Intelligent papaya processing system and method based on AI vision and composite drying technology
By combining an AI visual recognition system with a composite drying process, and utilizing deep learning models and adjustable temperature control equipment, the problems of papaya surface damage, irregular shape recognition errors, and unstable drying were solved, achieving efficient, precise, and stable intelligent processing of papaya.
Patent Information
- Application Number
- CN202510972587.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-15
- Publication Date
- 2025-10-31
AI Technical Summary
Existing intelligent papaya processing systems are prone to misidentification when faced with papaya surface damage or irregular shapes, leading to defective products entering the processing flow. Furthermore, the composite drying process is unstable when humidity or temperature is unsuitable.
An AI visual recognition system combined with a deep learning model is used. Through a hybrid algorithm of convolutional neural networks and long short-term memory networks, the surface features of papaya are identified. Combined with an adjustable temperature-controlled blower oven and drying area, the humidity and temperature during the drying process are dynamically optimized and controlled.
This improves the accuracy of papaya identification and the stability of drying effects, ensuring that defective products are automatically screened, and that papayas are dried evenly and consistently, thereby enhancing processing precision and product quality.
Smart Images
Figure CN120868752A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent papaya processing systems, specifically to an intelligent papaya processing system and method based on AI vision and composite drying technology. Background Technology
[0002] The intelligent papaya processing system based on AI vision and composite drying technology mainly comprises three key components: an AI vision recognition system, a composite drying system, and an automated cutting and packaging system. First, the AI vision recognition system intelligently classifies papayas to ensure their quality and size meet processing standards. This system uses high-definition cameras and deep learning algorithms for image analysis, automatically identifying the ripeness and appearance defects of the papayas to avoid human error. Second, the composite drying system combines oven drying and sun drying processes. It uses an adjustable temperature-controlled forced-air oven for initial drying, combined with sun drying, ensuring the papayas are processed under suitable temperature and humidity to prevent over-drying or mold growth. Finally, the automated cutting and packaging system automatically cuts the dried papayas into thin slices and packages them, effectively reducing manual operation and improving processing efficiency and product consistency.
[0003] While the system offers significant advantages in improving papaya processing efficiency and quality, it still has some drawbacks. First, the AI vision system may misidentify papayas with varying degrees of surface damage or irregular shapes, leading to substandard papayas entering the processing flow. Although the composite drying process improves drying efficiency, excessively high humidity or low temperature can affect the sun-dried portions, resulting in inconsistent drying outcomes. Finally, the automated cutting system may not adapt to the natural shape variations of papayas, potentially wasting some product or affecting the aesthetic appearance of the finished product. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides an intelligent papaya processing system and method based on AI vision and composite drying technology. This solves the problem that when faced with papayas with varying degrees of surface damage or irregular shapes, recognition errors can easily occur, leading to substandard papayas entering the processing flow. Furthermore, when the humidity is too high or the temperature is too low, the parts that rely on sun drying may be affected, resulting in unstable drying effects.
[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent papaya processing system and method based on AI vision and composite drying technology, comprising:
[0006] The AI visual recognition system is used to automatically inspect papayas and identify their ripeness, appearance defects, and surface damage.
[0007] The composite drying system includes a blower oven and a sun-drying area, used to combine drying and sun-drying processes for drying papaya;
[0008] An automated cutting and packaging system is used to cut dried papayas into thin slices and package them.
[0009] The AI visual recognition system uses a deep learning model to identify the surface features of papayas and combines them with preset recognition rules to judge whether the papaya surface is damaged or has an irregular shape, thereby improving the recognition accuracy.
[0010] Preferably, the AI visual recognition system is trained using a convolutional neural network algorithm, and the trained model is optimized using a papaya image dataset containing different damage types and shapes to enhance the system's ability to recognize different degrees of damage on the papaya surface.
[0011] Preferably, the composite drying system includes:
[0012] An adjustable temperature-controlled forced-air drying oven is used for the initial drying of papayas, controlling the drying temperature within the range of 50℃ to 80℃;
[0013] The sun-drying area is used to combine sun drying with controlled humidity and temperature to ensure that the papayas are dried to the required moisture content.
[0014] Preferably, the AI visual recognition system includes a camera, an image processing unit, and a deep learning model. It can classify papayas based on real-time image data, automatically screening out substandard papayas to prevent them from entering subsequent processing steps. The deep learning model employs a hybrid model combining multi-layer convolutional neural networks and long short-term memory networks. It dynamically optimizes and controls the drying and cutting processes by processing papaya images and environmental data in real time. The deep learning model is optimized using the following formula:
[0015]
[0016] Where y(t) is the optimized output at the current time, and w i The weights for each input feature, x i (t) represents the papaya image features and environmental data at the current moment, and b represents the bias term. By combining the temporal prediction model of the Long Short-Term Memory Network, the humidity and temperature control during the drying process are optimized to achieve a stable drying effect over a long period of time.
[0017] A method for intelligently processing papaya based on AI vision and composite drying technology includes the following steps:
[0018] a. Papaya harvesting and cleaning: After the papayas are harvested from the plantation, they are cleaned to remove surface dirt and impurities;
[0019] b. Papaya appearance inspection: The appearance of the papaya is detected using an AI visual recognition system and compared with the training dataset to identify the ripeness, surface damage and irregular shape of the papaya.
[0020] c. Papaya classification: Based on the test results in step b, the papayas are divided into two categories: qualified and unqualified. Unqualified papayas are automatically removed.
[0021] d. Blanching: Place qualified papayas into boiling water and blanch for 2 to 7 minutes, until the peel turns grayish-white and the flesh is cooked.
[0022] e. Preliminary drying: Place the scalded papaya in a forced-air drying oven and set the temperature to 50℃ to 80℃ for preliminary drying until the moisture content of the papaya is 40% to 50%.
[0023] f. Sun-drying treatment: Place the pre-dried papaya in a sun-drying area and sun-dry it, controlling the ambient temperature and humidity to ensure that the papaya is dried until the moisture content does not exceed 15%.
[0024] g. Slicing: After cooling the dried papaya, slice it into 2mm thin slices;
[0025] h. Finished product packaging: The cut papaya slices are packaged and sealed to ensure drying effect and stable quality.
[0026] Preferably, the drying temperature of the blower oven is 70°C to 80°C, and the drying time is 30 to 60 minutes, until the papaya reaches the preset degree of dryness.
[0027] Preferably, the drying area includes an adjustable temperature-controlled drying platform equipped with temperature and humidity sensors to monitor environmental conditions in real time and automatically adjust the drying time and intensity based on sensor data to ensure uniform drying of the papaya.
[0028] Preferably, the training dataset includes cracks, brown spots, and insect damage, and data augmentation techniques are used during the training process to further improve the system's ability to recognize irregular shapes on the surface of papaya.
[0029] This invention provides an intelligent papaya processing system and method based on AI vision and a composite drying process. It has the following beneficial effects:
[0030] This intelligent papaya processing system and method, based on AI vision and a composite drying process, employs a deep learning model combined with a hybrid algorithm of convolutional neural networks and long short-term memory networks. By training on a dataset of papaya images containing different damage types and shapes, the system's ability to identify surface damage on papayas is significantly enhanced. This technology ensures the automatic screening of defective papayas during processing, preventing them from entering subsequent processes and thus improving processing accuracy and product quality.
[0031] The application of a composite drying process significantly improves the stability of papaya drying, especially in environments with excessively high humidity or low temperature. By combining the processes of a forced-air drying oven and a sun-drying area, the drying temperature and humidity conditions are rationally adjusted to ensure that the papayas are dried evenly to the required moisture content. Furthermore, the sun-drying area is equipped with an adjustable temperature-controlled drying platform and uses temperature and humidity sensors for real-time monitoring, making the papaya drying process more precise and avoiding uneven drying caused by environmental changes. Attached Figure Description
[0032] Figure 1 This is a sliced papaya from Embodiment 1 of the present invention;
[0033] Figure 2 This is a sliced papaya from Embodiment 2 of the present invention;
[0034] Figure 3 This is a schematic diagram of the stone cell structure in Embodiment 1 of the present invention;
[0035] Figure 4 This is a schematic diagram of the sclereid structure in Embodiment 2 of the present invention;
[0036] Figure 5 This is a schematic diagram of the exocarp cells in Embodiment 1 of the present invention;
[0037] Figure 6 This is a schematic diagram of the exocarp cells in Embodiment 2 of the present invention;
[0038] Figure 7 This is a schematic diagram of pericarp thin-walled cells in Embodiment 1 of the present invention;
[0039] Figure 8 This is a schematic diagram of pericarp thin-walled cells in Embodiment 2 of the present invention.
[0040] Figure 9 This is a UV irradiation diagram of Embodiment 1 of the present invention;
[0041] Figure 10 This is a UV irradiation diagram of Embodiment 2 of the present invention. Detailed Implementation
[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0043] Example 1
[0044] like Figure 1-10 As shown, this invention provides an intelligent papaya processing system and method based on AI vision and composite drying technology. The system includes an AI vision recognition system for automated papaya detection, identifying ripeness, appearance defects, and surface damage. The AI vision recognition system comprises a camera, an image processing unit, and a deep learning model. It can classify papayas based on real-time image data, automatically filtering out substandard papayas to prevent them from entering subsequent processing steps. The deep learning model employs a hybrid model combining multi-layer convolutional neural networks and long short-term memory networks. It dynamically optimizes and controls the drying and cutting processes by processing papaya images and environmental data in real time. The deep learning model is optimized using the following formula:
[0045]
[0046] Where y(t) is the optimized output at the current time, and w i The weights for each input feature, x i (t) represents the papaya image features and environmental data at the current moment, and b represents the bias term. By combining the temporal prediction model of the long short-term memory network, the humidity and temperature control during the drying process are optimized to achieve a stable drying effect over a long period of time.
[0047] A composite drying system, comprising a forced-air drying oven and a sun-drying area, is used to combine drying and sun-drying processes for drying papaya. The composite drying system includes:
[0048] An adjustable temperature-controlled forced-air drying oven is used for the initial drying of papayas, controlling the drying temperature within the range of 50℃ to 80℃;
[0049] The drying area is used to combine sun drying with humidity and temperature control during the drying process to ensure that the papaya is dried to the required moisture content.
[0050] An automated cutting and packaging system is used to cut dried papayas into thin slices and package them.
[0051] The AI visual recognition system uses a deep learning model to identify papaya surface features and combines preset recognition rules to judge papaya surface damage or irregular shapes in order to improve recognition accuracy. The AI visual recognition system is trained using a convolutional neural network algorithm, and the trained model is optimized using a papaya image dataset containing different damage types and shapes to enhance the system's ability to recognize different degrees of damage to the papaya surface.
[0052] A method for intelligently processing papaya based on AI vision and composite drying technology includes the following steps:
[0053] a. Papaya harvesting and cleaning: After the papayas are harvested from the plantation, they are cleaned to remove surface dirt and impurities;
[0054] b. Papaya appearance inspection: The appearance of the papaya is detected using an AI visual recognition system and compared with the training dataset to identify the ripeness, surface damage and irregular shape of the papaya. The training dataset includes cracks, brown spots and insect damage. Data augmentation technology is used during the training process to further improve the system's ability to recognize irregular shapes on the surface of the papaya.
[0055] c. Papaya classification: Based on the test results in step b, the papayas are divided into two categories: qualified and unqualified. Unqualified papayas are automatically removed.
[0056] d. Blanching: Place the qualified papayas into boiling water and blanch for 2 minutes, until the peel turns grayish-white and the flesh is cooked.
[0057] e. Preliminary drying: Place the scalded papaya in a forced-air drying oven, set the temperature to 50℃, and perform preliminary drying until the moisture content of the papaya is 40%. The drying temperature of the forced-air drying oven is 70℃, and the drying time is 30 minutes, until the papaya reaches the preset degree of dryness.
[0058] f. Sun-drying treatment: Place the pre-dried papaya in a sun-drying area and sun-dry it in the sun. Control the ambient temperature and humidity to ensure that the papaya is dried to a moisture content of no more than 15%. The sun-drying area includes an adjustable temperature-controlled sun-drying platform. The sun-drying platform is equipped with temperature and humidity sensors to monitor the environmental conditions in real time and automatically adjust the sun-drying time and drying intensity according to the sensor data to ensure that the papaya is dried evenly.
[0059] g. Slicing: After cooling the dried papaya, slice it into 2mm thin slices;
[0060] h. Finished product packaging: The cut papaya slices are packaged and sealed to ensure drying effect and stable quality.
[0061] Example 2:
[0062] like Figure 1-10 As shown, an intelligent papaya processing method based on AI vision and composite drying technology includes the following steps:
[0063] a. Papaya harvesting and cleaning: After the papayas are harvested from the plantation, they are cleaned to remove surface dirt and impurities;
[0064] b. Papaya appearance inspection: The appearance of the papaya is detected using an AI visual recognition system and compared with the training dataset to identify the ripeness, surface damage and irregular shape of the papaya. The training dataset includes cracks, brown spots and insect damage. Data augmentation technology is used during the training process to further improve the system's ability to recognize irregular shapes on the surface of the papaya.
[0065] c. Papaya classification: Based on the test results in step b, the papayas are divided into two categories: qualified and unqualified. Unqualified papayas are automatically removed.
[0066] d. Blanching: Place the qualified papayas into boiling water and blanch for 5 minutes, until the peel turns grayish-white and the flesh is cooked.
[0067] e. Preliminary drying: Place the scalded papaya in a forced-air drying oven, set the temperature to 80℃, and perform preliminary drying until the moisture content of the papaya is 50%. The drying temperature of the forced-air drying oven is 80℃, and the drying time is 60 minutes, until the papaya reaches the preset degree of dryness.
[0068] f. Sun-drying treatment: Place the pre-dried papaya in a sun-drying area and sun-dry it in the sun. Control the ambient temperature and humidity to ensure that the papaya is dried to a moisture content of no more than 15%. The sun-drying area includes an adjustable temperature-controlled sun-drying platform. The sun-drying platform is equipped with temperature and humidity sensors to monitor the environmental conditions in real time and automatically adjust the sun-drying time and drying intensity according to the sensor data to ensure that the papaya is dried evenly.
[0069] g. Slicing: After cooling the dried papaya, slice it into 2mm thin slices;
[0070] h. Finished product packaging: The cut papaya slices are packaged and sealed to ensure drying effect and stable quality.
[0071] The thin-layer chromatography method (General Chapter 0502, Part IV, Chinese Pharmacopoeia 2020 Edition) was used for the test. Referring to the Chinese Pharmacopoeia 2020 Edition, and using papaya reference material and ursolic acid reference standard as controls, 10 batches of freshly cut papaya medicinal material samples and 10 batches of traditional papaya slices samples were tested. The results showed that there was no significant difference between the thin-layer chromatography of freshly cut papaya medicinal material and traditional papaya slices.
[0072] The yield, moisture, total ash, acidity, extract and content are shown in the table below:
[0073] Sample number Yield (%) Moisture (%) Total ash content (%) acidity Extract (%) content(%) Example 1 13.5 11.7 2.7 3.05 38.9 0.54 Comparative Example 1 15.5 11.1 2.6 3.05 37.1 0.53
[0074] Freshly cut papaya samples had a higher yield, and the extract and content indicators were higher than those of traditionally processed papaya samples.
[0075] Comparing Experiment 1 and Experiment 2, the results show that the papaya processing technology of the present invention is superior to the traditional processing method, and is simpler to operate, more efficient, and produces higher quality papaya medicinal materials.
[0076] Process verification: Three batches of papaya samples were prepared according to the operating steps of Example 1, and their properties, identification, impurities, total ash, acidity, extract and content were compared.
[0077] The test results for papaya are shown in the table below:
[0078]
[0079]
[0080] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A papaya intelligent processing system based on AI vision and composite drying technology, characterized in that, include: The AI visual recognition system is used to automatically inspect papayas and identify their ripeness, appearance defects, and surface damage. The composite drying system includes a blower oven and a sun-drying area, used to combine drying and sun-drying processes for drying papaya; An automated cutting and packaging system is used to cut dried papayas into thin slices and package them. The AI visual recognition system uses a deep learning model to identify the surface features of papayas and, in conjunction with preset recognition rules, makes judgments on papaya surface damage or irregular shapes.
2. The intelligent papaya processing system based on AI vision and composite drying technology according to claim 1, characterized in that: The AI visual recognition system is trained using a convolutional neural network algorithm, and the trained model is optimized using a papaya image dataset containing different damage types and shapes.
3. The intelligent papaya processing system based on AI vision and composite drying technology according to claim 1, characterized in that: The composite drying system includes: The temperature-controlled blower oven controls the drying temperature within the range of 50℃ to 80℃; The sun-drying area is used to combine sun drying with controlled humidity and temperature to ensure that the papayas are dried to the required moisture content.
4. The intelligent papaya processing system based on AI vision and composite drying technology according to claim 1, characterized in that: The AI visual recognition system includes a camera, an image processing unit, and a deep learning model. The deep learning model employs a hybrid model combining multi-layer convolutional neural networks and long short-term memory networks. It dynamically optimizes and controls the drying and cutting processes by processing papaya images and environmental data in real time. The deep learning model is optimized using the following formula: Where y(t) is the optimized output at the current time, and w i The weights for each input feature, x i (t) represents the papaya image features and environmental data at the current moment, and b represents the bias term.
5. A method for intelligently processing papaya based on AI vision and composite drying technology, characterized in that: Includes the following steps: a. Papaya harvesting and cleaning: After the papayas are harvested from the plantation, they are cleaned to remove surface dirt and impurities; b. Papaya appearance inspection: An AI visual recognition system is used to inspect the appearance of the papaya and compare it with the training... The training dataset is compared to identify the ripeness, surface damage, and irregular shape of papayas; c. Papaya classification: Based on the test results in step b, the papayas are divided into two categories: qualified and unqualified. Unqualified papayas are automatically removed. d. Blanching: Place qualified papayas into boiling water and blanch for 2 to 7 minutes, until the peel turns grayish-white and the flesh is cooked. e. Preliminary drying: Place the scalded papaya in a forced-air drying oven and set the temperature to 50℃ to 80℃ for preliminary drying until the moisture content of the papaya is 40% to 50%. f. Sun-drying treatment: Place the pre-dried papaya in a sun-drying area and sun-dry it, controlling the ambient temperature and humidity to ensure that the papaya is dried until the moisture content does not exceed 15%. g. Slicing: After cooling the dried papaya, slice it into 2mm thin slices; h. Finished product packaging: The cut papaya slices are packaged and sealed to ensure drying effect and stable quality.
6. The intelligent papaya processing method based on AI vision and composite drying technology according to claim 5, characterized in that: The drying temperature of the blower oven is 70°C to 80°C, and the drying time is 30 to 60 minutes, until the papaya reaches the preset degree of dryness.
7. The intelligent processing method for papaya based on AI vision and composite drying technology according to claim 5, characterized in that: The drying area includes a temperature-controlled drying platform equipped with temperature and humidity sensors.
8. The intelligent processing method for papaya based on AI vision and composite drying technology according to claim 5, characterized in that: The training dataset includes cracks, brown spots, and insect damage.