Description:FORM 2
THE PATENTS ACT 1970
(39 of 1970)
&
The Patents Rules, 2003
PROVISIONAL SPECIFICATION
(See section 10 and rule 13)
TITLE OF THE INVENTION: CALIBRATION SYSTEM AND METHOD FOR GRAIN QUALITY ANALYZER
2. APPLICANT:
(a) Name : UPJAO AGROTECH PRIVATE LIMITED
(b) Nationality : INDIAN
(c) Address : E-56, First Floor,
GIDC Electronics Estate,
Sector 26, Gandhinagar 382 028
Gujarat, INDIA.
PROVISIONAL
The following specification describes the invention.  COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed.

FIELD OF INVENTION
[1] The present disclosure relates to calibration systems for automated grain quality analysis equipment, and more particularly to a calibration system, method for ensuring consistent and accurate performance of grain quality analyzers that utilize imaging systems and machine learning models to assess grain characteristics.
BACKGROUND OF THE INVENTION
[2] Grain quality assessment has conventionally necessitated manual examination and laboratory-based analytical methodologies to evaluate characteristics encompassing dimensional parameters, morphological attributes, chromatic properties, moisture content, and the manifestation of defects or extraneous materials. With technological advancements in computer vision and artificial intelligence paradigms, automated grain quality analyzers have emerged that can expeditiously evaluate grain specimens utilizing imaging systems and machine learning models. These automated systems characteristically employ one or more cameras to acquire images of grain samples, which are subsequently processed using
[3] These automated systems typically employ computer vision techniques and image processing methods to analyze grain samples. By capturing high-resolution images of grain kernels, these analyzers can assess various quality parameters such as size, shape, color, and the presence of defects. This approach offers the potential for more consistent and objective evaluations compared to manual inspection methods.
[4] However, the implementation of automated grain quality analyzers presents several technical challenges. One issue is maintaining consistent performance across different devices and over time. Environmental factors such as lighting conditions, temperature, and humidity can affect image acquisition, potentially leading to variations in analysis results. Additionally, mechanical components of the analyzer, such as sample feeders and positioning mechanisms, may experience wear or misalignment over time, further impacting the consistency of measurements.
[5] Another challenge lies in ensuring the accuracy and reliability of the artificial intelligence models used for grain quality assessment. These models require careful calibration and periodic validation to maintain their performance. Changes in grain characteristics due to seasonal variations, new crop varieties, or regional differences can potentially affect the model's accuracy if not properly accounted for.
[6] Furthermore, the integration of multiple imaging perspectives, such as top and bottom views of grain samples, introduces complexities in aligning and correlating data from different camera angles. Ensuring precise synchronization and mapping between these views is essential for comprehensive grain analysis but can be technically demanding.
[7] Prior art U.S. Patent No. 6,239,554 discloses a computer-based system for analyzing images of agricultural products. However, this system lacks comprehensive calibration mechanisms to ensure consistent performance across multiple devices. Unlike the '554 patent, the present invention provides a standardized calibration system utilizing specialized targets with concentric colored rings and radial lines that enable precise validation of camera positioning, focus quality, and color accuracy. The present invention further implements a systematic validation workflow that addresses mechanical variations, lighting inconsistencies, and image processing parameters that the prior art fails to adequately address.
[8] U.S. Patent Publication No. US20080309968 describes a system for analyzing and grading agricultural products using image processing techniques. The system captures images of grain samples and processes them to determine various quality parameters. However, the system described in US20080309968 may not fully address the challenges of maintaining consistent calibration across multiple devices and varying environmental conditions. In contrast, the present invention provides a comprehensive calibration system that utilizes specialized targets with concentric colored rings and radial lines. This approach may enable more precise validation of camera positioning, focus quality, and color accuracy across different devices and operating environments. Additionally, the present invention may implement a systematic validation workflow that addresses mechanical variations, lighting inconsistencies, and image processing parameters, potentially offering improved reliability and consistency in grain quality analysis compared to the prior art.
[9] The present invention addresses a critical need of the industry for highly accurate, consistent, and reliable grain quality analysis across diverse environments and equipment setups. This calibration system may overcome limitations of existing technologies by accounting for variations in camera positioning, lighting conditions, and mechanical components that can affect analysis results. It has been appreciated that a calibration system is needed that overcomes one or more of these deficiencies.
SUMMARY OF THE INVENTION
[10] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description.
[11] A calibration system for a grain quality analyzer comprises an imaging system configured to capture images of grain samples and reference objects, a sample positioning system, a processing unit configured to execute calibration procedures, and synthetic calibration objects comprising polymeric, metallic, ceramic, or composite materials designed to mimic grain kernels with predetermined physical characteristics. The system enables consistent performance validation across multiple analyzer units by providing standardized reference materials that maintain calibration stability over extended operational periods. The calibration system further comprises a storage medium having instructions that cause the processing unit to perform calibration operations including positioning synthetic calibration objects, capturing images, processing the images to determine performance parameters, and comparing the parameters with reference values to validate calibration status. The calibration system enables automated execution of calibration procedures, reducing dependence on manual intervention while ensuring consistent calibration practices across different installations. A method for calibrating a grain quality analyzer comprises positioning synthetic calibration objects within a field of view of an imaging system, capturing images of the synthetic calibration objects, processing the captured images to determine performance parameters, and comparing the determined performance parameters with predetermined reference values to validate calibration status. The method enables automated calibration validation without dependence on variable natural grain samples, providing consistent and repeatable calibration procedures that can be standardized across multiple analyzer installations.
OBJECTS OF THE INVENTION
[12] The primary object of the present invention is to provide a calibration system for grain quality analyzers that ensure consistent and accurate performance over extended operational periods.
[13] Another object of the present invention is to provide synthetic calibration objects comprising polymeric, metallic, ceramic, or composite materials that mimic grain kernels with predetermined physical characteristics, thereby eliminating variability associated with natural grain samples.
[14] Further object of the present invention is to provide a calibration system that enables standardization across multiple analyzer units deployed in different locations or facilities.
[15] Yet another object of the present invention is to provide automated calibration procedures that reduce dependence on manual intervention and specialized expertise.
[16] An additional object of the present invention is to provide reference objects with predefined dimensions, shapes, or patterns that establish metrological standards and scaling coefficients for dimensional measurements.
[17] Another object of the present invention is to provide illumination uniformity analysis and detecting variations beyond predetermined thresholds.
[18] Further object of the present invention is to provide machine learning model validation using synthetic calibration objects as test inputs to maintain performance standards.
[19] Yet another object of the present invention is to provide multi-perspective calibration capabilities through multiple imaging devices including rotating cameras, depth sensors, or three-dimensional scanners.
[20] An additional object of the present invention is to provide cloud-based synchronization of calibration data across distributed analyzer networks for fleet-wide standardization.
[21] Another object of the present invention is to provide blockchain-based calibration record management for auditability and traceability of calibration procedures.
[22] Further object of the present invention is to provide printed reference images containing predefined grain patterns and defect representations for model validation testing.
[23] Yet another object of the present invention is to provide automated parameter adjustment capabilities and system-generated alerts for maintaining optimal calibration status.
[24] Another object of the present invention is to provide comprehensive calibration parameter frameworks that encompass morphological, chromatic, textural, and dimensional reference standards.
[25] BRIEF DESCRIPTION OF FIGURES
[26] FIG. 1 illustrates a block diagram of a centralized calibration system for grain quality analyzers, according to aspects of the present disclosure.
[27] FIG. 2 illustrates a flowchart for a calibration method for grain quality analyzers, according to aspects of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[28] As used herein, the term "calibration system" may refer to a comprehensive framework of hardware, software, and methodological components designed to validate, maintain, and standardize the performance of grain quality analyzers through systematic testing and adjustment procedures.
[29] As used herein, the term "grain quality analyzer" may refer to an automated system that utilizes imaging technology, computer vision techniques, and machine learning models to assess physical and visual characteristics of grain samples, including but not limited to dimensional parameters, morphological attributes, color properties, and defect identification.
[30] As used herein, the term "synthetic calibration objects" may refer to artificially manufactured reference materials designed to replicate the physical, dimensional, and visual characteristics of natural grain kernels while providing consistent and stable properties that are not subject to environmental variation, moisture changes, or biological degradation.
[31] As used herein, the term "imaging system" may refer to one or more cameras, illumination sources, optical components, and associated hardware configured to capture digital images of grain samples and calibration objects for subsequent analysis and processing.
[32] As used herein, the term "processing unit" may refer to computational hardware including processors, memory components, and specialized processing elements configured to execute image processing methods, machine learning models, and calibration procedures.
[33] As used herein, the term "reference objects" may refer to precision-manufactured items with predetermined and certified physical characteristics, including dimensions, shapes, colors, and patterns, that serve as known standards for measurement validation and system calibration.
[34] As used herein, the term "performance parameters" may refer to quantitative metrics that characterize the operational accuracy and consistency of grain quality analyzers, including dimensional measurement precision, color accuracy, shape recognition performance, and defect detection reliability.
[35] As used herein, the term "machine learning models" may refer to artificial intelligence systems trained to recognize patterns, classify objects, and extract features from grain images, including but not limited to neural networks, support vector machines, and ensemble methods.
[36] As used herein, the term "sample positioning system" may refer to mechanical components and control mechanisms that position grain samples and calibration objects within the field of view of imaging systems, including feeders, conveyors, vibrators, and positioning stages.
[37] As used herein, the term "fleet-wide standardization" may refer to the coordination of calibration procedures and performance standards across multiple grain quality analyzer units deployed in different locations to ensure consistent and comparable analysis results.
[38] As used herein, the term "predetermined values" or "predetermined reference values" may refer to established baseline measurements, specifications, or performance thresholds that are defined during initial system characterization using traceable measurement standards and certified reference materials, which serve as comparison benchmarks for validating calibration accuracy and system performance during routine calibration procedures.
[39] The present disclosure relates to calibration systems and methods for grain quality analyzers that utilize computer vision and artificial intelligence techniques to assess grain quality characteristics. Grain quality analyzers have become increasingly utilized in agricultural and food processing applications to automate and enhance the precision of grain quality assessment. However, consistent performance of such analyzers relies on the accuracy and repeatability of image acquisition hardware, environmental conditions, and processing methods.
[40] Over time, various factors can introduce variance in analyzer results, affecting reliability and consistency. Changes in lighting conditions may occur due to LED degradation, ambient light fluctuations, or dust accumulation on illumination sources, resulting in altered spectral characteristics and intensity variations that affect color measurement accuracy. Camera performance degradation can manifest through sensor drift, lens contamination, mechanical misalignment, or electronic component aging, leading to reduced image sharpness, color accuracy deviations, and geometric distortion. Sample positioning variations may arise from mechanical wear in vibrators, feeders, or tray mechanisms, causing inconsistent grain placement within the imaging field of view and affecting measurement repeatability. Environmental factors including temperature fluctuations, humidity changes, vibration from nearby equipment, and particulate contamination can impact both hardware performance and image quality, introducing systematic errors that compromise analysis accuracy. Additionally, when multiple analyzer units are deployed across different locations, maintaining consistent performance standards between units presents additional challenges related to hardware tolerances, installation variations, environmental differences, and operational practices that can lead to divergent results for identical grain specimens.
[41] Referring now to FIG. 1, there is illustrated a block diagram of a centralized calibration system (100) for grain quality analyzers according to aspects of the present disclosure. The centralized calibration system (100) comprises an imaging system (102) configured to capture high-resolution images of grain samples and calibration objects during analysis and validation procedures. The imaging system (102) is operatively connected to a sample positioning system (104) that positions grain samples and synthetic calibration objects within the field of view of the imaging system (102). The sample positioning system (104) includes mechanical components such as feeders, conveyors, vibrators, and positioning stages that ensure consistent and repeatable placement of samples for imaging.
[42] The centralized calibration system (100) further includes a processing unit (106) that serves as the central computational component for executing calibration methods, image processing procedures, and machine learning models. The processing unit (106) receives image data from the imaging system (102) and coordinates the overall operation of the calibration system (100). The processing unit (106) comprises high-performance processors, memory components, and specialized processing elements configured to handle computationally intensive image processing tasks, pattern recognition methods, and statistical data analysis procedures required for comprehensive calibration validation.
[43] The centralized calibration system (100) incorporates synthetic objects (108) comprising polymeric, metallic, ceramic, or composite materials designed to mimic grain kernels with predetermined physical characteristics. The synthetic objects (108) are positioned by the sample positioning system (104) and provide stable reference standards that maintain calibration consistency over extended operational periods without being subject to environmental variation, moisture changes, or biological degradation that would affect natural grain samples. The synthetic objects (108) enable standardized calibration procedures across multiple analyzer installations by providing consistent and repeatable reference materials.
[44] The processing unit (106) is operatively connected to a cloud synchronization system (110) through bidirectional communication pathways that enable secure data exchange and fleet-wide calibration management. The cloud synchronization system (110) facilitates centralized storage, processing, and distribution of calibration data across multiple grain quality analyzer deployments using secure cloud infrastructure with redundant data storage and high-availability computing resources. The cloud synchronization system (110) is configured to operate in cloud-based mode for distributed fleet management or local compute mode for standalone operation. The cloud synchronization system (110) enables synchronization of calibration parameters, reference measurements, and performance data between individual analyzers and the central cloud system, providing fleet-wide updates and deviation tracking capabilities.
[45] The centralized calibration system (100) includes a user interface (112) that provides operator interaction capabilities for controlling and monitoring calibration procedures. The user interface (112) is connected to both the processing unit (106) and the cloud synchronization system (110), enabling local and remote access to calibration functions. The user interface (112) provides comprehensive visualization of calibration status, performance trends, and system health indicators through customizable display layouts that accommodate different user roles and access levels, including real-time monitoring capabilities and historical trend analysis.
[46] The centralized calibration system (100) further incorporates a data alert system (114) that provides automated notification functions regarding calibration status and system performance. The data alert system (114) is operatively connected to the processing unit (106) and receives supplementary communication from the cloud synchronization system (110) through dashed connection pathways, indicating additional communication channels for distributed system management. The data alert system (114) provides system-generated alerts including automated notifications of calibration status changes, performance deviations, maintenance requirements, and environmental conditions that may affect analyzer performance, with configurable alert thresholds and multiple notification methods.
[47] The system architecture illustrated in FIG. 1 enables coordinated operation of the imaging system (102), sample positioning system (104), processing unit (106), synthetic objects (108), cloud synchronization system (110), user interface (112), and data alert system (114) to achieve comprehensive calibration validation across multiple grain quality analyzer deployments. The interconnected components work together to provide systematic hardware component verification, environmental condition assessment, and performance monitoring, while enabling fleet-wide standardization and centralized monitoring of analyzer performance through the cloud synchronization system (110).
[48] Referring now to FIG. 2, there is illustrated a flowchart for a calibration method for grain quality analyzers using synthetic calibration objects according to aspects of the present disclosure. The calibration method begins at step (200) where synthetic calibration objects are positioned within the imaging field of the analyzer system using the sample positioning system (104). The synthetic calibration objects are precisely placed to ensure optimal visibility and measurement accuracy within the field of view of the imaging system (102). The positioning process includes placement of the synthetic objects (108) at predetermined locations that enable comprehensive evaluation of imaging system performance, dimensional measurement accuracy, and color calibration validation.
[49] The method proceeds to step (202) where images are captured using the imaging system (102) under controlled conditions that replicate normal operational parameters. The image capture process includes acquisition of high-resolution digital images with standardized illumination conditions, camera settings, and environmental parameters to ensure consistent and repeatable calibration measurements. The imaging system (102) captures multiple images of the positioned synthetic calibration objects to enable statistical analysis of measurement repeatability and identification of any systematic variations in image quality or measurement accuracy.
[50] Following image capture, the method advances to step (204) where the captured images are processed by the processing unit (106) to determine performance parameters of the analyzer system. The image processing procedures include dimensional measurements of the synthetic calibration objects to verify scaling accuracy and geometric measurement precision, color analysis to assess chromatic accuracy and illumination uniformity, shape recognition to validate morphological analysis capabilities, and texture analysis to confirm surface characteristic detection performance. The processing unit (106) executes machine learning models and computer vision techniques to extract quantitative performance metrics that characterize the current operational status of the grain quality analyzer.
[51] The method continues to step (206) where the determined performance parameters are compared with predetermined reference values to assess calibration accuracy and system performance. The comparison process involves statistical analysis of measured values against certified reference standards, calculation of measurement deviations and uncertainty estimates, and evaluation of performance trends over time to identify gradual degradation or systematic drift. The predetermined reference values are established through initial system characterization using traceable measurement standards and are periodically updated to maintain calibration accuracy as system components age or environmental conditions change.
[52] The process then reaches decision point (208) which evaluates whether the measured performance parameters meet established calibration standards and acceptance criteria. The decision logic incorporates multiple performance metrics including dimensional measurement accuracy within specified tolerances, color measurement precision within acceptable limits, shape recognition performance above minimum thresholds, and overall system performance indicators that demonstrate satisfactory operational status. The evaluation process considers both individual parameter compliance and overall system performance to ensure comprehensive calibration validation.
[53] If the parameters meet the calibration standards (Yes branch), the method proceeds to step (210) where the calibration status is validated as acceptable, indicating that the grain quality analyzer is properly calibrated and ready for operational use. The validation process includes documentation of calibration results, updating of calibration records with timestamp and performance data, and notification to operators and management systems that the analyzer has successfully completed calibration validation. The validated calibration status enables continued operation of the grain quality analyzer with confidence in measurement accuracy and consistency.
[54] If the parameters do not meet the calibration standards (No branch), the method proceeds to step (212) where calibration adjustment recommendations are generated to address the identified performance deviations. The adjustment recommendations include specific corrective actions such as camera focus adjustments, illumination intensity modifications, mechanical component realignment, or software parameter updates that are calculated to restore system performance to acceptable levels. The recommendations may include automated parameter adjustments where possible, or detailed instructions for manual adjustments by qualified technicians, along with priority levels and estimated completion times for the recommended corrective actions.
[55] The flowchart illustrated in FIG. 2 demonstrates a systematic approach to calibration validation that utilizes synthetic calibration objects (108) to provide consistent reference standards for performance assessment across multiple grain quality analyzer installations. The decision-making structure at step (208) enables automated determination of calibration status and appropriate response actions based on measured performance parameters, while the comprehensive workflow ensures thorough evaluation of all critical system components and performance characteristics that affect grain quality analysis accuracy and reliability.
[56] Synthetic calibration objects (108) provide stable reference standards designed to mimic the physical and visual characteristics of natural grain kernels while maintaining consistent properties that are not subject to environmental variation, moisture changes, biological degradation, or seasonal variations that would compromise calibration consistency over time. The synthetic calibration objects (108) are suited for high precision calibration which is necessary for automated calibration procedures. The synthetic calibration objects (108) comprise various material compositions including polymeric materials that provide controlled density characteristics matching natural grain densities, surface texture properties that replicate the tactile and visual characteristics of grain surfaces, and flexibility or hardness properties that simulate mechanical behavior of natural grains during handling and processing. Metallic materials provide enhanced dimensional stability with thermal expansion coefficients that minimize size changes across temperature variations, durability under repeated handling and cleaning procedures, and corrosion resistance for long-term stability in various environmental conditions. Ceramic materials offer high dimensional accuracy or better, thermal stability across wide temperature ranges without dimensional changes, and chemical inertness that prevents degradation from exposure to cleaning agents or environmental contaminants. Composite materials that combine multiple material properties within individual calibration elements may include polymer matrices with embedded particles for controlled density, multi-layer constructions with different surface and core properties, or gradient materials with varying properties across the calibration object to simulate complex grain characteristics.
[57] Manufacturing methods for synthetic calibration objects (108) include precision molding processes such as injection molding, compression molding, or transfer molding for precise dimensional control that replicates natural grain surface textures, pore structures, and morphological features. Three-dimensional printing processes enable production of complex internal structures such as hollow cavities, internal channels, or gradient density distributions, and intricate surface features including detailed texture patterns, geometric features, or encoded identification markers. Coating processes may be applied to achieve specific surface properties including color matching to natural grain specimens, reflectance characteristics that replicate spectral properties of grain surfaces, texture modifications for tactile properties, or protective coatings for enhanced durability and cleaning resistance. Multiple coating layers may be applied through spray coating, dip coating, or vapor deposition processes to achieve complex surface properties that replicate natural grain kernel variations including color gradients, surface roughness variations, or multi-spectral reflectance characteristics that simulate the optical properties of different grain varieties or quality conditions.
[58] A master sample set provides comprehensive calibration reference standards that encompass the range of grain characteristics encountered in typical analysis applications across different grain types, quality grades, and seasonal variations. The master sample set consists of N grains per set, where N may range from 50 to 500 or more depending on the complexity of the analysis application and the desired statistical confidence in calibration results, with structured variation across multiple grain characteristic parameters to ensure comprehensive coverage of the measurement space. Shape variations replicate geometric diversity found in natural grain populations including elongated kernels, round kernels, irregular shapes, and kernels with surface defects or damage, with shape parameters quantified through aspect ratios, circularity measures, and surface curvature characteristics. Size variations encompass dimensional ranges from small to large kernels within each grain type, with length, width, and thickness dimensions spanning the natural distribution found in commercial grain samples, and volume variations that affect weight-based measurements and density calculations. Color variations replicate spectral characteristics across the visible and near-infrared spectrum, including variations in hue, saturation, and brightness that correspond to different grain varieties, maturity levels, and quality conditions, with color standards traceable to international color measurement standards. Density variations accommodate different grain types and moisture contents that affect weight-based analysis methods, with density values ranging from typical minimum to maximum values found in commercial grain samples. Texture variations replicate surface characteristics observed across different grain types and conditions including smooth surfaces, rough surfaces, pitted surfaces, and surfaces with various types of damage or contamination, with texture parameters quantified through surface roughness measurements and statistical texture analysis.
[59] Reference objects provide dimensional calibration and scaling verification capabilities with predefined dimensions within a range of 5-30 mm to accommodate various calibration requirements and grain size categories, with dimensional accuracy certified through traceable measurement standards and periodic verification procedures. Reference objects may include 2D coded markers that incorporate dimensional specifications, calibration parameters, manufacturing dates, and traceability information within encoded data structures that can be automatically read by the analyzer system for identification and parameter retrieval. Shade cards for color and reflectance calibration provide certified color standards with known spectral reflectance properties across visible and near-infrared wavelengths, enabling accurate color measurement calibration and verification of chromatic analysis methods. Patterned plates for geometric calibration feature precisely defined geometric features including circles, squares, rectangles, and polygons with certified dimensions and positional accuracy, enabling verification of shape recognition methods and dimensional measurement accuracy. Planar calibration discs may serve as convenient reference objects for basic dimensional calibration procedures, providing readily available circular references with standardized dimensions, though with limited accuracy compared to precision-manufactured calibration objects. Reference materials such as single or multi-tone or colour background enable lighting uniformity verification by providing uniform, low-reflectance surfaces that reveal illumination variations, hot spots, or shadows in the imaging system (102). Surface-applied reference calibration marker with precisely defined diameters serve as geometric reference standards for basic dimensional calibration and can be applied to various surfaces or substrates as needed. Auto-detectable reference markers incorporate machine-readable identification capabilities such as barcodes, 2D codes, or encoded patterns that enable automated calibration procedures through automatic recognition, positioning, and parameter retrieval without manual operator intervention. The reference calibration objects are described herein only for the illustrative purpose.
[60] A processing unit (106) executes machine learning models and calibration procedures, comprising high-performance processors such as multi-core CPUs, graphics processing units (GPUs) for parallel computation, or specialized AI accelerators for machine learning inference, memory components including RAM for active data processing and storage devices for calibration data and model storage, and specialized processing hardware configured to handle computationally intensive image processing tasks, pattern recognition methods, and statistical data analysis procedures. The processing unit (106) for image classification, objects detection, and feature extraction from grain images. Training datasets may incorporate reference datasets of synthetic kernels to provide consistent and controlled training inputs without reliance on natural grain samples that may exhibit seasonal variations, quality inconsistencies, or availability limitations, with training data augmentation techniques to increase dataset diversity and model robustness.
[61] Self-calibrating artificial intelligence capabilities enable automated model adaptation and performance optimization without manual intervention through continuous learning methods that adapt to changing conditions, automated hyperparameter optimization that adjusts model parameters for optimal performance, and performance monitoring systems that detect model drift or accuracy degradation. The self-calibrating AI may automatically retrain using reference kernels and printed defects to maintain accuracy as system conditions change over time due to hardware aging, environmental variations, or changes in grain characteristics, with automated validation procedures that verify model performance after retraining and rollback capabilities that restore previous model versions if performance degrades. Federated learning enable collaborative training across multiple analyzer devices while maintaining data privacy through distributed learning methods that allow individual analyzers to contribute calibration data and model improvements to a shared cloud-based model without transmitting raw image data or sensitive operational information. This approach enables continuous model improvement through collective learning from multiple installations while preserving data confidentiality and reducing bandwidth requirements for data transmission.
[62] Adaptive thresholding capabilities enable automatic adjustment of analysis parameters based on seasonal grain changes, environmental variations, and evolving quality standards through dynamic threshold adjustment methods that modify classification boundaries based on observed grain characteristics, statistical analysis of measurement distributions that identify shifts in grain properties over time, and machine learning approaches that learn optimal threshold values from historical data and performance feedback. The adaptive thresholding system may monitor grain characteristics over time including size distributions, color variations, moisture content changes, and defect frequencies, and automatically adjust classification boundaries for quality grades, detection thresholds for defects or foreign materials, measurement parameters for dimensional analysis, or color space boundaries for chromatic classification to maintain consistent analysis accuracy despite natural variations in grain properties due to seasonal changes, variety differences, or storage conditions.
[63] Automated calibration mechanisms enable grain quality analyzer systems to perform calibration procedures with reduced manual intervention through motorized positioning systems and intelligent control methods that coordinate calibration activities without operator involvement.
[64] User interface (112) automation features include automated parameter adjustments that optimize system settings based on calibration measurements and environmental conditions through intelligent methods that analyze calibration results and automatically modify imaging parameters, analysis thresholds, or processing settings to maintain optimal performance. System-generated alerts provide automated notification of calibration status and performance issues through configurable alert systems that monitor key performance indicators, threshold-based notifications that trigger when performance metrics exceed acceptable ranges and predictive alerts that warn of potential issues before they affect analysis accuracy. Dashboard interfaces provide comprehensive visualization of calibration status, performance trends, and system health indicators with customizable display layouts that accommodate different user roles and information requirements, real-time monitoring capabilities that show current system status and recent performance metrics, historical trend analysis that identifies long-term performance patterns and degradation indicators, and multi-level access control that ensures appropriate information access for operators, maintenance technicians, quality managers, and system administrators with role-based permissions and audit trails for security and compliance requirements.
[65] Cloud-based synchronization systems (110) enable coordinated calibration management across multiple grain quality analyzer deployments through centralized storage, processing, and distribution of calibration data using secure cloud infrastructure with redundant data storage, high-availability computing resources, and scalable bandwidth for supporting large numbers of connected analyzers. The cloud system (110) enables synchronization of calibration data across multiple analyzers through automated data exchange protocols that securely transmit calibration parameters, reference measurements, and performance data between analyzers and the central cloud system, with data encryption for security and compression for efficient transmission. Fleet-wide updates ensure consistent calibration standards across all connected analyzers through centralized distribution of calibration parameter updates, improvements, or new reference standards, with version control systems that track calibration changes and enable rollback if issues arise. Deviation tracking capabilities monitor calibration performance trends and identify analyzer units exhibiting performance drift through statistical analysis of calibration data across the fleet, automated detection of outlier performance, and predictive analytics that identify analyzers likely to require maintenance or recalibration based on performance trends and usage patterns.
[66] Blockchain-based recording systems provide secure and auditable logging of calibration procedures and performance data through distributed ledger technology that creates immutable records of calibration activities, measurement results, and system modifications with cryptographic verification of data integrity and tamper-evident storage that prevents unauthorized modification of calibration records. The blockchain approach ensures data integrity through cryptographic hashing of calibration records, distributed consensus mechanisms that verify the validity of calibration data entries, and immutable storage that prevents retroactive modification of historical calibration records. This system provides enhanced auditability for trading and procurement applications where grain quality documentation requires verification and traceability through complete audit trails of calibration procedures, traceable measurement standards, and verified performance data that can be independently validated by third parties. Smart contracts may automate compliance checking and alert generation based on calibration status and performance metrics.
[67] Calibration protocols establish systematic procedures for maintaining grain quality analyzer performance through scheduled validation activities and reference standard management with documented procedures that ensure consistent calibration practices across different operators and installations. Periodic replacement schedules provide structured maintenance of synthetic calibration objects (108) based on usage frequency with replacement intervals determined by the number of calibration cycles performed, time intervals such as monthly, quarterly, or annual replacement schedules regardless of usage, or performance degradation indicators that trigger replacement when calibration objects show signs of wear, damage, or dimensional changes that could affect calibration accuracy. Batch validation processes ensure consistent performance characteristics across multiple synthetic calibration objects (108) within production batches through dimensional verification using precision measurement equipment to confirm that all objects meet specified tolerances, color accuracy assessment using spectrophotometry to verify that color properties match reference standards, surface property evaluation including texture analysis and visual inspection to confirm surface quality, and material composition analysis to verify that material properties meet specifications for density, hardness, and other relevant characteristics.
[68] Transparent sheets containing predefined patterns provide calibration verification capabilities across multiple camera perspectives within multi-camera analyzer configurations through optically clear substrates such as glass or transparent polymers that maintain pattern visibility from multiple viewing angles while preserving geometric accuracy and dimensional stability. The transparent sheets enable simultaneous calibration validation from multiple imaging angles through patterns that remain visible and measurable from different camera positions, geometric features that provide consistent reference points regardless of viewing angle, and optical properties that minimize distortion or refraction effects that could affect measurement accuracy. Predefined patterns may include geometric shapes such as circles, squares, rectangles, and polygons with precisely defined dimensions and positional relationships, dimensional references including rulers, grids, or measurement scales for scaling verification, grid structures with regular spacing for geometric distortion assessment, or encoded markers such as 2D codes or barcodes that provide identification and parameter information. These patterns provide comprehensive calibration validation capabilities including dimensional accuracy verification, geometric distortion assessment, and multi-perspective calibration confirmation to verify measurement consistency across multiple camera viewpoints and ensure that all cameras in a multi-camera system provide consistent and accurate measurements.
[69] System integration within grain quality analyzer calibration systems enables coordinated operation of multiple components to achieve comprehensive performance validation and consistent analysis results through unified control systems that coordinate imaging systems (102), mechanical components, processing units (106), and data management systems. The integrated approach combines imaging systems (102) including cameras, illumination, optical components, synthetic calibration objects (108) and reference standards with known characteristics, processing units (106) with machine learning models and analysis software, and data management systems for calibration data storage and synchronization into unified calibration workflows that systematically address hardware verification through testing of imaging system (102) performance and mechanical component accuracy, environmental assessment through monitoring of temperature, humidity, and lighting conditions, and validation through testing of machine learning models and image processing methods. Integration protocols ensure systematic execution of calibration procedures through automated scheduling of calibration activities, coordinated operation of multiple system components, and standardized data collection and analysis procedures, while enabling effective utilization of performance data across analyzer deployments through centralized data management, fleet-wide performance monitoring, and coordinated maintenance scheduling based on performance trends and predictive analytics.
[70] Features of any of the examples or embodiments outlined above may be combined to create additional examples or embodiments without losing the intended effect. It should be understood that the description of an embodiment or example provided above is by way of example only, and various modifications could be made by one skilled in the art. Furthermore, one skilled in the art will recognise that numerous further modifications and combinations of various aspects are possible. Accordingly, the described aspects are intended to encompass all such alterations, modifications, and variations that fall within the scope of the appended claims.

, Claims:
We Claims:
1. A calibration system (100) for a grain quality analyzer, the calibration system (100) comprising:
at least one imaging system (102) configured to capture digital images of grain samples and calibration objects; synthetic calibration objects (108) designed to mimic grain kernels with predetermined physical characteristics including size, shape, color, density; a sample positioning system (104) configured to position the synthetic calibration objects (108) within a field of view of the imaging system (102) in a repeatable manner; a processing unit (106) configured to process captured images and execute calibration procedures and statistical data analysis procedures; a cloud synchronization system (110) operatively connected to the processing unit (106) and configured to enable fleet-wide calibration management across multiple grain quality analyzers through secure data exchange and centralized storage of calibration data, wherein the cloud synchronization system (110) is configured to operate in cloud-based mode for distributed fleet management or local compute mode for standalone operation; a user interface (112) connected to the processing unit (106) and the cloud synchronization system (110), configured to provide operator interaction capabilities for controlling and monitoring calibration procedures with comprehensive visualization of calibration status and performance; and a data alert system (114) operatively connected to the processing unit (106) and configured to provide automated notification functions regarding calibration status and system performance through system-generated alerts and configurable alert thresholds.
2. The calibration system (100) as claimed in claim 1, wherein the synthetic calibration objects (108) is selected from polymeric, metallic, ceramic or composite materials designed to mimic grain kernels with predetermined physical characteristics including controlled density, surface texture, dimensional stability, and thermal properties.
3. The calibration system (100) as claimed in claim 1, wherein the synthetic calibration objects (108) includes reference objects selected but not limited to 2D codes, shade cards, patterned plates, auto-detectable reference markers, planar calibration discs, reference color backgrounds, precision-manufactured geometric standards, certified dimensional references, and transparent sheets.
4. The calibration system (100) as claimed in claim 1, wherein the cloud synchronization system (110) is configured to secure cloud infrastructure with data storage and to synchronize calibration parameters, reference measurements, and performance data between individual analyzers and a central cloud system through automated data exchange protocols with data encryption and compression capabilities.
5. The calibration system (100) as claimed in claim 1, wherein the user interface (112) is configured to display customizable layouts that accommodate different user roles and access levels, real-time monitoring capabilities for current system status, and historical analysis for identifying long-term performance patterns and degradation indicators.
6. The calibration system (100) as claimed in claim 1, wherein the data alert system (114) is configured to alert systems that monitor key performance indicators, threshold-based notifications that trigger when performance metrics exceed acceptable ranges and predictive alerts that warn of potential issues before they affect analysis accuracy.
7. A method for calibrating a grain quality analyzer using the calibration system (100), the method comprising the steps of:
positioning (200) synthetic calibration objects (108) within a field of view of an imaging system (102) to mimic grain kernels with predetermined physical characteristics;
capturing (202) high-resolution digital images of said objects by imaging system (102) under standardized illumination conditions;
processing (204) said captured images through a processing unit (106) to determine performance parameters including dimensional measurements, color analysis, shape recognition, and texture analysis;
comparing (206) said determined performance parameters with predetermined reference values to assess calibration accuracy and system performance; and
determining (208) calibration status based on said comparison.
8. The method as claimed in claim 8, wherein the method includes generating (212) calibration adjustment recommendations when said performance parameters fail to meet said calibration standards.
9. The method as claimed in claim 8, wherein the method includes synchronizing calibration data across multiple grain quality analyzers via a cloud-based system (110) to automate data exchange protocols with data encryption and compression capabilities for secure transmission of calibration parameters, reference measurements, and performance data.
10.The method as claimed in claim 8, wherein the method includes implementing adaptive thresholding procedures that automatically adjust analysis parameters based on seasonal grain changes, environmental variations, and evolving quality standards through dynamic threshold adjustment methods.

Dated this on 6th October 2025