are obtained, the environment parameters comprise temperature field distribution data, humidity data and gas concentration data, and the tobacco leaf physical and chemical indexes comprise water content data, morphological data and physiological activity data of tobacco leaves; constructing a multi-dimensional state vector based on the environmental parameters and the physical and chemical indexes of the tobacco leaves; processing the multi-dimensional state vector through a pre-trained hierarchical reinforcement learning model to generate a dynamic control parameter; and the heating power, the ventilation rate and the humidity compensation amount of the baking equipment are adjusted according to the dynamic control parameters. Through the technical scheme provided by the invention, the problem of insufficient adaptability of a traditional PID control method for baking tobacco leaves is solved, the adaptive capacity of the tobacco leaf baking control process is improved, and the tobacco leaf baking quality is improved.

The invention relates to the technical field of quality detection of flue-cured tobacco leaves, in particular to an intelligent grading method, medium and system for appearance quality of flue-cured tobacco leaves, and the method comprises the steps: obtaining high-resolution multi-angle flue-cured tobacco leaf image data; noise in the image is removed through Gaussian filtering, and image quality is enhanced through histogram equalization; using a convolutional neural network to extract multi-level appearance features such as color, vein and integrity of the flue-cured tobacco leaves; carrying out binarization segmentation on the image, and extracting an accurate edge contour and leaf meridian features of the flue-cured tobacco leaves in combination with morphological operation; a deep convolutional neural network is utilized to fuse the multi-dimensional features, grading evaluation is carried out on the appearance quality of the cured tobacco leaves, and scores are output; the tobacco leaf local abnormal area identification method can be widely applied to quality detection in tobacco production, and has the advantages of high efficiency, intelligence and accuracy.

A system for the automated evaluation of tobacco leaves, comprising: • a mechanical feeding module (201) configured to feed individual tobacco leaves one after the other; • a controlled imaging chamber (202) equipped with a high-resolution camera and calibrated light sources to capture detailed images of tobacco leaves under standardized lighting conditions; • a color sensor (203) configured to extract colorimetric data from the tobacco leaves, including, but not limited to, RGB values and hue information; • a processing unit (204) comprising one or more processors and memories configured to: i) performing image pre-processing including noise reduction and contrast enhancement, (ii) Execution of segmentation algorithms to isolate the leaf from the background, (iii) Extraction of visual features such as size, shape, texture and color uniformity from the images, (iv) Analyzing the sensor data using statistical or machine learning models to determine quality parameters, (v) Integration of the extracted image features and colorimetric data into an evaluation algorithm based on predefined quality standards, (vi) to assign a quality mark to each tobacco leaf on the basis of the analysis; • an output module (205) configured to display the classification result in real time via an LED screen or a digital interface; • a storage module (206) configured to store classification results for future reference and analysis; • and an Internet of Things (IoT) communication module (207) that enables remote access, monitoring and control of the classification process.

The invention provides application of candida tropicalis and a preparation thereof in prevention and treatment of tobacco mildew, and belongs to the technical field of microorganisms. A strain of candida tropicalis is named as candida tropicalis 2-17, the preservation number of the candida tropicalis 2-17 is CCTCC NO: M 20242184, a microbial preparation prepared from the candida tropicalis can inhibit growth of main mould in tobacco leaves, and the candida tropicalis has good application potential for biological prevention and treatment of tobacco leaf mould.

The invention discloses enterobacter hormaechei XW-01 and a microbial preparation thereof, the strain (preservation number is CCTCC NO: M 20242595) can efficiently degrade protein, total sugar and nicotine in tobacco leaves, sensory indexes such as aroma and aroma quantity are remarkably improved, and protease activity is remarkably improved when the enterobacter hormaechei XW-01 and wheat bran act synergistically. The microbial preparation is obtained through the processes of strain activation, fermentation culture and microbial agent preparation, and when the microbial preparation is applied to tobacco leaf treatment, through the steps of water content adjustment, preparation spraying, sealed fermentation, low-temperature drying and the like, the technical breakthrough that the protein content is reduced, the tar release amount is reduced, and the comprehensive quality grade is improved is achieved. Compared with a traditional method, the technology has the advantages that the treatment period is shortened, the cost is greatly reduced, the COD value of wastewater is greatly reduced, the cigarette quality and the production environmental protection property are remarkably improved, and the method has a wide industrial application prospect.

The present invention discloses a flue-curing method for reducing ash on upper tobacco leaves. The method shortens the yellowing time at or before 38°C, maintains the same dry-bulb and wet-bulb temperatures at 40°C, stops the circulating fan, reduces the temperature and humidity difference between different tobacco layers in the flue-curing barn, then lowers the wet-bulb temperature. This process is repeated to cause the tobacco leaves to soften, wilt, and turn yellow. The dry-bulb temperature is then raised to 44°C, causing the main ribs to soften. The high temperature is then returned to 42°C, and the wet-bulb temperature is lowered by 1-2°C at 42°C to control the water loss of the tobacco leaves to 40-45%, inhibiting the activity of polyphenol oxidase and promoting yellowing. The dry-bulb and wet-bulb temperatures are then lowered by 1-2°C at the initial color setting stage, slowing the heating rate. A medium temperature, medium humidity, and slow heating are used for color setting in the middle color setting stage. The temperature is then slowly increased in the later color setting stage. The flue-curing time at a steady temperature of 54°C is extended to increase the water loss and dryness of the tobacco leaves. The wet-bulb temperature is increased during the dry rib drying stage. This method can significantly reduce the occurrence of ash on tobacco leaves and improve the appearance quality of the upper tobacco leaves after curing.

The invention discloses a method for identifying insect pests of tobacco leaves. The method comprises the following steps: S1, acquiring near infrared spectrum data and visible light image data of the tobacco leaves; s2, pre-processing parameters are dynamically adjusted according to the data quality indexes, smoothing processing and derivative calculation are carried out on the near infrared spectrum data, and spectrum feature vectors are obtained; s3, processing the visible light image data through a feature extraction algorithm to generate an image feature vector; s4, splicing the preprocessed spectral feature vector and the preprocessed image feature vector to form a fused feature vector; s5, a convolutional neural network model is used to process the fusion feature vector, and the convolutional neural network model comprises an attention mechanism so as to identify the insect pest type; and S6, outputting a pest recognition result. The method can solve the problems that in the prior art, the morphological characteristics of the tobacco diseases cannot be comprehensively captured due to data singleness, near infrared spectrum data are easily interfered by environmental factors, and the characteristics are lost after preprocessing, so that the accuracy of identifying the insect pests of the tobacco is improved.