46 results about "Ripeness" patented technology

The invention discloses a self-adaptive apple maturity lossless picking flexible claw and a picking method. The device comprises a base assembly, and a visual recognition module, a flexible clamping jaw assembly, a distributed pressure sensing module, a transmission execution module and a control unit are integrated on the base assembly. And the visual module adopts a depth camera and a YOLO11n model, judges the maturity grade in real time based on the apple red area proportion and is matched with the corresponding grabbing threshold force. The pressure sensing module is a thin film pressure sensor, and multi-point pressure feedback is achieved through a plurality of pressure detection points. The transmission module adopts a single motor to drive a lead screw nut lifting table, opening and closing of clamping jaws are synchronously controlled, the structure is light and simple, and cost is low. The picking method follows the process of recognition, matching, force control and picking, a cutter is not needed, lossless and high-success-rate self-adaptive picking is achieved, the problem of damage or failure caused by traditional fixed force control is effectively solved, the method is suitable for dense orchards, and the intelligent and lossless levels of apple picking are improved. The control unit is low in response delay and small in overshoot, and it is ensured that the grabbing force is accurately matched with the fruit state.

The invention relates to a fruit maturity nondestructive intelligent detection method and system based on high-frequency dynamic excitation and acoustic vibration signal analysis. The method comprises the following steps: controlling an excitation device to apply high-frequency transient mechanical excitation to a fruit; synchronously collecting a vibration response signal on the surface of the fruit and an internal sound wave signal; extracting dynamic feature vectors including formant characteristics, sound wave attenuation coefficients and time domain attenuation constants from the sound vibration signals; inputting the feature vector into a time sequence convolutional network model to obtain a final maturity grade and a judgment confidence coefficient; and starting an active learning mechanism for a low-confidence sample, and continuously optimizing the model through manual annotation and incremental learning. By detecting the internal physical characteristics of the fruits, the defects that the traditional visual and tactile methods are interfered by surface states and the internal maturity is difficult to judge are overcome, and rapid, lossless and high-precision intelligent detection of the maturity of the fruits with self-evolution capability is realized.

Global food systems face a paradox: rising demand and widespread hunger coexist with massive food waste, especially fresh produce. Short shelf life, poor forecasting, and lack of transparency lead to unnecessary disposal, contributing to greenhouse gas emissions and sustainability challenges. Consumers also lack reliable information on freshness and nutritional value, further worsening waste. Hence, embodiments of present disclosure provide a method and system for traceability, quality assessment and end of life recommendations of a food item. Initially an image of the food item is captured and a machine-readable tag on the food item is scanned to retrieve traceability data. Then, using trained Visual Language and physics-based models, visual and biochemical features are analyzed to compute ripeness, shelf life, and freshness scores. Further, usage recommendations and end-of-life options are recommended, including recycling guidance for packaging and upcycling opportunities for leftovers. This integrated approach ensures transparency, optimizes consumption, and supports sustainability.

This invention discloses a fruit harvesting method, relating to the fields of agricultural harvesting and post-harvest preservation. The fruit harvesting method includes the following steps: acquiring infrared detection data of the fruit to be harvested; processing the infrared detection data based on a maturity model to obtain fruit harvesting parameters; determining whether the fruit harvesting parameters meet preset harvesting parameter requirements, which are used to determine the harvestable state of the fruit; and harvesting the harvestable fruit to a collection area. The technical solution of this invention uses infrared equipment to acquire real-time infrared detection data of the fruit to be harvested, and then obtains fruit harvesting parameters through a maturity model. Based on the fruit harvesting parameters and preset harvesting parameter requirements, it determines whether the corresponding fruit meets the preset parameter requirements, effectively solving the problems of inaccurate and inconvenient maturity determination in existing methods for harvesting fragrant pears.

The application discloses a fruit visual ripening method, system and device suitable for a transport vehicle, and relates to the technical field of fruit ripening, and aims to realize real-time monitoring of fruit ripening during fruit transportation and control of a ripening agent and temperature and humidity, so that the fruit can reach a preset maturity when transported to a destination, and the purposes of saving storage and improving capital recycling efficiency are achieved.

The utility model discloses a fruit and vegetable storage box and refrigeration equipment, the fruit and vegetable storage box comprises a box body and a ripening device, the box body is provided with a ripening space, the temperature of the ripening space is a first temperature, and the first temperature is greater than or equal to 12 DEG C and less than or equal to 15 DEG C; the ripening device is arranged in the ripening space, and the ripening device is configured to perform ripening on fruits and vegetables in the ripening space in a physical mode. The ripening device provided by the utility model performs ripening in a physical mode, does not need the limitation of high-temperature conditions, can slow down nutrition consumption and moisture loss of fruits and vegetables by setting the temperature of the ripening space to be between 12 DEG C and 15 DEG C in a low-temperature environment, is favorable for maintaining the freshness of the fruits and vegetables, is not too low in temperature, and is convenient to use. The cold damage phenomenon of the fruits and vegetables can be avoided, so that low temperature is combined with physical ripening, the maturity of the fruits and vegetables can be improved, and meanwhile the quality of the fruits and vegetables can be guaranteed.

The application relates to the technical field of fruit recognition, and provides a fruit maturity recognition method, a recognition device, a control device and a storage medium, the method comprising the following steps: acquiring visual information and tactile information of a fruit to be recognized; inputting the visual information and the tactile information into a preset feature analysis network for feature analysis processing, so as to obtain a visual feature vector corresponding to the visual information and a tactile feature vector corresponding to the tactile information; inputting the visual feature vector and the tactile feature vector into a preset feature fusion network for feature fusion processing, so as to obtain a combined feature vector corresponding to the fruit to be recognized; inputting the combined feature vector into a preset maturity prediction network for prediction processing, so as to obtain maturity probability distribution data corresponding to the fruit to be recognized; and determining the maturity of the fruit to be recognized based on the maturity probability distribution data. The application aims to improve the accuracy of fruit maturity recognition.

The invention belongs to the technical field of sea grass culture, and particularly relates to an eelgrass seed after-ripening method. The invention provides an eelgrass seed after-ripening method which comprises the following steps: collecting eelgrass reproductive branches, and removing main branches of the eelgrass reproductive branches to obtain eelgrass reproductive branch branches with Buddhist bracts; hanging the eelgrass reproduction branch branches in seawater for after-ripening to obtain eelgrass seeds. According to the method, the main branches of the eelgrass reproduction branches are removed, nutrient resource consumption of redundant tissues to the branches is reduced, more favorable conditions are created for development of the Buddha bracts and the seeds, the perfectness ratio and the seed maturity of the Buddha bracts in the after-ripening period are increased by 30% or above, the seed recovery rate is increased to 90% or above, the seed activity reaches 100%, the cost is reduced by 30% compared with that of a conventional method, and the method is suitable for large-scale popularization and application. After sowing, the seedling establishment rate exceeds 50%, the overall effect far exceeds that of a conventional collection and after-ripening mode, and the method is easy to operate, low in cost and more suitable for large-scale repair and reconstruction of the seaweed bed.

The invention relates to the technical field of unmanned aerial vehicle image recognition, and discloses a jinggang honey pomelo fruit recognition ripeness judgment and grading system based on an unmanned aerial vehicle image, and the system comprises a central processing unit, and the input end of the central processing unit is electrically connected with the output end of a marking module. Jinggang honey pomelo fruit trees can be quickly photographed through an unmanned aerial vehicle and a camera of the data acquisition module, different Jinggang honey pomelo fruit trees can be marked through a response marker of the marking module, and marked signals can be received through a receiver in the unmanned aerial vehicle, so that photographed pictures are conveniently received, and the accuracy of the Jinggang honey pomelo fruit trees is improved. A data analysis device of the data analysis module can analyze the images collected by the unmanned aerial vehicle, mark numbers are provided to cover and integrate the images of the same tree, so that the images of the fruits on the Jinggang honey pomelo fruit tree are obtained, then the maturity and the insect attack rate of the fruits on the Jinggang honey pomelo fruit tree are calculated through the images, and the fruit maturity and the insect attack rate of the fruits on the Jinggang honey pomelo fruit tree are calculated. And the grading module can grade each Jinggang honey pomelo fruit tree conveniently.

The invention discloses a high-efficiency and low-cost durian ripening acceleration method, and belongs to the technical field of fruit ripening acceleration, the durian ripening acceleration method comprises the following steps: placing durian with the first-grade ripening degree in a fruit packaging carton, taking an ethephon controlled release bag, soaking with water, placing in the fruit packaging carton, closing a carton cover, placing at the temperature of 25-28 DEG C for 4 days, opening the carton cover every 6-8 hours for ventilation for 1-2 minutes, and then taking out the durian; the invention further discloses a preparation method of the ethephon controlled release bag. Compared with an existing ethylene gas ripening method, the method can be implemented by adopting a common fruit packaging carton, professional equipment is not needed, and the cost is reduced; compared with an ethephon liquid ripening method, the method has the advantages that ethylene gas is slowly and continuously released through the hydrolysis effect of the ethephon controlled release bag, uniform ripening of the durian is achieved, negative problems caused by uneven spraying of the ethephon liquid are effectively avoided, the environmental protection risk is greatly reduced, and the method is suitable for large-scale popularization and application.

This invention provides a fruit preparation process using ultrasonic permeation combined with hot air drying. The process includes the following steps: S1: Pre-treatment stage, selecting fresh, undamaged apples free from pests and diseases and of uniform ripeness, rinsing them with running water to remove impurities, and drying them. The fruit preparation process using ultrasonic permeation combined with hot air drying provided by this invention can effectively shorten the overall drying cycle, reduce production energy consumption, improve fruit drying efficiency, better adapt to the needs of industrial-scale production, and reduce enterprise processing costs. The final post-treatment stages of manual screening and vacuum packaging further improve product storage stability, reduce product defect rates, and improve product texture and rehydration properties, making the taste after rehydration close to that of fresh fruit, significantly increasing product added value. This process has a wide range of applications and can be widely used in the drying and processing of various common fruits.

A hand-held berry harvesting device consisting of a ripeness detection module with sensor arrays for assessing color and firmness, a microcontroller for real-time analysis of sensor data using an artificial intelligence model trained on ripeness thresholds to identify ripe berries, a soft, adaptive gripper for enclosing the identified berries with adjustable pressure, a low-frequency vibration module to assist detachment from the plant, and a collection path for receiving the harvested berry.

The invention discloses a low-temperature after-ripening method for malus spectabilis seeds in a desertification-free refrigeration house, and relates to the field of low-temperature after-ripening method.The low-temperature after-ripening method comprises the following steps that S1, multi-dimensional fruit grading and targeted repairing are conducted, directional pretreatment and targeted repair are carried out on fruits with different maturity degrees and damage grades; s2, adaptive stepped pre-cooling: according to the maturity and damage grade of the fruits, different pre-cooling strategies are adapted; s3, performing zoning and layering temperature-changing after-ripening; and S4, carrying out integrated connection treatment. According to the method, fruit maturity and micro-damage grading is completed within 2 h after harvesting, directional pretreatment and targeted repair are matched, the situation that dormancy of low-maturity fruits is not thoroughly removed is avoided, the hidden danger of mold breeding caused by damaged fruits is eliminated, meanwhile, by combining after-ripening environment humidity and gas concentration regulation and control, reproduction of penicillium and fusarium is reduced, the seed mildew rate is reduced, and the seed quality is improved. The after-ripening uniformity of various fruits is improved.

The utility model discloses a macadamia nut maturity rapid determination device, which comprises a base, a fixed cylinder, an opening and closing mechanism, a clamping mechanism, a detection measuring cylinder, a take-out mechanism, a scale line and a determination line, the upper part of the base is fixedly connected with the fixed cylinder, the opening and closing mechanism is arranged in the fixed cylinder, the clamping mechanism is arranged in the fixed cylinder, and the detection measuring cylinder is fixedly connected with the determination line. The detection measuring cylinder is arranged in the middle of the clamping mechanism, and the taking-out mechanism is arranged at the upper end of the detection measuring cylinder. Compared with the prior art, the device has the advantages that the detection measuring cylinder with the scale marks and the judgment line is arranged, and the macadamia nuts are immature fruits above the judgment line and mature fruits below the judgment line by observing the corresponding positions of the macadamia nuts in the measuring cylinder, so that the oil content in the nuts can be quickly judged; the nut screening machine is small in size, simple in structure and convenient to use, and the production and use cost is greatly reduced.

The refrigeration equipment comprises a box body, a first storage box assembly and a second storage box assembly, the first storage box assembly and the second storage box assembly are arranged in the box body, the first storage box assembly is provided with a first storage space, the first storage box assembly comprises a ripening device, and the ripening device is configured to ripening fruits and vegetables in the first storage space in a physical mode; the ripening device is further configured to perform ripening at a first temperature, the first temperature is higher than the temperature in the refrigerating chamber, and the first temperature is higher than or equal to 12 DEG C and lower than or equal to 15 DEG C; the second storage box assembly is provided with a second storage space and comprises a ripening restraining device, and the ripening restraining device is used for restraining fruits and vegetables in the second storage space from ripening. The ripening device is arranged in the first storage space, and the ripening restraining device is arranged in the second storage space, so that the refrigeration equipment has a ripening function and a ripening restraining function at the same time, the ripening degree of fruits and vegetables can be improved, the fruits and vegetables can be prevented from being over-ripe, and the requirements of users can be met.

The invention relates to the technical field of ovens, in particular to a food maturity identification method for an oven. Food positions are obtained through radar probe non-contact distance measurement, a first heating strategy is generated accordingly, and the power of upper and lower pipes is intelligently distributed to improve heating uniformity; meanwhile, the food maturity is judged through infrared probe non-contact temperature measurement, a second heating strategy is generated, and accurate temperature control is achieved. The electric control module combines double strategies to cooperatively control the heating load, and synchronously responds to food space position and thermodynamic state changes. According to the method, non-contact and multi-dimensional food state sensing and intelligent regulation and control are achieved, the problems that a traditional probe insertion type scheme is sensitive in position, tedious in operation and limited in applicability are solved, cooking uniformity, energy efficiency and finished product quality are remarkably improved, and dependence on user experience is reduced.

The present application belongs to the technical field of fruit and vegetable transportation, and particularly relates to an e-commerce transportation method of high-temperature yellow-ripe bananas. The present application transports and sells bananas by combining and packaging bananas of different ripeness degrees according to transportation air temperature and transportation time, can use the time in the transportation process to ripen, and thus slowly ripens bananas of different ripeness degrees, so that consumers can receive a combination of bananas that can be directly eaten and bananas that can be ripened by continued storage, and ensure that consumers can eat bananas with correct ripeness and taste. Meanwhile, the e-commerce transportation method of the present application directly transports bananas from the field source to consumers through express delivery, that is, a new industry form that directly sells bananas from farmers to consumers, reduces intermediate links and time, is easier to trace the product source, can timely feedback the production and sales situation of varieties, ensures the quality of bananas, reduces the price of bananas, and improves the sales volume of bananas.

The invention relates to the technical field of fruit and vegetable quality detection and evaluation, and discloses a fresh chili fruit texture multi-parameter determination method based on whole fruit puncture, and the method comprises the following steps: step S1, selecting complete, damage-free and ripeness-consistent fresh chili fruits; s2, a texture analyzer is used, a puncture probe is configured, and parameters are set; s3, planning a plurality of puncture measurement points along the longitudinal axis direction of the fruit, wherein each measurement point is located on the side wall of the fruit and avoids the position under a fruit stem and a fetal base; puncture tests are sequentially carried out according to the planning points, and data of puncture points are obtained; s4, analyzing a force and time curve and / or a force and displacement curve obtained in the puncture process, and calculating to obtain texture parameters; and S5, outputting a texture evaluation result. The invention provides an accurate, comprehensive, standardized and practical pepper fruit texture evaluation and diagnosis tool, and pepper quality evaluation is changed from subjective experience to objective quantification.

The invention discloses a durian maturity detection method based on improved YOLOv8n and a multi-mode large model. The durian maturity detection method comprises the following steps: acquiring durian image data sets of different maturity; randomly dividing the data set into a training set, a verification set and a test set, and performing data enhancement on the training set; marking immature and mature states of the durians; a coordinate attention mechanism is embedded in the YOLOv8 model, and a CA-YOLOv8 model is constructed; respectively using the training set and the verification set to train and optimize a CA-YOLOv8 model; and inputting a detection result of the CA-YOLOv8 model into the multi-mode large model, and generating a comprehensive analysis report of durian maturity detection. According to the method, intelligent judgment and multi-dimensional information output of durian maturity can be realized, the hallucination phenomenon in large model output is effectively inhibited while the detection precision is improved, and the credibility of a result is enhanced.

The invention discloses a picking robot fruit ripeness evaluation method fused with atlas reasoning, which comprises the following steps of: acquiring image data and environment data of a target fruit and a surrounding environment thereof, and performing time synchronization, space registration and noise filtering to obtain multi-modal sensing data; image data in the multi-modal sensing data are preprocessed; depth features and environment features are extracted and integrated to form a multi-modal feature vector; constructing a fusion map of the fruit features and the environment features; performing a map reasoning process to generate fruit ripeness reasoning embedded features; the atlas reasoning embedded features are input into an improved evidence deep learning algorithm, an evidence dynamic adjustment coefficient is introduced, adaptive correction is performed, and the credibility and uncertainty of the fruit ripeness grade are calculated; and carrying out confidence correction, and outputting the fruit ripeness grade. According to the method, graph reasoning and self-adaptive correction are fused, intelligent evaluation of the fruit ripeness is achieved, and high precision and high stability are achieved.

The present application relates to fruit and vegetable ripening equipment technical field, specifically provide a kind of fruit and vegetable ripening synergic control method and system, the method includes: real-time acquisition multiple ripening unit's environmental parameter and fruit and vegetable state data, and environmental parameter and fruit and vegetable state data are preprocessed;According to the kind, quantity and target maturity of fruit and vegetable to be ripened and the environmental parameter and fruit and vegetable state data after ripening unit preprocessing, the temperature and humidity, ethylene release amount and gas circulation frequency of corresponding ripening unit are adjusted, and the multiple ripening unit collaborative operation is realized.The present application is according to the kind, quantity and target maturity of fruit and vegetable to be ripened and the environmental parameter and fruit and vegetable state data after ripening unit preprocessing, the temperature and humidity, ethylene release amount and gas circulation frequency of corresponding ripening unit are adjusted, and the multiple ripening unit collaborative operation is realized, can ensure that the ripening environment of all ripening unit remains consistent, effectively improves the uniformity of the same batch fruit and vegetable maturity.

The utility model provides a novel ripeness measuring tool for watermelons, and relates to the technical field of watermelon ripeness measuring tools, the novel ripeness measuring tool for watermelons comprises a first fixing plate, the inner wall of the first fixing plate is provided with two sliding grooves, the inner walls of the two sliding grooves are slidably connected with sliding blocks, a sliding plate is fixedly connected between the outer surfaces of the two sliding blocks, and the sliding plate is fixedly connected with the inner wall of the first fixing plate. A moving shaft is fixedly embedded in the center of the inner wall of the sliding plate, and one end of the moving shaft is fixedly connected with a knocking rod. According to the device, the motor is arranged to drive the sliding plate to move, the sliding plate can drive the knocking rod to move and extrude the spring at the same time when moving, when the rotating rod rotates to a certain degree, the extrusion force on the spring disappears, the spring can drive the knocking rod to knock the outer surface of a watermelon, and knocking sound can be transmitted into the sound wave sensor; the ripening of the watermelons is distinguished through the knocking sound, so that the completeness of the peel of the watermelons is guaranteed, and the storage time of the watermelons is prolonged.

The embodiment of the invention provides a refrigerator control method, a controller, a refrigerator and a storage medium, in the embodiment of the invention, a spectral imaging module is arranged in a cabin of the refrigerator, and image information and spectral information of food materials are obtained through the spectral imaging module; determining the food material type of the food material according to the image information, and determining the food material maturity of the food material according to the spectral information; according to the food material maturity, the cabin is controlled to open a target mode corresponding to the food material maturity, and the target mode comprises a ripening acceleration mode or a storage mode; and in the target mode, adjusting storage environment parameters of the cabin according to the food material types and the food material maturity. According to the embodiment of the invention, under the condition that the spectral imaging module is arranged, the food material type and the food material maturity can be obtained, and then the cabin is controlled for processing, so that identification of fruit and vegetable types and accurate ripening acceleration are realized, and the user experience is better.

The invention discloses a food material ripening control method and device of refrigeration equipment and the refrigeration equipment, and belongs to the technical field of refrigeration equipment. The refrigeration equipment comprises a ripening acceleration device, and the method comprises the steps that according to image information of a target food material in a chamber of the refrigeration equipment, the type and color and luster characteristics of the target food material are determined; determining the maturity grade of the target food material based on the type and the color characteristics; based on the maturity grade, determining a ripening acceleration control parameter corresponding to the target food material; and based on the ripening control parameter, controlling the ripening device to ripening the target food material. According to the food material ripening control method of the refrigeration equipment, the food materials in different ripening states can be processed by adopting the differentiated ripening conditions, so that the ripening result of the food materials is closer to the expected ripening state, and the use experience of a user is improved.

The invention provides a cold chain transportation management method and system based on fruit and vegetable maturity, and relates to the technical field of transportation management, and the method comprises the steps: obtaining a surface image of a to-be-transported fruit and vegetable, so as to analyze the maturity grade; after control parameters are matched according to the destination coordinates, the fruit and vegetable types and the maturity grades, the warehouse environment of the cold-chain transport vehicle is adjusted; and in the transportation process, the arrival time and the ripening rate of the fruits and vegetables are calculated, so that the warehouse environment of the cold-chain transportation vehicle is dynamically controlled after the adjustment parameters are analyzed. According to the method, maturity analysis is carried out on fruit and vegetable surface images, in the transportation process, environment data and the remaining transportation distance in a warehouse are collected in real time, the fruit and vegetable maturity rate and arrival time are analyzed, corresponding adjustment parameters are generated, self-adaptive adjustment control is carried out on the warehouse environment according to the adjustment parameters, and the problem that cold chain parameter control is not matched is solved.

The invention discloses a method for physically ripening fruits based on magnetic field treatment and capable of regulating and controlling the ripening process and application. According to the method, different fruit ripening acceleration effects are achieved by adjusting the magnetic induction intensity, the magnetic field treatment period and the intensity of the atmosphere cold plasma coupled with the magnetic field. The conditions are as follows: the magnetic induction intensity is 10-120 Gs, the treatment mode is magnetic field short-time intermittent treatment, magnetic field continuous treatment or magnetic field coupling atmosphere cold plasma treatment, and the treatment period is 30 minutes to 24 hours per day; the magnetic field coupling atmosphere cold plasma concentration is 0-450 ppm (based on the ozone concentration); the maturity of the respiration climacteric fruit is a green ripe stage; the temperature of the ripening chamber is 15-35 DEG C, and the relative humidity is 70-95%. And the fruits can reach a commercial mature state 2-12 days in advance. The technology provided by the invention can accurately regulate and control the after-ripening process of fruits, and has the advantages of greenness and no pollution.

The invention relates to an image recognition method and device for an eggplant picking machine. The method comprises the following steps: acquiring an eggplant image collected by the eggplant picking machine; performing feature extraction on the eggplant image based on a scale scaling strategy of an interactive scale transformation factor improved fixed factor of eggplant feature perception to obtain a feature set; performing feature fusion on the feature set based on feature importance evaluation and an adaptive feature fusion mechanism to obtain a fused feature; optimizing eggplant color features in the fusion features to obtain final color features; and identifying the updated fusion feature based on conditional gating and a bidirectional information flow mechanism to obtain the maturity and position information of the eggplant. According to the method, dynamic information complementation between classification and regression tasks can be realized, and maturity judgment and positioning precision is improved; finally, an eggplant image recognition model with high robustness and wide adaptability is constructed to accurately recognize eggplant maturity and position information in different environments, and reliable technical support is provided for automatic picking.

This invention discloses an indentation-type soft and hardness measurement device and method for identifying the maturity of post-ripening fruits. The device includes a shell, a fixed ring, a drive mechanism, an indentation self-locking mechanism, a sensing module, and a control module. The drive mechanism is installed between the shell and the fixed ring, the indentation self-locking mechanism and the sensing module are placed below the fixed ring, and the control module is installed on the inner wall of the shell. The input end of the control module is connected to a five-channel bridge circuit of the sensing module, and the output end of the control module is connected to a two-phase four-wire motor of the drive mechanism. The indentation-type soft and hardness measurement device of this invention can quantify the soft and hardness information of post-ripening fruits and judge the maturity of post-ripening fruits at different stages of maturity. Moreover, the probe in the device of this invention is made by micron-level 3D printing, which helps to identify the maturity of fruits with slight and non-destructive indentation. It is expected to be applied to the intelligent identification of post-ripening fruits such as kiwifruit and green mango.

The application discloses a dry-cured ham maturity prediction method based on feature voting gated multimodal fusion, and belongs to the technical field of machine vision and food detection. The method comprises the following steps: collecting machine vision images and near-infrared spectrum data of the ham, and respectively performing pretreatment; extracting visual depth features through a deep residual network embedded with an attention mechanism, and extracting spectrum depth features through a multi-scale one-dimensional convolutional neural network; mapping the two types of features to a common subspace and splicing into a joint feature vector; adopting a TopK voting gating mechanism, dynamically screening key features through context enhancement and Bayesian uncertainty scoring, and forming a sparse fusion feature vector; and finally, based on a base classifier group composed of a support vector machine, a random forest and a multilayer perception mechanism, combining a logistic regression meta-learner to perform integrated decision-making, and outputting a maturity prediction result. The application realizes balanced complementation and adaptive fusion of multimodal features, and significantly improves the prediction accuracy and model generalization ability.