75 results about "Ripeness" patented technology

The invention discloses a fresh food quality monitoring method fusing fresh food after-ripening characteristics and logistics data, and relates to the field of food science and technology, and the method comprises the steps: collecting logistics environment data, logistics process data and fresh food ontology data in real time through a distributed sensor network; time stamp synchronization and space coordinate mapping technologies are adopted, space-time alignment is carried out on heterogeneous data with different sampling frequencies, and a unified data matrix is established; the method comprises the following steps: establishing a variable parameter dynamical model comprising variety difference factors, harvest maturity and environment interaction coefficients based on post-harvest physiological mechanisms: dynamically updating model parameters of various varieties in different logistics scenes through a federated learning framework crossing supply chain nodes; when the quality attenuation curve predicted in real time deviates from a preset threshold value, graded early warning is triggered, and logistics parameter adjustment suggestions are generated. According to the invention, full-link accurate management and control of fresh products from a supply chain to a consumption terminal is realized through multi-dimensional technology collaboration.

The invention discloses a fresh commodity after-ripening regulation and control planning method oriented to maturity grading, and relates to the technical field of food science and biologication.The method comprises the steps that physiological parameters of fresh commodities are collected in real time based on a multi-modal sensor, and the physiological parameters comprise the epidermis pigment content, the ethylene release rate, the fruit hardness and the respiration intensity; the collected physiological parameters are input into a multi-objective optimization model based on deep learning, the model fuses a metabolic kinetic equation and an LSTM time sequence prediction algorithm, and a regulation and control parameter combination adaptive to the current mature stage is output; according to the regulation and control parameter combination, the temperature, the relative humidity and the O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously regulated through a distributed control system, and a plant hormone antagonist is injected through an ultrasonic atomization device; and constructing a supply chain collaborative network based on a block chain technology, binding a grading result with logistics path planning, and adjusting a temperature control strategy and a shelf life countdown parameter of the cold chain transport vehicle in real time through an edge computing node.

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.

The invention belongs to the field of postharvest preservation of fruits and vegetables, and particularly discloses a multi-element fusion intelligent fruit preservation monitoring method which comprises the following steps: extracting spectral characteristics of fruits; extracting spatial features of the fruits; environment parameters are collected; through a dynamic main feature selection module, according to the spectrum, spatial features and environmental parameters of the fruits, determining the types of the fruits, querying a fruit feature library, and automatically configuring core detection indexes; carrying out weighted fusion on the spectral feature data and the spatial feature data of the fruits through a bilinear convolutional network, and outputting fusion feature vectors of the maturity and the freshness of the fruits; coupling the fusion feature vectors of the maturity and freshness of the fruits; predicting the remaining shelf life of the fruits according to a maturity-freshness coupling model; and outputting a fruit grading decision and a shelf life early warning signal. According to the method, high compatibility and accurate maturity identification of multiple fruit varieties can be realized, and the evaluation precision of the comprehensive quality of the fruits and the prediction robustness in a complex environment are remarkably improved.

The invention discloses a banana spraying and ripening device and a using method thereof. The banana spraying and ripening device comprises a banana processing cabin, a partition plate is fixedly arranged in the banana processing cabin, the banana processing cabin is divided into a banana pretreatment cabin and a banana ripening cabin through the partition plate, a communication door is arranged on the partition plate, and a sealing door is rotationally installed on the communication door through a hinge; the spraying mechanism and the air drying mechanism are both arranged in the banana pretreatment cabin; and the banana spraying processing adjusting mechanism is mounted on the inner wall of the top of the banana pretreatment cabin. According to the device, accurate release of ethephon in banana stem / fruit body sensitive areas is realized through a microcapsule suspension spraying + HIFU ultrasonic targeted detonation technology, a four-station rotating bracket (90-degree switching) is linked with spraying, air drying and a transfer module, a three-axis positioning system is matched, banana treatment-ripening full-automatic closed loop is realized, the state of bananas is monitored in real time by a multispectral sensor, and the quality of bananas is ensured. And the ultrasonic power and the pulse duration are dynamically adjusted, so that the maturity consistency is ensured.

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.

The present invention relates to the field of mango ripening technology, and more particularly to an automatic control method and system for fruit ripening. To address the issues of uneven ripening and uneven appearance of mangoes in existing mango ripening devices, a solution is proposed. The solution includes a warehouse, an air circulation mechanism, an oxygen and ethylene replenishment assembly, and multiple storage mechanisms installed within the warehouse. The air circulation mechanism communicates with the interior of the warehouse for monitoring and replacing the air inside the warehouse. The air circulation mechanism also communicates with the oxygen and ethylene replenishment assembly for replenishing oxygen and ethylene during the circulation of air within the warehouse. The present invention ensures sufficient and uniform contact between the mangoes and the ethylene, effectively ensuring uniform ripeness and uniform appearance of the mangoes.

The invention relates to the technical field of artificial intelligence deep learning and target detection, and discloses a syzygium samarangense maturity detection method and device. The method comprises the following steps: S1, preprocessing a collected wax apple image to obtain a wax apple data set; s2, constructing a syzygium samarangense maturity detection model by improving a loss function of a YOLOv10 model and a backbone network; and S3, training the syzygium samarangense maturity detection model by using the syzygium samarangense data set, and detecting the maturity of the syzygium samarangense through the trained syzygium samarangense maturity model. The new wax apple maturity detection model is obtained by improving the loss function of the YOLOv10 model and the backbone network, and the detection model can accurately and quickly identify the wax apple maturity, thereby promoting the agricultural intelligent development of wax apples.

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 invention discloses a fruit ripening detection method and electronic equipment, and the fruit ripening detection method comprises the steps: determining the first ripening degree of a to-be-detected fruit according to the volatile gas concentration of the to-be-detected fruit; determining the second maturity of the to-be-detected fruit according to the visual features of the to-be-detected fruit; determining a third maturity degree of the to-be-detected fruit according to the fruit volume of the to-be-detected fruit; and according to the first maturity, the second maturity and the third maturity, determining whether the to-be-detected fruit is mature. According to the method, the fruit ripening detection of the to-be-detected fruit is rapidly carried out based on the volatile gas concentration, the visual characteristics and the fruit volume of the to-be-detected fruit, and the detection efficiency and the detection accuracy of the fruit ripening detection of the to-be-detected fruit are improved.

1. Name of the product in this design: Detector (for detecting grape ripeness). 2. Purpose of this design: An instrument for detecting the ripeness of grapes. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

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 present application relates to a kind of pineapple ripeness target detection method based on YOLOv10 model, belong to fruit and vegetable ripeness detection technical field, including: obtaining the pineapple image to be detected;The pineapple image to be detected is input into pineapple ripeness detection model, outputs the fruit eye ripeness score in the pineapple image to be detected, obtains the ripening grade of pineapple in the pineapple image to be detected, wherein the pineapple ripeness detection model is based on the improved YOLOv10 model is constructed and is obtained by training set training, the improved YOLOv10 model is obtained by improving the feature extraction network structure of traditional YOLOv10 model, and the training set is the pineapple image containing pineapple fruit eye ripeness annotation.This application can detect the pineapple fruit eye that existing detection model cannot detect, and can effectively solve the problem of leaf blade shielding pineapple fruit eye.

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 invention discloses an intelligent identifying and sorting robot for maturity of vegetables and fruits, which belongs to the technical field of intelligent sorting and comprises a first conveying device, a support frame, a base and a spraying mechanism. A preliminary detection mechanism is installed on the first conveying device and can dynamically detect and recognize vegetables and fruits. According to the device, the maturity of vegetables and fruits can be dynamically and visually detected through the preliminary detection mechanism, comprehensive and accurate visual maturity detection is achieved, and sorting is completed through the material guide mechanism; through the blanking bin, in the blanking process, based on the principle that the concentrations of ethylene secreted by vegetables and fruits with different maturity degrees are different, further maturity degree detection is carried out on the vegetables and fruits with qualified visual maturity degree detection, so that accurate detection on the vegetables and fruits with large sizes, such as green and awn fruits, and the maturity degree of the vegetables and fruits is difficult to judge through the appearance is realized; and a corresponding amount of ethephon solution can be sprayed according to the maturity, different degrees of ripening acceleration can be carried out on the seeds, the maturity can be controlled, and great convenience is brought to market sales.

To predict a force-ripening state of a banana in a force-ripening chamber.SOLUTION: A banana force-ripening prediction device comprises: a hue determination unit 12a which determines a hue from a color image of a banana 3 in a force-ripening chamber 2; a temperature cumulative value calculation unit 11a which calculates a temperature cumulative value on the basis of a force-ripening schedule and a temperature in the force-ripening chamber 2; an average value calculation unit 12b which calculates an average value of an individual score of the banana 3; an average value determination unit 12c which determines whether the average value of the individual score exceeds a threshold; a data output unit 13 which outputs the temperature cumulative value in the force-ripening chamber 2 as explanatory variable data and the average value of the individual score as target variable data when the average value of the individual score exceeds the threshold; a force-ripening model generation unit 14 which calculates a and b by means of a maximum likelihood estimation method on the basis of the explanatory variable data and the target variable data in a logistic regression model:1 / (1+exp(-a(x-b))); and a force-ripening state prediction unit 15 which predicts a force-ripening state of the banana 3 by applying the calculated a and b to the logistic regression model.SELECTED DRAWING: Figure 1

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.

A ripeness determination system and a ripeness determination method that include determining a degree of climacteric ripeness of a melon based on at least two factors obtained from an image of a stem of the melon, and determining whether the melon is ripe to eat based on the degree of climacteric ripeness.

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 proposes a physical ripening technology ai data model simulation system, and relates to the technical field of artificial intelligence, and the system comprises a data collection module which is used for collecting various environmental parameter data related to physical ripening, including but not limited to temperature, humidity, gas concentration, illumination intensity, and initial state data of a ripened object; and the AI data processing and modeling module is used for constructing a mathematical model of a physical ripening process by applying an artificial intelligence algorithm based on the data collected by the data acquisition module, and the model can simulate the ripening process of the ripening object under different environmental parameter combinations. According to the physical ripening technology ai data model simulation system provided by the invention, the data acquisition module, the AI data processing and modeling module, the simulation display module and the control feedback module are arranged, so that the maturity information of fruits can be acquired in real time, and the maturity condition of the fruits can be judged according to the acquired data information; and the ripening condition of the fruits can be accurately monitored.

To provide a ripeness degree estimation method for estimating a ripeness degree of fruits more appropriately than conventional methods.SOLUTION: A fruit peel, a test specimen, is irradiated with light in visible and near-infrared regions ranging from 400 to 1700 nm. Then, reflection intensity corresponding to the wavelength of the light is measured, and the reflectance is calculated from the reflection intensity. The calculated reflectance value is second-order differentiated to calculate a second-order derivative value. A ripeness degree index of the fruit is then estimated from the calculated secondary differential value on the basis of a ripeness degree prediction model. The ripeness degree prediction model represents a relation between the second derivative value of the reflectance and the ripeness degree index obtained by a sensory test. The ripeness degree prediction model is generated by partial least squares (PLS) regression analysis, with the ripeness degree index defined as a dependent variable and the second derivative of the reflectance defined as an independent variable.SELECTED DRAWING: Figure 5