Description:.

SYSTEM AND METHOD FOR ASSESSING THE QUALITY OF A COMMODITY

Field of the Invention

The present disclosure generally relates to quality assessment systems. Particularly, the present disclosure relates to a system for assessing the quality of a commodity using image capture and analysis.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
In recent years, the assessment of commodity quality has become increasingly reliant on digital technologies. Traditional methods of quality evaluation, often manual and subjective, have gradually been supplemented or replaced by more objective, technology-driven approaches. Among these, the use of imaging and machine learning technologies stands out due to its potential for accuracy, consistency, and efficiency.
Quality assessment involves evaluating various characteristics of a commodity, such as its physical appearance, which includes color, texture, and shape, as well as the presence of defects. Traditionally, this evaluation has been performed manually by human experts, which, while effective, is inherently subjective and can vary significantly from one assessor to another. Furthermore, manual assessments are time-consuming and can be impractical for large volumes of commodities.
The advent of digital imaging technologies offered a new avenue for quality assessment. By capturing images of commodities, it became possible to evaluate them more consistently and rapidly. However, early attempts at automating quality assessment through digital images faced several challenges. Firstly, the quality of images captured, especially with varying lighting conditions and perspectives, often lacked standardization. Image noise, instability, and inconsistencies in lighting could significantly affect the accuracy of subsequent analyses.
To address these challenges, advancements in image processing algorithms were developed. Techniques such as noise reduction, image stabilization, and lighting adjustment have significantly improved the quality of images, making them more suitable for analysis. Nonetheless, processing images to a consistent standard remains a complex task that requires sophisticated algorithms and computational resources.
The evolution of computer vision algorithms has further enhanced the capability to extract relevant features from images for quality assessment. By identifying specific attributes such as color, texture, shape, and defects, these algorithms have enabled a more detailed and objective evaluation of commodities. However, the effectiveness of feature extraction is heavily dependent on the quality of the underlying images and the precision of the algorithms used.
Machine learning, particularly with the use of supervised learning techniques, has introduced a new dimension to quality assessment. By analyzing extracted features with trained models, it is possible to classify commodities into quality categories with a high degree of accuracy. The training of these models, however, requires a substantial dataset of labeled examples, and the models must be continuously updated to maintain their accuracy over time.
The integration of these technologies into a cohesive system presents its own set of challenges. Ensuring seamless operation between modules, such as image capture, processing, feature extraction, and analysis, is critical. Moreover, providing feedback and information to users in a meaningful and actionable manner requires a well-designed user interface. Despite these advancements, the quest for a more efficient, accurate, and user-friendly system for commodity quality assessment continues.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for assessing commodity quality.

Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
The present disclosure relates to a system designed for the assessment of commodity quality, incorporating a comprehensive suite of modules that work in tandem to capture, process, analyze, and display information about the quality of commodities. This system aims to revolutionize the way commodity quality is assessed by leveraging the capabilities of modern mobile devices and advanced computational algorithms.
In an embodiment, the system comprises an image capture module that harnesses the built-in camera of a mobile device. This module is adept at capturing multiple images of a commodity from varied angles and perspectives, ensuring a thorough visual examination. The utility of the mobile device's camera for such a critical application underscores the system's adaptability and ease of use in real-world scenarios.
In an embodiment, an image processing module is incorporated, which applies a series of sophisticated image enhancement algorithms on the captured images. These algorithms are designed to address common issues such as noise, instability, and varying lighting conditions, thereby standardizing the image quality across different environments. This module serves as a crucial step in preparing the images for further analysis by improving their clarity and consistency.
In an embodiment, the system features a feature extraction module. This module employs cutting-edge computer vision algorithms to meticulously extract relevant features from the processed images. The features of interest include but are not limited to color, texture, shape, and the presence of defects, which are pivotal in determining the quality of the commodity.
In an embodiment, a machine learning analysis module is integrated into the system. This module utilizes trained machine learning models to analyze the extracted features, employing supervised learning techniques to accurately classify the commodity into predefined quality categories. The incorporation of machine learning offers a significant advancement in automating and enhancing the accuracy of quality assessments.
In an embodiment, the system is equipped with a user interface module. This module is responsible for displaying a quality score and detailed analysis results to the user via a mobile application interface. It offers real-time feedback and additional information about the commodity's quality, facilitating informed decision-making.
In an embodiment, the image processing module further includes functionalities for color correction and dynamic range adjustment. This enhancement allows the module to adapt to varying lighting conditions during image capture, ensuring that the image quality is not compromised by environmental factors.
In an embodiment, the feature extraction module is enhanced with algorithms specifically optimized for the commodity in question. These algorithms include edge detection, color histogram analysis, and texture analysis, allowing for a more tailored and accurate feature extraction process.
In an embodiment, the machine learning analysis module is described as including models that have been trained on a dataset comprising labeled examples of high and low-quality commodities. This training facilitates the module's ability to perform accurate quality classification, leveraging the insights gained from a comprehensive dataset.
In an embodiment, the user interface module is designed to provide recommendations for alternative products or suggestions for improving the quality of the commodity. This feature adds a layer of value to the analysis results, offering practical advice and options to the user based on the assessed quality.
In an embodiment, the machine learning analysis module employs convolutional neural networks (CNNs) for the analysis and classification of commodity quality. CNNs are particularly suited for image-based analysis, offering enhanced accuracy in the identification and classification of features relevant to quality assessment.
In an embodiment, the image capture module is configured to guide the user in capturing images that cover the necessary angles and perspectives for comprehensive analysis. This guidance is facilitated through visual cues on the mobile device screen, ensuring that the images captured are of sufficient quality and coverage for accurate analysis.
In an embodiment, the user interface module is designed to collect user feedback on the quality assessment's accuracy. This feedback is utilized to continuously refine and improve the machine learning analysis module, ensuring that the system evolves and enhances its performance over time.
The method for assessing the quality of a commodity using the described system comprises several steps, beginning with the capture of multiple images using the image capture module. These images are then processed by the image processing module to standardize their quality. Relevant features are extracted from the processed images by the feature extraction module, followed by the analysis of these features by the machine learning analysis module to classify the commodity into quality categories. Finally, the quality score and additional analysis results are displayed to the user through the user interface module, providing comprehensive feedback and information.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a system for the assessment of the quality of various commodities, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method for assessing the quality of a commodity using a system, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates a user flow diagram for assessing the quality of a commodity using a system, in accordance with the embodiments of the present disclosure.
FIG. 4 illustrates a process flow diagram for assessing the quality of a commodity using a system, in accordance with the embodiments of the present disclosure.
Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
FIG. 1 illustrates a system (100) for the assessment of the quality of various commodities, in accordance with the embodiments of the present disclosure. This system is meticulously designed to leverage the capabilities of contemporary mobile devices, employing a combination of advanced imaging, processing, and analytical technologies to provide a robust solution for determining commodity quality.
The term "image capture module" as used throughout the present disclosure relates to a component, identified as 102, that is adept at utilizing the built-in camera of a mobile device. The primary function of this module is to capture multiple images of the commodity from a diverse range of angles and perspectives. This functionality is crucial for ensuring a comprehensive visual inspection of the commodity, enabling the detection of quality-defining characteristics that may be visible only from certain viewpoints. Optionally, the image capture module includes an interface to guide the user in capturing images that adequately represent the commodity's physical attributes. An exemplary operation of the image capture module involves providing on-screen instructions or visual aids to the user, ensuring that images are taken at optimal angles and lighting conditions.
The term "image processing module" as used throughout the present disclosure refers to a component, designated as 104, tasked with applying a series of image enhancement algorithms on the captured images. These algorithms are specifically selected to address common image quality issues such as noise, instability due to hand movement, and variations in lighting conditions. The algorithms include, but are not limited to, noise reduction, image stabilization, and lighting adjustment. By standardizing image quality, the image processing module ensures that the images are in a suitable state for further analysis, thereby eliminating or significantly reducing the impact of environmental and operational variables on the assessment process. An example of the operation of the image processing module is the application of a stabilization algorithm to images that appear blurred due to minor movements during capture, ensuring that details are preserved for accurate feature extraction.
The term "feature extraction module" as used in the present disclosure is identified as 106 and is responsible for employing computer vision algorithms to extract relevant features from the processed images. These features encompass aspects such as color, texture, shape, and the presence of defects, which are instrumental in determining the quality of the commodity. The module utilizes sophisticated computer vision techniques to accurately identify and quantify these features, providing a detailed characterization of the commodity that forms the basis for quality assessment. An operating example of the feature extraction module involves analyzing the texture of a fabric to identify and quantify defects such as tears or frays, contributing to the assessment of the fabric's quality.
The term "machine learning analysis module" as used throughout the present disclosure, denoted as 108, is configured to analyze the features extracted by the feature extraction module using trained machine learning models. These models, which employ supervised learning techniques, are trained to classify the commodity into predefined quality categories based on the extracted features. The module's ability to classify commodities into quality categories with a high degree of accuracy is a cornerstone of the system, offering a scalable and efficient solution for quality assessment across a wide range of commodities. An illustrative example of the machine learning analysis module's function is the classification of fruits into quality categories based on color uniformity, size, and defect presence, utilizing a dataset of labeled examples to train the models.
The term "user interface module" as used in the present disclosure is referred to as 110 and is designed to display the quality score and analysis results to the user through a mobile application interface. This module offers real-time feedback and additional information regarding the commodity's quality, facilitating informed decision-making. The user interface module is the conduit through which the results of the quality assessment are communicated to the user, ensuring clarity and accessibility of information. An operational example of the user interface module involves presenting the user with a detailed report on the commodity's quality, including visual representations of defects detected and a numerical quality score, alongside recommendations for improvement or suggestions for alternative products.
In an embodiment, said image processing module (104) is equipped with functionalities for color correction and dynamic range adjustment. Such functionalities are introduced to adapt to varying lighting conditions encountered during the capture of images. By incorporating these capabilities, a standardized image quality is maintained irrespective of environmental lighting variations. The application of color correction serves to neutralize color biases and restore the original hues of the commodity, while dynamic range adjustment optimizes the contrast and brightness of images. This enhancement of the image processing module (104) plays a significant role in preparing images for subsequent analysis, by improving the consistency and reliability of the image data that is processed and analyzed.
In an embodiment, said feature extraction module (106) incorporates algorithms specifically optimized for the commodity in question. These algorithms include edge detection, color histogram analysis, and texture analysis. By employing these algorithms, detailed features of the commodity such as edges, color distribution, and textural patterns are accurately extracted. The precision of these algorithms enables the detection of minute and critical features relevant to the quality assessment of the commodity. The optimization of algorithms for specific commodities ensures that the feature extraction module (106) can adapt to and effectively analyze a wide range of commodities, enhancing the system's versatility and applicability across different sectors.
In an embodiment, said machine learning analysis module (108) comprises models that have been trained on a dataset containing labeled examples of commodities classified into high and low-quality categories. The utilization of such a dataset for training enables the machine learning models to develop a nuanced understanding of the quality indicators for the commodities. Through supervised learning techniques, the models learn to associate specific patterns and features extracted from the commodity images with the corresponding quality categories. This training process equips the machine learning analysis module (108) with the capability to accurately classify commodities based on their quality, facilitating a data-driven approach to quality assessment.
In an embodiment, said user interface module (110) is configured to provide recommendations for alternative products or suggestions for improving the quality of the assessed commodity. Such recommendations are generated based on the analysis results and are displayed to the user through a mobile application interface. The provision of actionable insights and alternatives adds a significant value to the quality assessment process, enabling users to make informed decisions regarding the commodity. Furthermore, the user interface module (110) enhances user engagement by offering practical solutions and suggestions, thereby extending the functionality of the system beyond mere quality assessment.
In an embodiment, said machine learning analysis module (108) employs convolutional neural networks (CNNs) for the analysis and classification of the commodity's quality. CNNs are particularly effective in processing and analyzing image data, making them well-suited for this application. The use of CNNs allows for the extraction of complex patterns and features from images, which are instrumental in determining the quality of the commodity. The deployment of convolutional neural networks in the machine learning analysis module (108) marks a significant advancement in the system's analytical capabilities, enabling a more accurate and nuanced analysis of commodity quality.
In an embodiment, said image capture module (102) is designed to guide the user in capturing images that cover all necessary angles and perspectives for a comprehensive analysis. Visual cues displayed on the mobile device screen instruct users on how to position the camera to capture the commodity from optimal angles. This guidance ensures that the images captured encompass all relevant aspects of the commodity, providing a complete visual dataset for analysis. The configurability of the image capture module (102) to provide such guidance is instrumental in enhancing the quality and completeness of the image data collected, thereby supporting a thorough assessment of the commodity's quality.
In an embodiment, said user interface module (110) is structured to collect user feedback on the accuracy of the quality assessment. The feedback collected is then utilized to continuously refine and improve the accuracy of the machine learning analysis module (108). By incorporating user feedback into the system, a mechanism for iterative improvement is established, allowing the system to evolve and adapt over time. The collection and application of user feedback not only enhance the precision of the machine learning analysis module (108) but also foster a user-centric approach to quality assessment, ensuring that the system remains aligned with user needs and expectations.
FIG. 2 illustrates a method (200) for assessing the quality of a commodity using a system, in accordance with the embodiments of the present disclosure. At step 202, multiple images of the commodity are captured using the built-in camera of a mobile device. This process is facilitated by the image capture module (102), which guides the user to obtain images from various angles and perspectives. At step 204, the captured images undergo a series of image enhancement algorithms. These algorithms, executed by the image processing module (104), include noise reduction, image stabilization, and lighting adjustment to improve image quality. At step 206, relevant features such as color, texture, shape, and the presence of defects are extracted from the enhanced images. The feature extraction module (106) employs advanced computer vision algorithms for this purpose. At step 208, the extracted features are analyzed to classify the commodity into quality categories. This analysis, performed by the machine learning analysis module (108), uses models trained on labeled examples of commodity quality. At step 210, a quality score and detailed analysis results are displayed to the user. The user interface module (110) presents this information through a mobile application interface, offering real-time feedback and additional information.
FIG. 3 illustrates a user flow diagram for assessing the quality of a commodity using a system, in accordance with the embodiments of the present disclosure. Initially, a user captures multiple images of the commodity using a mobile device (1), leveraging the built-in camera to obtain varied visual data. Subsequently, these images are uploaded to a cloud service (2), where robust image enhancement algorithms are applied. These algorithms, designed to improve the quality of the captured images, address issues such as noise, instability, and inconsistent lighting conditions. Once the images are enhanced, they are processed by an image processing module (3), where computer vision algorithms extract critical features indicative of the commodity's quality, including color, texture, and shape. The extracted features are then analyzed by a machine learning analysis module (4), which employs sophisticated models trained on vast datasets to classify the commodity into predefined quality categories. Finally, the results of this analysis, along with a quality score, are relayed back to the user's mobile device, providing a comprehensive quality assessment that aids in informed decision-making regarding the commodity. This streamlined process embodies an integrated approach, from image capture to quality evaluation, encapsulated within the user-centric workflow of the system.
FIG. 4 illustrates a process flow diagram for assessing the quality of a commodity using a system, in accordance with the embodiments of the present disclosure. The process commences with "Image Acquisition," where images of a commodity are collected, likely through a mobile or dedicated imaging device. Subsequently, these images undergo "Image Processing," a phase in which they are refined through algorithms that might enhance clarity, correct colors, and adjust brightness, to ensure uniformity in quality for analysis. In the "Feature Extraction" stage, distinctive attributes of the commodity, such as shape, color, and textural patterns, are identified from the processed images using advanced computer vision (CV) techniques. These extracted features are then subjected to a "Machine Learning Analysis," where a trained model evaluates the data to categorize the commodity into quality levels. Following this, "Quality Assessment using CV" involves a deeper CV-based evaluation to affirm or further detail the quality classification. Finally, the "User Interface & Feedback" stage presents the outcome of the assessment to the user, typically through an application interface, where the user can also provide feedback, thereby closing the loop for continuous system improvement and personalization. This flow encapsulates a comprehensive and iterative process designed to leverage machine learning and CV for accurate and user-informed commodity quality assessment.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We claim:

A system (100) for assessing the quality of a commodity comprising:
an image capture module (102) configured to utilize a built-in camera of a mobile device to capture multiple images of said commodity from various angles and perspectives;
an image processing module (104) configured to apply a series of image enhancement algorithms on said captured images, said algorithms including noise reduction, image stabilization, and lighting adjustment to standardize image quality;
a feature extraction module (106) configured to employ computer vision algorithms to extract relevant features from said processed images, said features including color, texture, shape, and presence of defects of said commodity;
a machine learning analysis module (108) configured to analyze said extracted features using trained machine learning models, said models employing supervised learning techniques to classify said commodity into quality categories; and
a user interface module (110) configured to display a quality score and analysis results to the user through a mobile application interface, offering real-time feedback and additional information regarding said commodity's quality.
The system of claim 1, wherein said image processing module (104) further comprises color correction and dynamic range adjustment functionalities to adapt to varying lighting conditions during image capture.
The system of claim 1 or 2, wherein said feature extraction module (106) includes algorithms for edge detection, color histogram analysis, and texture analysis specifically optimized for said commodity.
The system of any preceding claim, wherein said machine learning analysis module (108) includes models trained on a dataset comprising labeled examples of high and low-quality commodities to facilitate accurate quality classification.
The system of any preceding claim, wherein said user interface module (110) further provides recommendations for alternative products or suggestions for improving the quality of said commodity based on the analysis results.
The system of any preceding claim, wherein said machine learning analysis module (108) is configured to utilize convolutional neural networks (CNNs) for the analysis and classification of said commodity's quality.
The system of any preceding claim, wherein said image capture module (102) is configured to guide the user in capturing images covering necessary angles and perspectives for comprehensive analysis through visual cues on the mobile device screen.
The system of any preceding claim, wherein said user interface module (110) is configured to collect user feedback on the quality assessment's accuracy, which is used to continuously refine and improve the accuracy of said machine learning analysis module (108).
A method for assessing the quality of a commodity using a system (100) comprising modules for image capture (102), image processing (104), feature extraction (106), machine learning analysis (108), and user interface (110), the method comprising the steps of:
capturing multiple images of said commodity using a built-in camera of a mobile device through an image capture module (102);
applying image enhancement algorithms, including noise reduction, image stabilization, and lighting adjustment, to said captured images using an image processing module (104);
extracting relevant features from said processed images, including aspects related to color, texture, shape, and defects of said commodity, using a feature extraction module (106);
analyzing said extracted features to classify said commodity into quality categories using a machine learning analysis module (108), wherein said analysis employs trained machine learning models;
and displaying a quality score and additional analysis results regarding said commodity's quality to the user through a user interface module (110) of a mobile application, providing real-time feedback and further information to aid in decision-making regarding said commodity.

SYSTEM AND METHOD FOR ASSESSING THE QUALITY OF A COMMODITY

The present disclosure provides a system for assessing the quality of a commodity. The system comprises an image capture module configured to utilize a built-in camera of a mobile device to capture multiple images of the commodity from various angles and perspectives. An image processing module is configured to apply a series of image enhancement algorithms on the captured images, which include noise reduction, image stabilization, and lighting adjustment to standardize image quality. A feature extraction module is configured to employ computer vision algorithms to extract relevant features from the processed images, which include color, texture, shape, and the presence of defects of the commodity. A machine learning analysis module is configured to analyze the extracted features using trained machine learning models, employing supervised learning techniques to classify the commodity into quality categories. Additionally, a user interface module is configured to display a quality score and analysis results to the user through a mobile application interface, offering real-time feedback and additional information regarding the commodity's quality.
Fig. 1

Drawings

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FIG. 4
, Claims:I/We claim:

A system (100) for assessing the quality of a commodity comprising:
an image capture module (102) configured to utilize a built-in camera of a mobile device to capture multiple images of said commodity from various angles and perspectives;
an image processing module (104) configured to apply a series of image enhancement algorithms on said captured images, said algorithms including noise reduction, image stabilization, and lighting adjustment to standardize image quality;
a feature extraction module (106) configured to employ computer vision algorithms to extract relevant features from said processed images, said features including color, texture, shape, and presence of defects of said commodity;
a machine learning analysis module (108) configured to analyze said extracted features using trained machine learning models, said models employing supervised learning techniques to classify said commodity into quality categories; and
a user interface module (110) configured to display a quality score and analysis results to the user through a mobile application interface, offering real-time feedback and additional information regarding said commodity's quality.
The system of claim 1, wherein said image processing module (104) further comprises color correction and dynamic range adjustment functionalities to adapt to varying lighting conditions during image capture.
The system of claim 1 or 2, wherein said feature extraction module (106) includes algorithms for edge detection, color histogram analysis, and texture analysis specifically optimized for said commodity.
The system of any preceding claim, wherein said machine learning analysis module (108) includes models trained on a dataset comprising labeled examples of high and low-quality commodities to facilitate accurate quality classification.
The system of any preceding claim, wherein said user interface module (110) further provides recommendations for alternative products or suggestions for improving the quality of said commodity based on the analysis results.
The system of any preceding claim, wherein said machine learning analysis module (108) is configured to utilize convolutional neural networks (CNNs) for the analysis and classification of said commodity's quality.
The system of any preceding claim, wherein said image capture module (102) is configured to guide the user in capturing images covering necessary angles and perspectives for comprehensive analysis through visual cues on the mobile device screen.
The system of any preceding claim, wherein said user interface module (110) is configured to collect user feedback on the quality assessment's accuracy, which is used to continuously refine and improve the accuracy of said machine learning analysis module (108).
A method for assessing the quality of a commodity using a system (100) comprising modules for image capture (102), image processing (104), feature extraction (106), machine learning analysis (108), and user interface (110), the method comprising the steps of:
capturing multiple images of said commodity using a built-in camera of a mobile device through an image capture module (102);
applying image enhancement algorithms, including noise reduction, image stabilization, and lighting adjustment, to said captured images using an image processing module (104);
extracting relevant features from said processed images, including aspects related to color, texture, shape, and defects of said commodity, using a feature extraction module (106);
analyzing said extracted features to classify said commodity into quality categories using a machine learning analysis module (108), wherein said analysis employs trained machine learning models;
and displaying a quality score and additional analysis results regarding said commodity's quality to the user through a user interface module (110) of a mobile application, providing real-time feedback and further information to aid in decision-making regarding said commodity.

SYSTEM AND METHOD FOR ASSESSING THE QUALITY OF A COMMODITY