METHOD TO DETERMINE FRESHNESS OF FRUITS USING MACHINE LEARNING AND VISUAL ANALYSIS

Abstract

Abstract Disclosed is a method for assessing the freshness of fruit. Said method involves capturing an image of fruit using a camera, transmitting said image to a computing device, and processing such image with a machine learning model trained on a dataset of fresh and rotten fruit images. Said image is analyzed to classify the fruit as fresh or rotten based on visual characteristics. A classification result is output to indicate the freshness condition of said fruit, providing a prompt assessment that enables improved handling, inventory, and waste management practices by identifying spoiled fruits effectively.

Specification

Description:







METHOD TO DETERMINE FRESHNESS OF FRUITS USING MACHINE LEARNING AND VISUAL ANALYSIS
Field of the Invention
[0001] The present disclosure generally relates to fruit freshness detection systems. Further, the present disclosure particularly relates to methods for determining fruit freshness using machine learning and visual image analysis.
Background
[0002] 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.
[0003] In recent years, increased attention has been directed toward improving the quality and safety of fresh produce, particularly fruits. Maintaining fruit freshness and accurately identifying spoiled produce has been an essential aspect of supply chain management in the food industry. Several conventional methods have been developed to address the need for determining fruit freshness, including manual inspection by vendors and consumers. However, such manual inspection methods often suffer from subjective judgment and are highly variable in effectiveness, which can lead to inconsistent assessments and increase the risk of selling spoiled fruits to consumers. Such inconsistencies in freshness determination are a significant factor contributing to food waste, economic losses, and foodborne illness risks.
[0004] One well-known method employed for fruit freshness assessment involves the use of gas sensors. In such systems, sensors detect volatile organic compounds (VOCs) emitted by fruits during the ripening process or in the early stages of spoilage. By monitoring the concentration of VOCs, the freshness of the fruit can be inferred based on predefined thresholds associated with fresh and rotten states. However, the effectiveness of gas sensor-based techniques is often limited by external factors such as temperature, humidity, and the surrounding environment, which may interfere with sensor readings and lead to inaccurate assessments. Additionally, gas sensors are prone to wear and malfunctions over time, which can result in inconsistent data. This limitation is further compounded by the fact that gas sensors generally require calibration and periodic maintenance, which adds operational complexity and cost.
[0005] Another conventional approach in fruit freshness detection involves the measurement of physical properties, such as firmness, weight, and colour. For example, some systems employ mechanical sensors to evaluate fruit firmness, which decreases as the fruit ripens or spoils. Weight measurements, in turn, are used to detect dehydration in fruits, which is often indicative of spoilage. Although such mechanical methods provide some level of automation in determining freshness, they are often constrained by the variability in physical properties between different fruit types. Furthermore, firmness and weight alone are not definitive indicators of spoilage, as environmental factors such as storage conditions and fruit variety can affect such measurements. Such systems generally lack precision, as changes in colour or weight may occur without actual spoilage, leading to false positives and ineffective sorting in high-volume environments.
[0006] In addition to gas and mechanical sensor methods, there are spectroscopic techniques that assess fruit freshness based on the reflection and absorption of light by fruit surfaces. Spectroscopy methods, such as near-infrared (NIR) and visible light spectrometry, analyse specific wavelengths of light that correlate with the chemical composition of the fruit. Such techniques are capable of providing detailed information about fruit ripeness and spoilage, as they detect changes in sugar content, moisture levels, and other internal characteristics. Despite their utility, spectroscopic systems are generally expensive and require specialized equipment that may not be feasible for large-scale implementation by vendors and distributors. Additionally, the need for controlled lighting conditions and standardized procedures limits the practical application of spectroscopy-based methods for freshness determination in dynamic, real-world environments.
[0007] Other state-of-the-art systems for freshness detection rely on computer vision technology to visually inspect fruit appearance, including surface texture, size, and shape. Traditional image processing techniques use predefined thresholds to evaluate fruit attributes, such as the presence of blemishes, discoloration, or deformities. However, such approaches are highly sensitive to variations in image quality, lighting, and positioning, which can affect the reliability of the analysis. Further, conventional computer vision techniques often require manual feature extraction, which can be time-consuming and may not adapt well to different types of fruits. Due to such limitations, traditional computer vision systems often fail to achieve consistent accuracy in detecting spoilage across various fruit varieties and environmental conditions.
[0008] 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 determining the freshness of fruits.
Summary
[0009] 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.
[00010] The following paragraphs provide additional support for the claims of the subject application.
[00011] An objective of the present disclosure is to enable accurate and efficient determination of fruit freshness through image-based analysis. The method of the present disclosure uses a machine learning model to classify fruit images as fresh or rotten based on visual characteristics.
[00012] In an aspect, the present disclosure provides a method for determining the freshness of fruit, comprising capturing an image of fruit using a camera, transmitting said image to a computing device, and processing such image with a machine learning model trained on a dataset containing images of fresh and rotten fruits. Said image is analyzed to classify the fruit based on visual characteristics, and a classification result is output to indicate the freshness condition.
[00013] Further, said method enables more accurate fruit classification through various features, including a convolutional neural network, multiple-angle image aggregation, and dataset enhancements for ripeness classification. Moreover, image pre-processing, periodic model updates, and user feedback through visual indicators contribute to improved detection and usability. Furthermore, an alert is generated when a predetermined quantity of rotten fruits is detected in a batch, supporting efficient sorting and removal of spoiled produce.
Brief Description of the Drawings
[00014] 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:
[00015] FIG. 1 illustrates a method for determining the freshness of fruit, in accordance with the embodiments of the present disclosure.
[00016] FIG. 2 illustrates a decision-based process for determining fruit freshness, in accordance with the embodiments of the present disclosure.
Detailed Description
[00017] 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.
[00018] 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.
[00019] 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.
[00020] As used herein, the term "method for determining the freshness of fruit" refers to a series of steps enabling the assessment of fruit freshness through image-based classification. Such a method encompasses capturing an image of the fruit and using digital processes to evaluate freshness. Said method involves obtaining visual information by capturing fruit images, which are then transmitted for computational processing to evaluate freshness levels. In such a context, the freshness of fruit refers to its state of ripeness or potential spoilage, discerned through visual cues that indicate freshness or rottenness. Said method employs computational analysis to categorize fruit condition, improving the accuracy and efficiency of freshness assessment. Additionally, the method applies to any process that assesses fruit freshness based on image-based classifications and is not limited to any specific type of fruit or imaging device, thereby encompassing diverse fruit types and camera setups in a wide range of commercial or consumer environments.
[00021] As used herein, the term "capturing an image of a fruit" refers to the process of obtaining a digital representation of the fruit's visual characteristics through a camera. Said process involves photographing the fruit in a manner that preserves essential visual details necessary for freshness analysis. The term "capturing" applies broadly to any type of imaging that records an image suitable for analysis, regardless of fruit type, shape, or colour. Furthermore, capturing an image includes taking multiple images in cases where additional perspectives or lighting conditions improve analysis accuracy. Such image captures are typically performed in a controlled environment to optimize clarity, contrast, and detail. Additionally, the image-capturing process may involve adjusting focus, exposure, and resolution to produce high-quality images that facilitate effective evaluation and classification based on visual traits indicative of freshness or rottenness.
[00022] As used herein, the term "transmitting said image to a computing device" refers to the act of transferring the captured fruit image data from the camera to a processing system for analysis. Said transmission may occur over a wired or wireless connection, depending on system configuration. Transmission involves converting captured image data into a compatible digital format and sending such data to a computing device for subsequent processing. The computing device receiving the image data is intended to analyze said image, making transmission a key step in enabling image processing functions. Additionally, said image transmission may include data formatting, compression, or encryption to optimize compatibility, speed, and security during transfer. Transmission methods apply to various types of computing systems and are adaptable to multiple devices, ensuring versatile operation across differing hardware configurations while preserving data integrity for reliable freshness classification.
[00023] As used herein, the term "processing such image with a machine learning model" refers to the computational analysis of the captured fruit image by applying a pre-trained machine learning model designed to evaluate and classify visual characteristics. Processing such image involves loading the image data into a computing framework where a machine learning model identifies patterns associated with freshness or spoilage. Said machine learning model is typically trained on a dataset of labelled images depicting fresh and rotten fruit states, allowing said model to recognize visual indicators of each classification. Processing the image may also include performing preliminary adjustments, such as resizing and normalizing the image, to ensure compatibility with model requirements. The term "processing" broadly encompasses each step necessary to prepare, analyze, and classify the image data, ultimately yielding insights into the fruit's condition through pattern recognition within said model.
[00024] As used herein, the term "analyzing said image to classify the fruit as fresh or rotten based on visual characteristics" refers to interpreting the processed image data through computational techniques to determine the freshness status of the fruit. Analyzing the image involves examining visual attributes, such as color, texture, and blemishes, which may correlate with freshness or spoilage indicators. Said analysis typically employs image processing algorithms that detect specific features within the visual data, enabling the classification of the fruit as either fresh or rotten. Visual characteristics, such as discoloration or soft spots, provide cues about the freshness condition, and said analysis process aims to identify such markers for accurate classification. Additionally, analysis may use multiple visual indicators for robust classification, considering variations in fruit types, shapes, and sizes. The analysis process thus utilizes a comprehensive approach to interpreting visual details that correlate with the freshness level of the fruit.
[00025] As used herein, the term "outputting a classification result to indicate the freshness condition of said fruit" refers to the act of displaying or transmitting the final result of the freshness analysis, which specifies whether the fruit is fresh or rotten. Outputting said classification result may involve presenting such information on a display screen or transmitting said result to a connected system for further action. Said result serves as the end point of the freshness determination process, providing an easily interpretable outcome based on the visual analysis. The classification result is generated through the machine learning model's interpretation of the analyzed image, where freshness indicators inform the final categorization. Outputting said classification result may also involve conveying additional details, such as a freshness score, indicating the degree of freshness or spoilage. Such a result enables immediate feedback regarding fruit condition, supporting decisions for storage, sale, or disposal.
[00026] FIG. 1 illustrates a method for determining the freshness of fruit, in accordance with the embodiments of the present disclosure. In an embodiment, an image of a fruit is captured using a camera. Said camera is positioned to focus on the fruit, ensuring a clear representation of visual characteristics essential for freshness analysis. Image capture involves setting specific parameters, such as resolution, focus, and exposure, to obtain detailed visual data for accurate processing. In capturing said image, the camera may be operated manually or automatically to photograph the fruit from various angles or distances, depending on system requirements. The image may be captured under controlled lighting conditions to reduce shadow interference and optimize clarity. In some setups, multiple images may be taken, allowing the system to generate a comprehensive view of the fruit's surface features, such as color, texture, and any visible irregularities. This image capture step provides the foundational data necessary for subsequent analysis in freshness determination.
[00027] In an embodiment, said captured image is transmitted to a computing device for further processing. Transmission of said image may occur through a wired connection, such as a USB interface, or through wireless methods, including Wi-Fi or Bluetooth. Said transmission step involves converting the image into a compatible digital format suitable for analysis by the computing device. The image file may be compressed to reduce data size without compromising quality, ensuring efficient and reliable transmission. Said image transmission may also include protocols for data security and integrity, preventing loss or alteration during transfer. Upon successful transmission, said image is stored temporarily or permanently on the computing device's memory for processing. Said transmission allows the image data to be available to various computational elements, facilitating immediate or scheduled analysis of the fruit's freshness condition.
[00028] In an embodiment, such image transmitted to the computing device undergoes processing by a machine learning model trained on a dataset of images depicting fresh and rotten fruits. Said machine learning model has been developed and optimized to identify distinguishing visual features related to freshness and spoilage. Processing includes loading the image into the computational framework where said model can analyze pixel patterns, color variations, and texture attributes that indicate freshness or rottenness. Said model may perform pre-processing tasks, such as resizing and normalization, ensuring compatibility with the machine learning model's operational standards. In the processing stage, said model applies learned parameters derived from the dataset, allowing the system to identify relevant patterns with high accuracy. Such processing prepares the image for final analysis, enabling effective classification of the fruit's condition.
[00029] In an embodiment, said image is analyzed to classify the fruit as fresh or rotten based on specific visual characteristics identified by the machine learning model. Said analysis step involves examining color hues, texture inconsistencies, and other visual markers that correlate with either freshness or spoilage. The analysis employs computational recognition techniques that interpret said image data, enabling accurate classification. Indicators of freshness may include uniform coloration and firm textures, while signs of spoilage may involve discoloration, softening, or blemishes. By analyzing various visual factors, the system assesses the fruit's condition, ultimately categorizing it as fresh or rotten. Analysis may also consider multiple factors collectively to ensure robustness in classification across varying fruit types and conditions.
[00030] In an embodiment, a classification result is output to indicate the freshness condition of the fruit based on said analysis. Said output may be displayed on a visual interface or transmitted to a connected device, providing a user with an immediate assessment of freshness. Said classification result may include a simple designation of "fresh" or "rotten" or may present additional details, such as a freshness score that quantifies the likelihood of spoilage. The output may be generated in real time or stored for later retrieval, depending on the specific application requirements. Additionally, the output may trigger an alert in cases where a batch contains a significant proportion of rotten fruits, supporting operational decisions related to sorting and disposal.
[00031] In an embodiment, a computing device comprises a Raspberry Pi, which operates as the primary processor for executing a machine learning model to assess fruit freshness. The Raspberry Pi is structured to manage the computational demands of running the machine learning model while being compact and energy-efficient, making it

suitable for real-time image analysis. Said Raspberry Pi is interfaced with an imaging device to receive digital images of fruit, which are then processed and analyzed. The Raspberry Pi accesses stored code and datasets, performing calculations to classify each image based on visual indicators of freshness or spoilage. Said device can execute multiple tasks simultaneously, including image pre-processing, data transmission, and model analysis, optimizing operational continuity. Additional components, such as memory storage and communication interfaces, may be integrated into the Raspberry Pi setup to support data handling and transmission. Such a computing device is adaptable, supporting updates to enhance model accuracy over time.
[00032] In an embodiment, the machine learning model utilized to process fruit images employs a convolutional neural network (CNN) to identify visual characteristics indicative of freshness or spoilage. A CNN is structured to analyze images by detecting patterns within layers of visual data, such as texture, color distribution, and irregularities that signify freshness levels. Said CNN is trained on a dataset containing labeled examples of fresh and rotten fruit, enabling said network to differentiate between various conditions by recognizing associated patterns. The CNN operates by segmenting images into smaller units, allowing analysis of localized features, which are aggregated to form a comprehensive classification. Through successive layers of processing, the CNN can detect complex patterns beyond simple color or shape, including subtle indicators of ripeness or decay. Said neural network can adapt to variations across fruit types, improving reliability in diverse application settings.
[00033] In an embodiment, multiple images of the fruit are captured from different angles and combined to improve classification accuracy. By obtaining varied perspectives, the system can acquire a more comprehensive representation of the fruit's surface, accounting for visual features that may vary with angle or lighting. Such images are aggregated to create a composite dataset, minimizing the chances of misclassification due to partial occlusion or inconsistent lighting conditions. Capturing images from multiple perspectives allows the model to identify unique or subtle indicators of freshness, as certain blemishes or texture changes may be visible from specific angles. Said approach improves the robustness of the classification by providing diverse data points, which support a more accurate analysis by the machine learning model. Aggregation of multiple images enables more consistent assessments across varying lighting conditions and fruit orientations.
[00034] In an embodiment, the dataset used to train the machine learning model includes images labeled with different ripeness stages, allowing the model to classify intermediate stages between fresh and rotten. Such dataset encompasses a range of visual conditions, from early ripeness to advanced spoilage, providing the model with comprehensive examples of each stage. By including images of partially ripe or partially spoiled fruits, the dataset enables the model to identify gradations in freshness, resulting in a nuanced classification system that differentiates more than two states. Such labeled images help the model recognize the progression of spoilage and ripening characteristics, such as slight color shifts or minor texture changes, which may not be as pronounced in standard fresh or rotten images. A diverse dataset structure allows the model to perform well in real-world applications, where fruit conditions often fall between idealized states.
[00035] In an embodiment, the method further includes pre-processing the captured image by resizing, normalizing, and enhancing contrast to improve model accuracy. Resizing adjusts the image dimensions to meet the machine learning model's input requirements, ensuring consistent data across all images. Normalizing involves adjusting the color and lighting levels to maintain uniform brightness and contrast, minimizing inconsistencies due to varying light conditions. Contrast enhancement is applied to emphasize the differences between lighter and darker areas, making visual features such as bruises or discoloration more distinct. Such pre-processing steps reduce the likelihood of misclassification by providing the model with clearer, standardized images that highlight relevant features. Said steps are performed automatically to streamline the preparation process, enabling efficient image handling and optimal input quality for model analysis.
[00036] In an embodiment, the classification result includes a freshness score, which represents a likelihood value indicating the probability of the fruit being fresh. Said freshness score provides a quantitative measure that reflects the model's confidence in the freshness classification. The score is generated based on the analysis of visual features, such as color and texture, with higher scores indicating a higher likelihood of freshness. Such a scoring system allows users to understand the degree of freshness or spoilage in a more nuanced way, as opposed to a binary fresh or rotten classification. The freshness score can be displayed on a scale, such as 0 to 100, where each point represents a distinct probability level. Providing a probability score enhances the interpretability of the classification result, supporting more informed decision-making based on freshness likelihood.
[00037] In an embodiment, the machine learning model undergoes periodic updates by incorporating additional labeled images of fresh and rotten fruits to improve performance over time. Said updates allow the model to adapt to variations in fruit types, seasonal changes, and other factors affecting fruit appearance. New images are collected and labeled, expanding the dataset to include recent or unique fruit conditions that may not have been captured in the original dataset. Training on such updated dataset enables the model to refine its pattern recognition capabilities, enhancing classification accuracy. Said updating process may be scheduled regularly or initiated manually, depending on specific requirements. Continuous model enhancement through data expansion allows the freshness determination system to maintain accuracy and adaptability in dynamic real-world environments, addressing potential variations effectively.
[00038] In an embodiment, the method includes providing feedback to a user through a visual indicator or display device associated with the computing device, where such visual indicator signifies the classification result. Said visual indicator may consist of a graphical display, color-coded light, or other display formats that communicate freshness status. For example, a green light may indicate a fresh classification, while a red light denotes spoilage. Alternatively, a screen may present the freshness score alongside the classification, allowing users to gauge the level of freshness quantitatively. Such feedback provides immediate information about the fruit's condition, enabling prompt action, such as sorting or discarding spoiled fruits. Said feedback mechanism is adaptable to different display types and configurations, supporting various user requirements and settings.
[00039] In an embodiment, the machine learning model is structured to provide an alert to a user when a predetermined number of rotten fruits is detected within a batch, enabling efficient sorting and removal of spoiled fruits. Said alert is triggered if the classification process identifies rotten fruits exceeding a specified threshold within the assessed batch. Such alert may be in the form of a visual cue, audio signal, or digital notification, drawing the user's attention to the need for sorting or discarding. The alert threshold can be adjusted based on operational requirements, allowing customization according to batch size or freshness criteria. Automated alerts streamline the management of fruit quality, reducing manual inspection efforts and supporting timely intervention to maintain produce standards in storage or distribution settings.
[00040] FIG. 2 illustrates a decision-based process for determining fruit freshness, in accordance with the embodiments of the present disclosure. Starting with image capture, the method first involves obtaining a photograph of the fruit using a camera. The captured image is then transmitted to a computing device for analysis. Upon receipt, the computing device processes the image through a machine learning model trained on a dataset containing images of fresh and rotten fruits. Following image processing, a decision point determines if the fruit is classified as fresh. If classified as fresh, the result outputs "Fruit is Fresh," and the process ends. If the fruit is classified as rotten, the output displays "Fruit is Rotten," and the process similarly concludes. This decision-based approach effectively categorizes fruit freshness, providing users with immediate and clear feedback on the fruit's condition, supporting efficient sorting and handling decisions.
[00041] In an embodiment, capturing an image of a fruit using a camera enables accurate collection of visual data essential for assessing freshness. Said image capture allows for a high-resolution view of the fruit's surface, revealing indicators such as color, texture, and any visual signs of spoilage. By obtaining detailed visual information, this image capture step provides foundational data, facilitating subsequent analysis of freshness and spoilage characteristics. Said method improves detection accuracy by ensuring that the camera captures all relevant features, which minimizes misinterpretation in cases where surface-level indicators are strong markers of freshness. Additionally, by enabling automation in image capture, such a method allows scalable assessments that can handle large quantities of fruits, which is beneficial for commercial settings.
[00042] In an embodiment, using a Raspberry Pi as the computing device to execute the machine learning model enhances the method's applicability by providing a compact, energy-efficient processing solution. The Raspberry Pi enables on-site computation, which eliminates the need for external servers and reduces data transmission latency, allowing for real-time classification results. Said device handles image input, processing, and output efficiently, supporting smooth operation across multiple stages of image analysis. The Raspberry Pi's adaptability to various power sources also makes it suitable for diverse operational environments, including those with limited infrastructure. Furthermore, the Raspberry Pi offers flexibility for integrating additional hardware components if needed, such as external storage or display devices, which broadens the method's application scope for various commercial and consumer-level implementations.
[00043] In an embodiment, using a convolutional neural network (CNN) within the machine learning model to analyze visual features enhances the model's ability to distinguish between fresh and rotten fruit based on nuanced visual patterns. A CNN operates by processing images through multiple layers, allowing it to recognize complex features, such as subtle texture changes, color gradients, and shapes associated with freshness or decay. Said neural network's structure allows for robust analysis across varying fruit types and conditions, accommodating differences in shape, color, and size that are common in fresh produce. By focusing on specific image regions, the CNN increases classification accuracy, even in cases where superficial color might not clearly indicate spoilage. Additionally, CNNs adapt well to varied image qualities, making them suitable for real-world applications with diverse image capture conditions.
[00044] In an embodiment, capturing multiple images of fruit from different angles and aggregating such images improves classification accuracy by providing a comprehensive view of the fruit's surface. Said method minimizes the risk of missing critical visual indicators that may only be visible from certain angles, such as minor blemishes or discolorations that could signify spoilage. Aggregating multiple perspectives into a single dataset allows the model to evaluate the fruit more accurately, especially when surface irregularities or lighting conditions could impact a single image's reliability. By incorporating multi-angle views, said method creates a more thorough representation, supporting accurate freshness classification regardless of initial image quality or environmental factors, such as shadows or uneven lighting. This approach enhances adaptability in diverse settings and supports consistent classification outcomes.
[00045] In an embodiment, incorporating a dataset labeled with varying degrees of ripeness allows the machine learning model to classify fruit into stages beyond just fresh or rotten, providing a more nuanced assessment. By training the model on images representing intermediate ripeness stages, the model can detect subtle visual indicators of freshness decline, such as slight discoloration or texture changes. This range of labeled data allows the model to assess fruits with greater precision, facilitating decisions around optimal usage or storage time based on freshness levels. Such training supports applications where fruits at different ripeness levels must be handled differently, such as separating fruits suitable for immediate sale from those needing further ripening. Consequently, said dataset structure supports adaptable freshness assessments applicable across diverse types of fruit and operational requirements.
[00046] In an embodiment, pre-processing the captured image by resizing, normalizing, and enhancing contrast optimizes the image for analysis, improving the model's classification accuracy. Resizing standardizes image dimensions, ensuring compatibility with model input requirements and promoting uniformity across images. Normalization adjusts color and brightness levels to account for lighting inconsistencies, which enhances reliability in feature detection. Contrast enhancement highlights critical visual features, such as discolorations or textural changes, making it easier for the model to identify freshness indicators. Such pre-processing steps eliminate potential errors from variations in image quality, supporting more consistent classification. Automation of these pre-processing tasks allows efficient handling of large image quantities, making such an approach suitable for high-throughput environments where consistent image quality is essential for reliable classification results.
[00047] In an embodiment, including a freshness score in the classification result provides a likelihood value indicating the probability that the fruit is fresh, enabling a quantitative approach to freshness assessment. Said score reflects the model's confidence in the classification, offering users a measurable indicator beyond a binary classification. A freshness score allows for flexible interpretation, such as setting thresholds to determine which fruits meet specific quality standards. Such scoring also provides transparency in cases where classification is less certain, supporting informed decision-making. For instance, higher scores suggest greater freshness, while lower scores may prompt sorting or removal. Such quantitative outputs offer an adaptable assessment system that can be customized according to user-defined freshness standards or operational requirements.
[00048] In an embodiment, periodic updates of the machine learning model with additional labeled images of fresh and rotten fruits enhance model performance over time by adapting to new data. These updates expand the dataset to include diverse fruit conditions, such as those impacted by seasonal variations or new fruit varieties, broadening the model's adaptability. Updating with recent data enables the model to learn new visual patterns that may arise in fresh produce, ensuring accuracy despite potential changes in fruit appearance. Such updates also support continuous improvement, as the model's classification capabilities are refined with each new dataset. Regularly enhancing the model with current images sustains consistent performance across various operational settings and helps maintain high reliability for freshness assessment.
[00049] In an embodiment, providing feedback to a user through a visual indicator or display device offers immediate information regarding fruit freshness, supporting timely decision-making. Said visual indicator could use color coding or numerical values to signify freshness status, providing an intuitive way for users to interpret classification results. For instance, different colors may represent fresh, ripe, or spoiled conditions, enabling quick visual confirmation. By presenting freshness information directly, said method allows users to manage sorting or discarding operations promptly, supporting efficient workflows. A variety of indicator types, including screens or lights, can be implemented depending on user needs and operational contexts, making the feedback system adaptable to commercial, retail, or consumer-level applications.
[00050] In an embodiment, an alert function triggers when the machine learning model detects a predetermined quantity of rotten fruits within a batch, enabling efficient management of fruit quality. Said alert notifies users if the detected proportion of spoiled fruits exceeds a set threshold, supporting decisions around sorting or discarding. An adjustable alert threshold allows users to customize settings according to batch size or quality standards, providing flexibility in various operational environments. Such an alert mechanism is particularly useful in large-scale sorting processes, where manual detection would be impractical. By automating quality monitoring through an alert system, said method reduces labor requirements and enhances reliability in identifying batches needing immediate attention, supporting streamlined quality control in high-volume settings.
[00051] 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.
[00052] 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.
[00053] 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:
1. A method for determining the freshness of fruit, comprising:
capturing an image of a fruit using a camera module;
transmitting said image to a computing device;
processing such image with a machine learning model trained on a dataset comprising images of fresh and rotten fruits;
analyzing said image to classify the fruit as fresh or rotten based on visual characteristics; and
outputting a classification result to indicate the freshness condition of said fruit.
2. The method of claim 1, wherein said computing device is a Raspberry Pi configured to execute said machine learning model.
3. The method of claim 1, wherein the machine learning model utilizes a convolutional neural network to analyze said image for visual features indicative of freshness or rottenness.
4. The method of claim 1, further comprising capturing multiple images of said fruit from different angles, wherein such images are aggregated to improve classification accuracy.
5. The method of claim 1, wherein said dataset includes images labeled with varying degrees of ripeness, allowing said machine learning model to classify intermediate stages between fresh and rotten.
6. The method of claim 1, further comprising pre-processing said image by resizing, normalizing, and enhancing contrast to improve model accuracy.
7. The method of claim 1, wherein the classification result includes a freshness score, wherein said freshness score represents a likelihood value indicating the probability of said fruit being fresh.
8. The method of claim 1, further comprising updating said machine learning model periodically with additional labeled images of fresh and rotten fruits to enhance model performance over time.
9. The method of claim 1, further comprising providing feedback to a user through a visual indicator or display device associated with said computing device, wherein said visual indicator signifies the classification result.
10. The method of claim 1, wherein said machine learning model is configured to provide an alert to a user if a predetermined number of rotten fruits is detected within a batch, enabling efficient sorting and removal of spoiled fruits.


METHOD TO DETERMINE FRESHNESS OF FRUITS USING MACHINE LEARNING AND VISUAL ANALYSIS
Abstract
Disclosed is a method for assessing the freshness of fruit. Said method involves capturing an image of fruit using a camera, transmitting said image to a computing device, and processing such image with a machine learning model trained on a dataset of fresh and rotten fruit images. Said image is analyzed to classify the fruit as fresh or rotten based on visual characteristics. A classification result is output to indicate the freshness condition of said fruit, providing a prompt assessment that enables improved handling, inventory, and waste management practices by identifying spoiled fruits effectively.
, Claims:Claims
I/We Claim:
1. A method for determining the freshness of fruit, comprising:
capturing an image of a fruit using a camera module;
transmitting said image to a computing device;
processing such image with a machine learning model trained on a dataset comprising images of fresh and rotten fruits;
analyzing said image to classify the fruit as fresh or rotten based on visual characteristics; and
outputting a classification result to indicate the freshness condition of said fruit.
2. The method of claim 1, wherein said computing device is a Raspberry Pi configured to execute said machine learning model.
3. The method of claim 1, wherein the machine learning model utilizes a convolutional neural network to analyze said image for visual features indicative of freshness or rottenness.
4. The method of claim 1, further comprising capturing multiple images of said fruit from different angles, wherein such images are aggregated to improve classification accuracy.
5. The method of claim 1, wherein said dataset includes images labeled with varying degrees of ripeness, allowing said machine learning model to classify intermediate stages between fresh and rotten.
6. The method of claim 1, further comprising pre-processing said image by resizing, normalizing, and enhancing contrast to improve model accuracy.
7. The method of claim 1, wherein the classification result includes a freshness score, wherein said freshness score represents a likelihood value indicating the probability of said fruit being fresh.
8. The method of claim 1, further comprising updating said machine learning model periodically with additional labeled images of fresh and rotten fruits to enhance model performance over time.
9. The method of claim 1, further comprising providing feedback to a user through a visual indicator or display device associated with said computing device, wherein said visual indicator signifies the classification result.
10. The method of claim 1, wherein said machine learning model is configured to provide an alert to a user if a predetermined number of rotten fruits is detected within a batch, enabling efficient sorting and removal of spoiled fruits.

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