Intelligent fruit defect identification method
By combining the comprehensive evaluation of fruit surface, physiological and environmental data sets, real-time monitoring of fruit status has been solved, and the problem of difficult internal defects in the existing technology has been solved, early warning and accurate insight have been achieved, and the accuracy and economic benefits of fruit detection have been improved.
Patent Information
- Application Number
- CN202510561629.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-30
- Publication Date
- 2025-07-08
AI Technical Summary
The existing fruit defect detection methods mainly rely on appearance feature recognition, making it difficult to predict internal defects and potential quality problems, lacking early warning capabilities, affecting food safety and economic benefits.
Combined with fruit surface data sets, physiological data sets and environmental data sets, comprehensive evaluation is carried out, and the fruit surface defect index and quality prediction index are calculated by arranging multimodal sensors in real time to achieve early warning and accurate insights.
Break through the limitations of traditional appearance inspection, accurately understand internal defects and potential quality deterioration, achieve early warning, ensure food safety, reduce losses, improve economic benefits, and promote refined regulation of agricultural production and intelligent industrial decision-making.
Smart Images
Figure CN120275592A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of fruit defect detection, and specifically provides an intelligent fruit defect recognition method. Background Art
[0002] Fruits are the mature ovaries of plants, usually juicy and edible, rich in sugars, vitamins, minerals, and dietary fiber, and are an important mechanism in nature to attract animals to spread seeds. They not only provide a rich source of nutrition for humans but also have become an indispensable part of the global food culture due to their diverse flavors, colors, and forms. Whether eaten directly, processed into fruit juices or jams, or used as ingredients in cooking, fruits are deeply loved by people for their natural sweetness and health value, and also play an important role in agriculture, economy, and culture.
[0003] Fruit defect detection is of great significance in modern agriculture, food processing, and supply chain management. Existing fruit defect detection methods mainly rely on the recognition of appearance features, have insufficient ability to estimate internal defects and potential quality problems, and lack dynamic evaluation of the development law of defects, making it difficult to achieve early warning. This not only directly affects food safety and economic benefits but also indirectly restricts the refined management of agricultural production and the intelligent upgrading of the industry. Summary of the Invention
[0004] (1) Technical Problems to be Solved Aiming at the deficiencies of the existing technology, the present invention provides an intelligent fruit defect recognition method, which can comprehensively evaluate the future changes of fruits by combining fruit surface datasets, physiological datasets, and environmental datasets. It can break through the limitations of traditional appearance detection, accurately identify internal defects and potential quality deterioration, predict the development trend of defects in advance, and achieve early warning. This can not only ensure food safety, reduce losses, and improve economic benefits but also promote refined regulation of agricultural production and intelligent decision-making in the industry, and optimize supply chain management.
[0005] (2) Technical Solutions To achieve the above object, the present invention provides the following technical solution: An intelligent fruit defect recognition method, including the following steps: Step 1: Sensor Arrangement: Arrange multi-modal sensors in the fruit storage area to monitor the fruits; Step 2: Data Acquisition: Acquire the monitoring data of the sensor monitors and save them with numbers; Step 3: Data Calculation: Calculate the fruit surface defect index and the fruit quality prediction index ; Step 4: Comprehensive Evaluation: Based on the fruit surface defect index and the fruit quality prediction index Conduct fruit evaluation; Step Five, Result Feedback: Feedback the fruit evaluation results.
[0006] Preferably, in the second step, the monitoring data of the sensor monitor is obtained and numbered for storage. The monitoring data includes: fruit surface data set , fruit physiological data set , and environmental data set .
[0007] Preferably, the numbering and storage expression of the fruit surface data set is: , in the expression, represents the first fruit surface data in the fruit surface data set , represents the th fruit surface data in the fruit surface data set , represents the total number of fruit surface data obtained. The fruit surface data includes: fruit surface color change data, damage data, mildew data, and abnormal texture.
[0008] Preferably, the fruit physiological data set , in the expression, represents the first fruit physiological data in the fruit physiological data set , represents the th fruit physiological data in the fruit physiological data set , represents the total number of fruit physiological data obtained. The fruit physiological data includes: fruit weight, fruit volatile organic compound content.
[0009] Preferably, the environmental data set , in the expression, represents the first environmental data in the environmental data set , represents the .th environmental data in the environmental data set , represents the total number of environmental data obtained. The environmental data includes: relative temperature, relative humidity, carbon dioxide concentration, light intensity, light time, air circulation index, storage time.
[0010] Preferably, the calculation formula of the fruit surface defect index is:
[0011] In the calculation formula, represents the fruit surface defect index, represents the fruit surface color change data in the th fruit surface data, represents the standard fruit color change data, represents the weight of the fruit surface color change data, represents the th fruit surface data for damaged data, represents the standard fruit damaged data, represents the weight of the damaged data, represents the th fruit surface data for mildew data, represents the standard fruit mildew data, represents the weight of the mildew data, represents the th fruit surface data for abnormal texture data, represents the standard fruit abnormal texture data, represents the weight of the abnormal texture data.
[0012] Preferably, the fruit quality prediction index has the following calculation formula:
[0013] In the calculation formula, represents the fruit quality prediction index, represents the fruit surface defect index, represents the initial fruit weight, represents the th fruit physiological data for fruit weight data, represents the th fruit physiological data for the content of volatile organic compounds in the fruit, represents the environmental fruit impact value.
[0014] Preferably, the environmental fruit impact value has the following calculation formula:
[0015] In the calculation formula, represents the environmental fruit impact value, represents the impact of relative temperature on fruit quality, represents the actual relative temperature, represents the optimal relative temperature, represents the relative temperature sensitivity coefficient, represents the base of natural numbers; represents the impact of relative humidity on fruit quality, represents the actual relative humidity, represents the optimal relative humidity, represents the humidity sensitivity coefficient; represents the impact of carbon dioxide concentration on fruit quality, represents the actual carbon dioxide concentration, represents the semi-inhibitory concentration; represents the impact of the air circulation index on fruit quality, represents the actual air circulation index, represents the optimal air circulation index, represents the maximum allowable air circulation index, represents the storage time.
[0016] Preferably, when the calculated value of the fruit surface defect index is greater than the fruit surface defect index threshold, it represents that the current fruit has defects, and a fruit surface anomaly signal is sent for feedback.
[0017] Preferably, when the calculated value of the fruit quality prediction index is greater than the fruit quality prediction index threshold, it represents that the environment during the current fruit storage time will affect the fruit, and a fruit physiological anomaly signal is sent for feedback.
[0018] Compared with the prior art, the present invention provides a method for intelligent identification of fruit defects, having the following beneficial effects: 1. By arranging multi-modal sensors in the fruit storage area, the present invention can accurately monitor environmental parameters such as temperature, humidity, and gas composition in real time, as well as the state of the fruit itself, which helps to timely regulate the environment to extend the preservation period, discover and process deteriorated fruits in advance, optimize inventory management, reduce losses, ensure fruit quality and supply stability, and improve economic benefits and operation efficiency.
[0019] 2. By comprehensively evaluating the future changes of fruits by combining fruit surface data sets, physiological data sets, and environmental data sets, the present invention can break through the limitations of traditional appearance detection, accurately insight into internal defects and potential quality deterioration, predict the development trend of defects in advance, and achieve early warning. It can not only ensure food safety, reduce losses, and improve economic benefits, but also promote the fine regulation of agricultural production and intelligent decision-making in the industry, and optimize supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION
[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0022] Please refer to Figure 1 , an intelligent method for identifying fruit defects, comprising the following steps: Step 1, sensor arrangement: Arrange multimodal sensors in the fruit storage area to monitor the fruits; Arranging multimodal sensors in the fruit storage area can accurately monitor environmental parameters such as temperature, humidity, and gas composition in real time, as well as the state of the fruits themselves, which helps to timely adjust the environment to extend the shelf life, discover and process spoiled fruits in advance, optimize inventory management, reduce losses, ensure fruit quality and supply stability, and improve economic benefits and operation efficiency; Step 2, data acquisition: Acquire and save the monitoring data of the sensor monitor with numbers. The monitoring data includes: fruit surface data set , fruit physiological data set , and environmental data set ; The number-saving expression of the fruit surface data set is: . In the expression, represents the first fruit surface data in the fruit surface data set , represents the th fruit surface data in the fruit surface data set , represents the total number of fruit surface data obtained. The fruit surface data includes: fruit surface color change data, damage data, mildew data, and abnormal texture; Obtaining data such as fruit surface color change, damage, mildew, and abnormal texture can accurately and timely grasp the quality changes and damage conditions of fruits, so as to quickly take measures such as grading and picking to avoid problem fruits affecting the overall storage and sales. At the same time, it provides a basis for adjusting the storage environment and optimizing the preservation strategy, effectively reducing losses, and ensuring fruit supply quality and economic benefits; The fruit physiological data set , in the expression, represents the first fruit physiological data in the fruit physiological data set , represents the th fruit physiological data in the fruit physiological data set , Represents the total number of fruit physiological data acquisitions. Fruit physiological data includes: fruit weight, fruit volatile organic compound content; Obtaining fruit weight data can accurately control its water loss, maturity change and loss situation. Monitoring the content of fruit volatile organic compounds can insight into the fruit's ripening process, flavor quality and respiration intensity. The combination of the two helps to scientifically regulate the storage environment, predict the best sales time of fruits in advance, ensure the quality stability of fruits during storage and transportation, reduce economic losses caused by over-ripening or spoilage, and enhance the efficiency and competitiveness of the fruit industry; Environmental data set , in the expression, represents the first environmental data in the environmental data set and represents the th environmental data in the environmental data set . Represents the total number of environmental data acquisitions. Environmental data includes: relative temperature, relative humidity, carbon dioxide concentration, light intensity, light time, air circulation index, storage time; Monitoring the relative temperature, humidity, carbon dioxide concentration, light intensity, time, air circulation index and storage time of fruits can accurately regulate the storage environment, maintain suitable temperature, humidity and gas environment, avoid the influence of improper lighting, ensure good ventilation, and timely master the state changes of fruits, so as to extend the preservation period, reduce losses and maintain quality, providing key data support and decision-making basis for the scientific storage, transportation and sales of fruits, and enhancing the efficiency of the fruit industry; Step 3: Data calculation: Calculate the fruit surface defect index and the fruit quality prediction index based on the data saved by the number; The calculation formula of the fruit surface defect index
[0023] is as follows: In the calculation formula, represents the fruit surface defect index, represents the fruit surface color change data in the th fruit surface data, represents the standard fruit surface color change data, represents the weight of the fruit surface color change data, represents the damaged data in the th fruit surface data, represents the standard damaged data of the fruit, represents the weight of the damaged data, represents the mildew data in the Represents the standard mildew data of fruits, Represents the weight of the mildew data, Table No. Abnormal texture data among the surface data of the Represents the standard abnormal texture data of fruits, Represents the weight of the abnormal texture data; Represents the ratio between the fruit surface color change data and the standard data considering the weight factor, measuring the impact of fruit surface color change on fruit surface defects; Represents the ratio between the damage data and the standard data considering the weight factor, measuring the impact of fruit damage data on fruit surface defects; Represents the ratio between the mildew data and the standard data considering the weight factor, measuring the impact of fruit mildew data on fruit surface defects; Represents the ratio between the abnormal texture data and the standard data considering the weight factor, measuring the impact of fruit abnormal texture data on fruit surface defects; Calculate the fruit surface defects by integrating multiple factors to achieve the preliminary screening of fruits and timely detect abnormal situations of fruits; Fruit quality prediction index The calculation formula is:
[0024] In the calculation formula, Represents the fruit quality prediction index, Represents the fruit surface defect index, Represents the initial fruit weight, Represents No. Fruit weight data among the physiological data of the Represents No. Content of volatile organic compounds in the fruit among the physiological data of the Represents the environmental fruit impact value; Environmental fruit impact value The calculation formula is:
[0025] In the calculation formula, Represents the environmental fruit impact value, Represents the impact of relative temperature on fruit quality, Represents the actual relative temperature, Represents the optimal relative temperature, Represents the relative temperature sensitivity coefficient, represents the base of natural numbers; represents the influence of relative humidity on fruit quality, represents the actual relative humidity, represents the optimal relative humidity, represents the humidity sensitivity coefficient; represents the influence of carbon dioxide concentration on fruit quality, represents the actual carbon dioxide concentration, represents the semi-inhibitory concentration; represents the influence of air circulation index on fruit quality, represents the actual air circulation index, represents the optimal air circulation index, represents the maximum allowable air circulation index, represents the storage time; Combined with the fruit surface data set and the fruit physiological data set and the environmental data set Comprehensively evaluate the future changes of fruits, which can break through the limitations of traditional appearance detection, accurately detect internal defects and potential quality deterioration, predict the development trend of defects in advance, and achieve early warning. It can not only ensure food safety, reduce losses, and improve economic benefits, but also promote the refined control of agricultural production and intelligent decision-making in the industry, and optimize supply chain management; Step Four, Comprehensive Evaluation: Based on the fruit surface defect index and the fruit quality prediction index conduct fruit evaluation; When the calculated value of the fruit surface defect index is greater than the threshold of the fruit surface defect index, it represents that the current fruit has defects, and a fruit surface anomaly signal is sent for feedback; When the calculated value of the fruit quality prediction index is greater than the threshold of the fruit quality prediction index, it represents that the environment during the storage time of the current fruit will affect the fruit, and a fruit physiological anomaly signal is sent for feedback; Step Five, Result Feedback: Feedback the fruit evaluation result.
[0026] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. An intelligent method for identifying fruit defects, characterized in that, Including the following steps: Step 1, sensing arrangement: Arrange multi-modal sensors in the fruit storage area to monitor the fruits; Step 2, data acquisition: Acquire the monitoring data of the sensing monitor and save it with numbers; Step 3: Data calculation: Calculate the fruit surface defect index and the fruit quality prediction index based on the saved data with numbers and ; Step 4, Comprehensive Evaluation: Based on the fruit surface defect index and the fruit quality prediction index to conduct fruit evaluation; Step 5, result feedback: Feedback the fruit evaluation results.
2. The intelligent fruit defect recognition method according to claim 1, wherein: In the second step, the monitoring data of the sensor monitor is acquired and numbered for storage. The monitoring data includes: the fruit surface data set , the fruit physiological data set , and the environmental data set .
3. The intelligent fruit defect recognition method according to claim 2, wherein: The surface dataset of the fruit is saved with the following expression: In the expression, represents the first fruit surface data in the fruit surface dataset , represents the th fruit surface data in the fruit surface dataset represents the total number of fruit surface data obtained. The fruit surface data includes: fruit surface color change data, damage data, mildew data, and abnormal texture.
4. The intelligent fruit defect recognition method according to claim 3, wherein: The fruit physiological data set , in the expression, represents the first fruit physiological data in the fruit physiological data set , represents the th fruit physiological data in the fruit physiological data set , represents the total number of fruit physiological data obtained, and the fruit physiological data includes: fruit weight, fruit volatile organic compound content.
5. The intelligent fruit defect recognition method according to claim 4, wherein: The environmental data set , in the expression, represents the first environmental data in the environmental data set , represents the environmental data set . The th environmental data in represents the total number of acquired environmental data, and the environmental data includes: relative temperature, relative humidity, carbon dioxide concentration, light intensity, light duration, air circulation index, storage time.
6. The intelligent fruit defect recognition method according to claim 5, characterized in that: The surface defect index of the fruit The calculation formula is as follows: In the calculation formula, represents the fruit surface defect index, represents the fruit surface color change data among the th fruit surface data, represents the standard fruit color change data, represents the weight of the fruit surface color change data, represents the damage data among the th fruit surface data, represents the standard fruit damage data, represents the weight of the damage data, represents the mildew data among the th fruit surface data, represents the standard fruit mildew data, represents the weight of the mildew data, represents the abnormal texture data among the th fruit surface data, represents the standard fruit abnormal texture data, represents the weight of the abnormal texture data.
7. The intelligent fruit defect recognition method according to claim 6, wherein: The predicted fruit quality index is calculated as follows: In the calculation formula, represents the fruit quality prediction index, Represents the fruit surface defect index, represents the initial fruit weight, Representative Fruit weight data in the fruit physiological data, Representative The content of volatile organic compounds in fruits in the physiological data of fruits, Represents the environmental fruit impact value.
8. The intelligent fruit defect recognition method according to claim 7, wherein: The environmental fruit impact value The calculation formula is as follows: In the calculation formula, represents the environmental fruit impact value, represents the impact of relative temperature on fruit quality, represents the actual relative temperature, represents the optimal relative temperature, represents the relative temperature sensitivity coefficient, represents the base of natural numbers; Represents the influence of relative humidity on fruit quality, Represents the actual relative humidity, Represents the optimal relative humidity, Represents the humidity sensitivity coefficient; Representing the effect of carbon dioxide concentration on fruit quality, Representing the actual carbon dioxide concentration, Representing the semi-inhibitory concentration; Represents the influence of the air circulation index on the fruit quality, Represents the actual air circulation index, Represents the optimal air circulation index, Represents the maximum allowable air circulation index, Represents the storage time.
9. The intelligent recognition method for fruit defects according to claim 8, wherein: When the calculated value of the fruit surface defect index is greater than the fruit surface defect index threshold, it indicates that the current fruit has defects, and a fruit surface abnormality signal is sent for feedback.
10. A method for intelligent recognition of fruit defects according to claim 9, characterized in that: When the calculated value of the fruit quality prediction index is greater than the fruit quality prediction index threshold, it indicates that the environment during the current fruit storage time will affect the fruit, and a fruit physiological abnormality signal is sent for feedback.