AN ARTIFICIAL INTELLIGENCE DRIVEN GRAIN GRADING SYSTEM FOR AUTOMATED QUALITY ASSESSMENT AND REAL-TIME MONITORING

Abstract

7. ABSTRACT The AI-driven grain grading system (100) automates grain quality assessment using multispectral imaging and machine learning to detect visible and hidden contaminants, defects, and foreign materials. Key components include a high-resolution imaging unit (102) for capturing detailed grain images, an AI-powered classification engine (104) for categorizing grains based on quality indicators, a data processing unit (106) for real-time report generation, and a cloud-based storage system (108) for secure data access. The user interface (110) displays grading results and productivity reports, while the alert system (112) notifies stakeholders of quality breaches. This system provides objective, consistent, and efficient grading, benefiting agricultural supply chain stages from storage to export. Compliant with ISO standards, it ensures real-time, high-precision quality control with a processing capacity of up to 100 tons per hour. The figure associated with abstract is Fig. 1.

Specification

Description:4. DESCRIPTION
Technical Field of the Invention

The present invention related to agricultural technology. More particularly focusing on the development of automated systems for grain quality assessment, utilizing artificial intelligence (AI), machine learning, and high-resolution imaging to enhance the accuracy, efficiency of grain grading processes.

Background of the Invention

In agriculture, grain grading is essential to assess the quality of crops, such as wheat, rice, pulses, and beans, which directly impacts their market value, export potential, and processing suitability. Accurate and reliable grading ensures that grains meet quality standards required by domestic markets and international regulations, supporting fair pricing, consumer trust, and food safety. Traditionally, grain grading has been carried out manually by skilled inspectors who visually assess samples for defects, contaminants, and other quality attributes. However, manual grading methods suffer from several limitations that have proven inadequate for today's high-volume, precision-oriented agricultural demands.

The primary challenge of manual grain grading lies in its subjectivity and inconsistency. Different inspectors may apply varying standards based on their expertise, interpretation, and external factors like lighting and fatigue. For instance, what one inspector may classify as acceptable, another may classify as substandard. This variability in human judgment can result in inconsistent quality standards, leading to potential conflicts within supply chains, especially across regions or markets where inspection criteria might differ. Such subjectivity not only affects grain pricing but also diminishes trust between buyers and sellers, complicating transactions and sometimes even leading to trade disputes. This inconsistency is especially problematic in export markets, where non-compliance with quality requirements can result in costly rejections or penalties.

In addition to subjectivity, manual grading methods are labour-intensive and time-consuming. The process requires skilled inspectors to analyse each sample, which becomes increasingly impractical with large quantities of grain. As the global demand for agricultural products continues to rise, particularly from bulk consumers like food processing industries and export markets, the limitations of manual grading become more evident. Manual inspections create bottlenecks in the supply chain, delaying transactions and slowing down overall processing speeds. This inefficiency has a significant economic impact, as it requires considerable resources in terms of time, labour, and associated costs, particularly for large-scale operations handling extensive volumes of grains.

Over the years, various automated and semi-automated grain grading solutions have been proposed to overcome the drawbacks of manual grading. Conventional optical sorting systems, for example, use high-speed cameras and sensors to detect visible characteristics like size and colour, aiding in sorting grains based on pre-determined standards. However, these optical systems are generally limited to superficial assessments that primarily identify colour variations or size discrepancies. For instance, optical sorters are often ineffective at detecting contaminants or internal defects, such as fungal growth, that may not be visible in standard lighting conditions. Another limitation of optical sorting is the high cost associated with these systems, which often includes expensive hardware, installation, and ongoing maintenance, making them accessible mainly to large agricultural enterprises rather than smaller farms or cooperatives.

Some advanced systems incorporate additional imaging techniques, like X-ray or hyper spectral imaging, to examine internal quality factors in grains. These technologies are intended to improve defect detection, but they also come with considerable disadvantages. For example, X-ray systems may detect certain internal anomalies, but they are less effective at identifying surface-level contaminants, such as dust or pesticide residues, that influence overall grain quality. Hyperspectral imaging, while capable of capturing a broader range of quality indicators, typically involves complex calibration processes and high implementation costs, making it difficult for routine use in commercial grain grading applications. Furthermore, these systems often lack real-time data processing and reporting capabilities, limiting their effectiveness in fast-paced environments like bulk handling facilities and food processing plants.

In light of these limitations, there remains a pressing need for an efficient, scalable, and objective grain grading system capable of handling high volumes of grains with high accuracy. The ideal system would not only overcome the subjectivity and labor requirements associated with manual inspection but also provide real-time analysis and feedback, enabling informed decision-making across the agricultural supply chain. An optimal solution would need to balance cost-efficiency with technical sophistication, making advanced grading accessible for both small-scale farmers and large agricultural enterprises.

Moreover, the system must have the ability to detect both visible and hidden contaminants and defects that go beyond the capabilities of conventional optical sorting or manual grading. Such contaminants, like mold or microbial infestations, often require specialized imaging to detect and are critical to ensuring food safety standards. The solution should also allow for secure data storage and sharing, providing stakeholders with access to grading results and historical data for better quality control, traceability, and regulatory compliance. With the increasing focus on food safety and export quality standards, a robust, technology-driven grain grading solution is urgently needed to address the limitations of existing grading methods and support the evolving demands of global agricultural markets.

In conclusion, the agricultural sector is in dire need of an AI-driven grain grading system that integrates advanced imaging and machine learning to deliver objective, accurate, and scalable grading capabilities. Such a system would mark a transformative step in grain quality assessment, allowing the agricultural supply chain to operate more efficiently, meet regulatory standards, and ultimately improve the quality and safety of food products reaching consumers.

Brief Summary of the Invention

The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

The primary object of the present invention is to introduce a comprehensive and automated grain grading system that overcomes the limitations of traditional manual and optical-based grading techniques. By integrating high-resolution multispectral imaging with advanced artificial intelligence, this invention aims to deliver accurate, consistent, and objective grain quality assessments. This solution seeks to address critical quality indicators, such as defects, contaminants, and foreign materials, that may be undetectable through traditional grading methods, thereby enhancing overall grading reliability and marketability of agricultural produce.

Another object of the invention is to significantly reduce the subjectivity inherent in manual grading processes. Grain quality grading performed manually by inspectors often varies based on individual judgment, experience, and environmental conditions, leading to inconsistencies in quality assessment. By employing machine learning algorithms, this invention provides an objective grading process that eliminates subjectivity, ensuring that every batch of grains meets consistent standards regardless of who or where the grading is conducted.

A further object of the invention is to improve grading efficiency and scalability for agricultural operations. The present invention leverages AI and cloud-based technologies to enable real-time data processing and report generation, significantly reducing the time required for grading while supporting high throughput of grain samples. This scalability is essential for meeting the demands of modern agricultural environments, such as large-scale farms, grain storage facilities, and processing plants, where vast quantities of grains need rapid and reliable quality assessment.

Additionally, an object of the invention is to enable real-time data sharing and seamless access across the agricultural supply chain. By incorporating a cloud-based storage and sharing platform, the invention allows stakeholders such as farmers, brokers, quality control teams, and export managers to access grading results from any location. This accessibility fosters collaborative decision-making, enabling faster and more informed responses to market needs and quality standards.

According to an aspect of the present invention, an AI-driven grain grading system designed to automate and improve the accuracy, speed, and scalability of grain quality assessments. The system utilizes advanced high-resolution, multispectral imaging to capture detailed images of individual grains across multiple wavelengths, detecting visible and hidden defects and contaminants. This imaging unit is equipped with RGB and infrared sensors that operate under variable lighting and spectral conditions, allowing the system to identify quality issues such as mold, discoloration, microbial growth, and foreign particles, which may not be visible to the naked eye. The multispectral imaging unit ensures that even subtle quality indicators are captured for a comprehensive analysis of each grain sample.

The AI-powered classification engine that processes the high-resolution images provided by the imaging unit. The classification engine is built on a machine learning model trained on an extensive dataset of grain quality metrics, enabling it to classify grains based on size, shape, surface texture, colour, defect type, and contamination level. This engine leverages deep learning algorithms that continuously improve through user feedback, allowing it to adapt to new quality standards and data patterns over time. The model's ability to analyse both visible and invisible defects enhances its accuracy, making it a reliable tool for ensuring consistent grading across diverse grain types and quality standards.

The data processing unit of the invention is configured to aggregate and analyse the classification results generated by the AI-powered classification engine. It compiles these results into real-time grading reports, providing stakeholders with a comprehensive overview of the quality metrics for each batch of grains. The data processing unit not only generates grading scores based on defect density and contamination levels but also categorizes grains into quality bins such as "Good Quality," "Defective," and "Contaminated." This categorized data enables swift and informed decision-making for stakeholders, particularly in high-demand environments like grain processing units and export markets.

In another aspect, the invention incorporates a cloud-based storage system that securely stores the grading data, ensuring that stakeholders can access grading results remotely. This cloud infrastructure supports the scalability of the system, allowing it to handle large volumes of data and provide simultaneous access for multiple users across different geographic locations. This real-time data sharing capability ensures that stakeholders along the supply chain, including farmers, brokers, and exporters, can monitor grading results and make timely, collaborative decisions to optimize grain quality and distribution.

In another aspect, an intuitive user interface is also provided to facilitate easy access and navigation of grading results. The user interface includes a visual dashboard that displays real-time grading outcomes and key performance indicators for each batch of grains. This interface allows users to generate customizable reports, access historical data for trend analysis, and utilize interactive filters to sort grading data based on specific quality metrics, time periods, or grain types. These features enhance user control, enabling tailored analysis and data presentation suited to the needs of different stakeholders in the agricultural supply chain.

In another aspect, the invention incorporates an alert system integrated within the user interface, configured to notify users whenever detected contaminants or defects exceed pre-defined thresholds. This alert system is adaptable, allowing users to set specific thresholds for different types of quality indicators such as contaminant levels or defect density. Alerts are generated and sent through multiple channels, including in-app notifications, email, and SMS, ensuring that stakeholders are promptly informed of any quality breaches. This proactive alert system enables users to take immediate corrective actions to address quality issues, thereby minimizing losses and enhancing overall grain quality management.

In yet another aspect, the system encompasses a method for grain grading that ensures comprehensive quality assessment and efficient data management. The method involves capturing multispectral images of individual grains, processing these images through the AI-powered classification engine to detect both visible and hidden quality issues, generating real-time grading reports, and storing these reports securely in the cloud for accessible and actionable insights. This process is streamlined to support high-volume grading requirements without compromising accuracy or consistency, making it well-suited for both small and large-scale agricultural operations.

Further scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, the detailed description and specific examples, while indicating preferred embodiments of the invention, will be given by way of illustration along with complete specification.

Brief Summary of the Drawings

The invention will be further understood from the following detailed description of a preferred embodiment taken in conjunction with an appended drawing, in which:

Fig. 1a-b illustrates the schematic diagram of AI-driven grain grading system, in accordance with the exemplary embodiment of the present invention;

Fig. 2a-b illustrates the grain analysis workflow from image capture to the classification and reporting stages within the system, in accordance with the exemplary embodiment of the present invention;

Fig. 3 illustrates the classification engine's detailed workflow, including pre-processing, feature extraction, and grain quality classification., in accordance with the exemplary embodiment of the present invention;

Fig. 4 illustrates the imaging unit's components, including sensors and lighting setups, facilitating the detection of visible and non-visible grain defects, in accordance with the exemplary embodiment of the present invention;

Fig. 5 illustrates the AI model's feature extraction and classification steps in the classification engine for accurate grain assessment, in accordance with the exemplary embodiment of the present invention;

Fig. 6 illustrates the data processing unit's report generation process, emphasizing real-time grading results, in accordance with the exemplary embodiment of the present invention;

Fig. 7 illustrates the user interface, highlighting real-time grading displays and customizable reporting functions, in accordance with the exemplary embodiment of the present invention.

Detailed Description of the Invention

The present disclosure emphasises that its application is not restricted to specific details of construction and component arrangement, as illustrated in the drawings. It is adaptable to various embodiments and implementations. The phraseology and terminology used should be regarded for descriptive purposes, not as limitations.

The terms "including," "comprising," or "having" and variations thereof are meant to encompass listed items and their equivalents, as well as additional items. The terms "a" and "an" do not denote quantity limitations but signify the presence of at least one of the referenced items. Terms like "first," "second," and "third" are used to distinguish elements without implying order, quantity, or importance.

According to the exemplary embodiment of the present invention, an AI-driven grain grading system that provides an automated, accurate, and efficient solution for grain quality assessment, addressing issues of subjectivity, time, and scalability found in traditional grading methods is disclosed. The system is designed to detect defects, contaminants, and foreign materials in grains by using advanced multispectral imaging and machine learning. With its high-resolution imaging unit and AI-powered classification engine, the system evaluates various parameters such as size, shape, texture, and contamination level to classify grains into quality categories.

The invention also includes a data processing unit for report generation, a cloud-based storage system for data access, a user interface for interactive results display, and an alert system for notifying stakeholders of quality issues. This innovative approach enables precise, real-time grading and decision-making across the agricultural supply chain, ensuring that grain quality meets both market and safety standards.

The AI-driven grain grading system comprises a high-resolution imaging unit that captures detailed images of individual grains. The imaging unit is designed to operate at multiple wavelengths, enabling the detection of both visible and invisible defects, such as surface damage, discoloration, Mold, and contaminants like dirt or foreign materials. This advanced imaging technology ensures that even minute defects, which may be missed by traditional optical methods, are accurately detected and analysed.

The core component of the system is the AI-powered classification engine. This engine uses machine learning algorithms trained on a vast dataset of grain images to classify grains based on their quality. The engine can detect surface defects, contaminants, and even differentiate between grains based on size and weight. The deep learning model continually improves over time, adapting to new data and enhancing its accuracy through user feedback, ensuring consistent and objective grading results.

The system includes a real-time data processing unit that generates instant grading reports. These reports provide detailed information about the quality of the grains, including any detected defects or contamination levels. The real-time nature of the system enables stakeholders across the agricultural supply chain to make timely and informed decisions regarding the marketing, storage, or export of the grains, reducing delays and bottlenecks commonly associated with manual inspection processes.

The cloud-based storage system facilitates data sharing and collaboration across multiple locations. Grain handlers, farmers, brokers, and quality control teams can access grading results remotely via the system's user interface. The cloud-based architecture also supports scalability, allowing the system to handle large volumes of grain samples simultaneously, making it ideal for use in large agricultural markets and grain processing plants.

The user interface is designed to be simple and intuitive, allowing users to easily navigate through grading results and generate customizable reports. The interface provides real-time alerts if contaminants or defects exceed predefined thresholds, enabling quick interventions. Additionally, the interface supports various filters, enabling users to sort and categorize grains based on specific quality metrics, which improves the decision-making process for all involved parties.

The system also includes an integrated alert system that notifies users when abnormal contaminant levels are detected or when grain quality falls below specified standards. This proactive feature ensures that stakeholders can take immediate corrective actions to preserve grain quality and reduce potential losses. The automation and real-time data processing significantly reduce the need for manual labour, resulting in cost savings, improved efficiency, and enhanced grain grading accuracy across the supply chain.

Now referring to the figures, Fig. 1a-b illustrates the block diagram of the AI-driven grain grading system (100) integrates multiple components that work in synergy to achieve high-precision grading. The system's core is the high-resolution multispectral imaging unit (102), which captures detailed images of individual grains. This imaging unit utilizes RGB and infrared sensors, capturing multispectral data across various wavelengths to identify visible and invisible defects, such as surface discoloration, microbial growth, mold, and other contaminants. The imaging unit (102) is positioned to ensure consistent, controlled lighting, facilitating the capture of high-quality images regardless of ambient lighting conditions.

These captured images are transmitted to the AI-powered classification engine (104), depicted in Figure 3. The classification engine (104) is equipped with a deep learning model trained on extensive datasets that include various grain quality parameters. By using advanced neural networks, the classification engine (104) processes each image to assess grain attributes like size, shape, surface texture, and the presence of foreign materials. The AI algorithms detect both subtle and pronounced defects, ensuring precise classification. User feedback can also be incorporated into the system, enabling the model to continuously refine its accuracy through adaptive learning.

Following the classification, the results are passed to the data processing unit (106), illustrated in Figure 6. This unit aggregates and analyzes the classification data, generating comprehensive real-time grading reports. These reports organize grains into categories such as "Good Quality," "Defective," and "Contaminated," providing clear metrics on defect density, contamination levels, and overall quality scores. The data processing unit (106) enables stakeholders to make rapid, data-driven decisions about grain batches, optimizing both time and resources in environments like bulk handling and food processing facilities.

The cloud-based storage system (108), as shown in Figure 5, is a secure, scalable infrastructure for storing grading data, allowing for multi-user access from remote locations. It provides secure, centralized storage that facilitates seamless data sharing among authorized stakeholders across the agricultural supply chain. This storage system enables real-time updates, so stakeholders can monitor grain quality assessments in real-time and make prompt decisions, regardless of geographic location.

The user interface (110), demonstrated in Figure 7, is designed for intuitive interaction with the system, allowing users to view grading results, generate custom reports, and navigate historical data. The interface includes a dashboard with visualizations of key performance indicators (KPIs), such as grain quality scores and contamination levels. Users can filter grading data by parameters like timeframe, grain type, and specific quality metrics, offering flexibility to accommodate different needs within the agricultural supply chain.

An alert system (112), illustrated in Figure 8, is embedded within the user interface (110) to notify users when contaminants or defects exceed predefined thresholds. This alert system enables users to receive notifications through in-app messages, emails, or SMS, ensuring they are informed in real-time. This system provides stakeholders with a proactive measure to address quality issues swiftly, mitigating potential losses or quality breaches in the supply chain.

The operation of the AI-driven grain grading system (100) begins with the image capture process, where grains pass through the high-resolution imaging unit (102). This imaging unit is equipped to capture multispectral images across various wavelengths, enabling the detection of both visible and hidden contaminants and defects. These wavelengths allow the system to detect imperfections that might not be visible in the standard light spectrum, such as microbial growth, internal damage, and certain discolorations. The imaging unit (102) is optimized to function under diverse lighting conditions, ensuring consistent, high-quality images regardless of environmental factors. This adaptability in lighting and wavelength use ensures that the captured images are detailed and precise, facilitating accurate and objective analysis in the subsequent stages of the grading process.

Once the images are captured, they are transmitted to the AI-powered classification engine (104) for processing. The classification engine (104) employs advanced machine learning algorithms, including neural networks, to analyze each image in depth. Key attributes such as size, shape, texture, and color are extracted from the images, which are then compared against a large, pre-existing dataset of grain quality metrics. This comparison allows the engine to assess the quality of each grain based on objective standards, identifying and categorizing grains into different quality bins such as "Good Quality," "Defective," or "Contaminated." This AI-driven classification process not only increases accuracy but also eliminates human subjectivity and bias, ensuring that each grain is graded consistently based on objective criteria and quality parameters.

The classification data for each grain is passed to the data processing unit (106), which aggregates the results and generates real-time grading reports. This processing unit compiles detailed data on quality indicators, including defect density, contamination levels, and overall quality scores for each batch of grains. By organizing this information into comprehensive reports, the data processing unit (106) enables stakeholders to quickly assess the quality of each batch and take appropriate actions. These real-time grading reports are particularly useful in high-volume environments, where quick decision-making is essential. The reports categorize grains into clear quality bins, simplifying the evaluation process and supporting seamless quality management for bulk handling and processing facilities.

Once the grading data is processed and compiled, it is stored in the cloud-based storage system (108), which offers secure and scalable storage options. This system enables remote access to the grading data, making it readily available to multiple stakeholders across the supply chain. The cloud-based nature of this storage allows stakeholders to access data in real-time from any location, providing a centralized platform for collaboration and decision-making. Additionally, this cloud infrastructure ensures that historical data is preserved for future reference, supporting traceability and compliance with regulatory standards. By keeping detailed records accessible to authorized users, the storage system aids in maintaining quality control and enables a thorough review of past performance data, which is valuable for audits, compliance, and optimization.

The alert system (112) is an essential component of the AI-driven grain grading system, designed to notify stakeholders when contaminants or defects exceed pre-set thresholds. This system is fully integrated with the user interface, allowing for instant notifications via multiple channels, including in-app alerts, email, and SMS. The alert system is customizable, enabling users to set specific thresholds based on the unique requirements of their operations. This flexibility ensures that stakeholders are informed of quality issues in real-time and can take immediate corrective actions when necessary. By providing proactive notifications, the alert system enhances the system's efficiency and responsiveness, minimizing potential losses and ensuring that only high-quality grains continue through the supply chain.

An example of the AI-driven grain grading system (100) in action includes its application in generating heat maps and productivity reports for batch analysis. For instance, in a high-capacity grain processing facility, the system can analyze large volumes of grains and generate a heat map displaying areas with higher defect densities. These heat maps provide a spatial representation of quality distribution within a batch, allowing operators to identify specific problem areas and adjust processing parameters accordingly. By targeting quality issues precisely, the system helps improve overall efficiency and reduces wastage in processing. Furthermore, these heat maps aid operators in prioritizing sections of the batch that require additional inspection or intervention, thus optimizing both quality and productivity.

The productivity reports generated by the data processing unit (106) offer a comprehensive view of the quality distribution within grain batches. These reports categorize the total grains inspected, displaying the percentages of grains falling into each quality category, such as "Good Quality," "Defective," or "Contaminated." Additionally, the productivity reports provide longitudinal data on quality trends over time, helping stakeholders to monitor and improve quality control efforts consistently. By analyzing these reports, users gain insights into factors affecting grain quality, which informs decisions related to storage, distribution, and pricing. The ability to track quality trends over time allows for predictive analysis, enabling proactive adjustments to maintain consistent quality and optimize operations in both the short and long term.

The AI-driven grain grading system (100) provides several key advantages, making it a highly effective solution for the agricultural supply chain:
• Objectivity and Consistency: The system's use of machine learning algorithms eliminates human bias, ensuring that all grains are assessed consistently and objectively. By adhering to an established dataset and predefined quality parameters, the system minimizes subjectivity and guarantees uniform grading across batches.
• Real-Time Analysis: The AI-driven grain grading system offers real-time data processing, allowing for rapid decision-making, which is especially valuable in high-throughput environments like grain processing facilities and storage centers. This real-time analysis ensures that quality control measures are immediate, minimizing delays and improving operational efficiency.
• Comprehensive Defect Detection: With its multispectral imaging capabilities, the system captures both visible and hidden contaminants and defects, filling a critical gap left by traditional optical or manual grading methods. This comprehensive defect detection capability ensures that microbial, internal, and surface defects are reliably identified and mitigated.
• Data Sharing and Collaboration: Through its cloud-based storage system (108), the AI-driven grain grading system enables seamless data sharing across stakeholders in the supply chain. This secure, remote access facilitates collaboration and ensures that all authorized parties have access to up-to-date grading data, streamlining quality control and fostering accountability.

The AI-driven grain grading system (100) has wide-ranging applications throughout the agricultural supply chain. Farmers can use it to evaluate crop quality before sale, gaining insights that inform pricing and market readiness. Grain storage facilities rely on the system for continuous quality monitoring, which helps prevent spoilage and ensures that stored grains maintain their quality over time. In grain processing plants, the system ensures that only high-quality grains are processed, enhancing the final product's safety and marketability. Additionally, exporters benefit from the system's ability to meet international quality standards, which is critical for maintaining market access and reducing the risk of rejections due to quality issues. The system's versatility makes it adaptable to various environments, supporting quality control efforts at multiple points within the agricultural sector.

Tests and Results
The AI-driven grain grading system was rigorously tested to ensure compliance with industry standards, including ISO 22000:2018 for food safety management and ISO 16000-27 for detection of contaminants. These tests assessed the system's accuracy in identifying microbial contaminants, foreign particles, and various defect types. The results demonstrated the system's high reliability, with an accuracy rate of over 98% in detecting and categorizing quality indicators across diverse grain samples. Furthermore, the system's performance in high-throughput operations was tested, confirming its ability to process up to 100 tons of grain per hour while maintaining precise quality assessment standards. These results underscore the system's suitability for large-scale operations and its effectiveness in delivering consistent, high-quality grading across different grain types.

In conclusion, the AI-driven grain grading system (100) stands out as a comprehensive, objective, and efficient solution for grain quality assessment. Its use of advanced imaging, AI-based classification, and real-time data processing ensures that stakeholders can rely on accurate, immediate insights into grain quality, empowering them to make informed decisions. The system's adaptability, tested accuracy, and real-time capabilities make it a valuable asset in today's agricultural industry, enhancing productivity, compliance, and overall food safety.
, Claims:5. CLAIMS
I/We Claim:
1. An AI-driven grain grading system (100) for automated quality assessment and real-time monitoring, comprising:
a. a high-resolution imaging unit (102) configured to capture images of individual grains;
b. an AI-powered classification engine (104) operatively connected to the imaging unit (102) comprising a machine learning model trained on grain quality data to classify grains based on parameters including size, shape, surface texture, defect type, and contamination level;
c. a data processing unit (106) linked to the classification engine (104) configured to aggregate and analyse classification results to generate real-time grading reports;
d. a cloud-based storage system (108), in communication with the data processing unit (106) configured to securely store grading data and provide access to multiple users;
e. a user interface (110) connected to the cloud-based storage system (108) configured to display grading results, offer customizable report generation and enable access to historical data for trend analysis;
f. an alert system (112) integrated within the user interface (110) configured to trigger notifications when contaminants or defects exceed predefined thresholds;
Characterized in that,
g. the high-resolution imaging unit (102) is a multispectral imaging unit capable of capturing images across multiple wavelengths, including non-visible wavelengths, to detect both visible and hidden contaminants and defects such as microbial infestations, internal discolorations, and mold not visible under standard lighting conditions; and
h. wherein the AI-powered classification engine (104) is specifically configured to process these multispectral images, leveraging machine learning algorithms trained to identify and classify grain quality issues based on non-visible spectral data.

2. The system (100) as claimed in claim 1, wherein the AI-powered classification engine (104) further comprises a deep learning model configured to improve classification accuracy continuously through adaptive learning based on user feedback, enabling dynamic updates to grain quality parameters.

3. The system (100) as claimed in claim 1, wherein the multispectral imaging unit (102) includes RGB and infrared sensors, configured to capture images under various lighting conditions and spectral ranges, thereby facilitating enhanced detection of both visible and invisible defects, including microbial growth and internal defects.

4. The system (100) as claimed in claim 1, wherein the data processing unit (106) is configured to compute and aggregate quality metrics such as defect density, contamination levels, and composite quality scores, providing a multi-dimensional evaluation of grain quality in real-time.

5. The system (100) as claimed in claim 1, wherein the cloud-based storage system (108) supports scalable data access, enabling asynchronous access to grading data for multiple stakeholders, thus facilitating remote quality control, collaborative decision-making, and trend analysis across the agricultural supply chain.

6. The system (100) as claimed in claim 1, wherein the user interface (110) includes an interactive dashboard, configured to generate customizable grading reports, visualize data through charts and graphs, and offer filters for quality metrics, timeframes, and grain types, tailored to specific user requirements.

7. The system (100) as claimed in claim 1, wherein the alert system (112) is configured to issue real-time notifications via multiple channels, including in-app alerts, email, and text messages, providing timely updates on quality breaches and enabling users to initiate immediate corrective actions.

8. The system (100) as claimed in claim 1, wherein the classification engine (104) employs a neural network architecture optimized for detecting defects in complex patterns, such as irregular surface textures and contamination clusters, enhancing accuracy in classifying grains with subtle quality variations.

9. The system (100) as claimed in claim 1, wherein the user interface (110) further comprises a data export module, configured to provide grading data in multiple formats compatible with external data management systems, allowing users to integrate the grading insights into their decision-support frameworks.

10. A method for automated grain grading using the system (100) as claimed in claim 1, comprising the steps of:
• capturing multispectral images of individual grains using the imaging unit (102) to detect both visible and hidden defects and contaminants;
• processing the captured images through the AI-powered classification engine (104), wherein machine learning algorithms analyze grain attributes, including size, shape, surface texture, and contamination level;
• aggregating classification results in real-time via the data processing unit (106) to generate detailed grading reports that categorize grains by quality and contamination metrics;
• storing grading data securely in the cloud-based storage system (108) for remote access by stakeholders;
• displaying grading results on the user interface (110), allowing users to generate customizable reports and view real-time grading data;
• triggering alerts through the alert system (112) when contaminant levels or defect thresholds are exceeded, enabling stakeholders to implement timely quality control actions.

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