Description:SYSTEM FOR DETERMINING QUALITY PARAMETERS OF GRAIN-BASED AGRICULTURAL COMMODITIES
Field of the Invention
[0001] The present disclosure generally relates to agricultural quality assessment systems. Further, the present disclosure particularly relates to a system to determine one or more quality parameters of a grain -based agricultural commodity.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Various systems are used to determine the quality of agricultural commodities. Agricultural commodities, such as grain-based products, must undergo quality assessments for various parameters to ensure suitability for consumption, processing, and trade. Quality parameters commonly include characteristics such as moisture content, texture, colour, and contamination levels. The quality of such agricultural commodities is essential for determining their market value, shelf life, and processing efficiency.
[0004] One of the conventional techniques for assessing quality parameters is visual inspection. Visual inspection techniques involve human operators manually inspecting samples to determine properties like colour, contamination, or texture. However, such methods are prone to human error, inconsistency, and subjectivity, resulting in unreliable results. Moreover, manual inspection is time-consuming, labour-intensive, and unsuitable for large-scale operations. As a result, visual inspection methods are considered inefficient for accurately determining quality parameters .
[0005] Another well-known technique for determining quality parameters involves chemical analysis. In chemical analysis techniques, samples of agricultural commodities are tested using chemical reagents or physical methods to quantify specific parameters like moisture content or contamination levels. While chemical analysis techniques can provide detailed information, such techniques are generally destructive, expensive, and time-consuming. Additionally, chemical analysis requires trained personnel and specialized equipment, making it less suitable for rapid, on-site assessments. As a result, chemical analysis techniques are also associated with various drawbacks when determining quality parameters.
[0006] Alternative systems utilize imaging technologies, such as near-infrared (NIR) spectroscopy, to assess the quality of grain-based agricultural commodities. NIR spectroscopy involves the use of infrared light to measure the absorption of light by the sample, which can then be correlated with various quality parameters. However, NIR-based systems can be expensive and complex to operate. Furthermore, the accuracy of such systems can be compromised by external factors, such as variations in sample size, uneven distribution of the sample, or interference from ambient light, making it difficult to obtain reliable data. Thus, NIR-based systems also face several challenges in determining quality parameters accurately.
[0007] In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for determining one or more quality parameters of grain-based agricultural commodities.
Summary
[0008] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[0009] The following paragraphs provide additional support for the claims of the subject application.
[00010] In an aspect, the present disclosure provides a system to determine one or more quality parameters of a grain-based agricultural commodity. The system comprises a container configured to receive a sample of said grain-based agricultural commodity over a glass barrier. A light source is disposed over said container to emit light toward said sample. An optical tunnel is positioned beneath said glass barrier, and such an optical tunnel is configured to direct light transmitted through said sample toward a sensing unit that is configured to receive said transmitted light and determine optical data. A processing unit utilizes a machine learning model to determine said one or more quality parameters based on said determined optical data. A computing device is operatively connected to said processing unit, wherein said processing unit transmits said determined one or more quality parameters to said computing device.
[00011] The system further comprises a dynamic adjustment mechanism, which includes a motorized assembly configured to alter a length of said optical tunnel based on a thickness of said sample. This adjustment enables consistent light transmission and enhances the accuracy of the optical data collection by adjusting the tunnel length based on sample thickness.
[00012] Said optical tunnel comprises an adaptive focus optical lens positioned between said optical tunnel and said sensing unit. The adaptive focus optical lens automatically adjusts the focal length based on an optical property of said sample, ensuring accurate focus and high-quality optical data collection, improving overall quality parameter determination.
[00013] The system utilizes said processing unit to automatically adjust the wavelength of light emitted by said light source based on the type of grain-based agricultural commodity detected. Such adjustment enables the system to account for variations in optical properties among different commodities, further enhancing the precision of the analysis.
[00014] The light source is coupled to a rail-based adjustment mechanism allowing vertical and horizontal movement of said light source over said container, enabling precise positioning relative to said sample. This mechanism ensures optimal light exposure across the sample for consistent data collection and analysis.
[00015] The system further comprises a sample ejection mechanism that automatically removes said grain-based agricultural commodity from said container after analysis. This allows for efficient handling of multiple samples in succession, increasing throughput and reducing manual intervention during operations.
[00016] An automated sample loading mechanism introduces said grain-based agricultural commodity into said container, providing consistent sample placement over said glass barrier. This mechanism ensures uniform sample positioning, which enhances the reliability of the quality parameter determination.
[00017] Said optical tunnel is equipped with internal baffles that direct the emitted light toward specific regions of said sample. This configuration enables targeted analysis of specific areas within the sample, providing localized quality parameter assessment.
[00018] The light source includes a polarizing filter that polarizes the emitted light before passing through said sample. This feature allows the system to analyze polarized light data, facilitating enhanced detection of surface texture and grain morphology.
[00019] Said container comprises a removable sample tray that facilitates easy handling of said grain-based agricultural commodity before and after analysis. This design enables consistent sample placement across repeated measurements, contributing to the system's overall efficiency and reliability in determining quality parameters.
Brief Description of the Drawings
[00020] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00021] FIG. 1 illustrates a system to determine one or more quality parameters of a grain-based agricultural commodity, in accordance with the embodiments of the present disclosure.
[00022] FIG. 2 illustrates an architectural flow diagram of a system to determine one or more quality parameters of a grain-based agricultural commodity, in accordance with the embodiments of the present disclosure.
[00023] FIG. 3 illustrates multiple views of a system to determine one or more quality parameters of a grain-based agricultural commodity, in accordance with the embodiments of the present disclosure.
Detailed Description
[00024] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00025] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00026] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00027] As used herein, the term "system" refers to a combination of hardware components and software that work in conjunction to determine one or more quality parameters of a grain-based agricultural commodity. The system as described includes various interconnected elements that operate collectively to perform the intended analysis. Such a system may be implemented in a laboratory or industrial setting for assessing the quality of agricultural products, particularly grain-based commodities. The system may be designed to handle different grain types and sizes, providing versatility in applications related to food safety, processing, and agricultural quality control. Additionally, the system is capable of functioning autonomously or semi-autonomously through integrated components like sensors, optical elements, and a processing unit, which together enable efficient analysis. Said system as used herein can be employed across various agricultural supply chains, including grain silos, processing plants, and distribution centres to ensure that the commodities meet predefined quality standards.
[00028] As used herein, the term "container" refers to a physical receptacle configured to receive a sample of said grain-based agricultural commodity. The container is structured to securely hold the sample during the analysis process and ensure that it remains in a controlled environment for accurate measurement. The container may be composed of various materials, such as plastic or metal, that are durable and suited for handling food-based commodities. The container is positioned over a glass barrier, which acts as a transparent interface for the light to pass through during analysis. Said container may vary in size and shape based on the type and volume of grain being assessed. In addition, such a container is adaptable to various grain types, and its design facilitates easy cleaning or removal of residues after each analysis, ensuring hygienic conditions are maintained throughout the process.
[00029] As used herein, the term "light source" refers to an optical element that emits light toward the grain-based agricultural sample placed in the container. The light source is located above the container and is positioned to ensure uniform light distribution across the sample. The emitted light interacts with the sample, and a portion of it is transmitted through the sample, which is then collected for further analysis. Such a light source may utilize different types of light, such as visible light, infrared light, or ultraviolet light, depending on the specific quality parameter being measured. The light source may also be adjustable, allowing the intensity or wavelength of the light to be modified as per the grain type or the specific requirements of the analysis. Said light source is a crucial component for enabling the system to collect optical data for determining quality parameters.
[00030] As used herein, the term "optical tunnel" refers to a structure positioned beneath the glass barrier, configured to guide light transmitted through the sample toward a sensing unit. The optical tunnel is designed to ensure that light is directed precisely from the sample to the sensing unit, thereby minimizing external interference and ensuring the accuracy of the data collected. The optical tunnel may be composed of reflective materials or coatings that help in focusing the transmitted light, improving the reliability of the system. Additionally, said optical tunnel may include internal components such as baffles or other elements that assist in light management, directing light to specific regions of the sample or preventing light scattering. Such an optical tunnel plays a critical role in ensuring that high-quality optical data is captured for further processing.
[00031] As used herein, the term "sensing unit" refers to a detection device that receives light transmitted through the sample via the optical tunnel. The sensing unit is configured to collect optical data related to the sample, which can include properties like absorption, reflectance, or transmittance of the light. Said sensing unit may include elements such as photodetectors, cameras, or other optical sensors that are capable of translating light signals into digital data for processing. The sensing unit forms a critical link between the physical interaction of light with the sample and the system's ability to quantify quality parameters based on the detected light. Such a sensing unit is capable of handling large volumes of data and is essential for obtaining accurate and reliable results in the analysis process.
[00032] As used herein, the term "processing unit" refers to a computational device responsible for analyzing the optical data obtained from the sensing unit. The processing unit is equipped to utilize a machine learning model to determine one or more quality parameters of the grain-based agricultural commodity. Said processing unit includes both hardware and software elements that work together to process complex datasets and generate output based on pre-trained models. The processing unit can be programmed to adjust its analysis based on the specific type of grain commodity being assessed. Furthermore, such a processing unit is responsible for managing the overall system’s operations, ensuring that data flows smoothly from the sensing unit to the computing device and that the analysis is completed efficiently and accurately.
[00033] As used herein, the term "computing device" refers to an external device operatively connected to the processing unit for receiving the determined one or more quality parameters. The computing device can be a computer, a tablet, or any other suitable device capable of displaying, storing, or further processing the results transmitted from the processing unit. The computing device may also facilitate user interaction, providing an interface for controlling the system or viewing results in real-time. Said computing device is essential for outputting the final analysis and can be integrated into larger data management systems for comprehensive agricultural monitoring and decision-making.
[00034] As used herein, the term "dynamic adjustment mechanism" refers to a motorized assembly included in the optical tunnel that alters the length of the optical tunnel based on the thickness of the grain sample being analyzed. Such a mechanism allows for adjustments in real-time, ensuring that the optical data collected is consistent across different sample sizes. The dynamic adjustment mechanism is particularly useful in situations where sample thickness varies, providing flexibility and ensuring that the light transmitted through the sample reaches the sensing unit accurately. Said mechanism contributes to the precision of the overall system, as it ensures that optical path length is optimized for each specific sample.
[00035] As used herein, the term "adaptive focus optical lens" refers to a lens situated between the optical tunnel and the sensing unit that automatically adjusts its focal length based on an optical property of the sample being analyzed. Said lens ensures that the light reaching the sensing unit is properly focused, which is critical for accurate optical data collection. The adaptive focus optical lens dynamically responds to changes in sample characteristics, such as size or texture, to provide a clear and focused image for analysis. Such an adaptive feature enhances the system's ability to process samples with varying optical properties without requiring manual adjustment.
[00036] As used herein, the term "polarizing filter " refers to an optical element included in the light source that polarizes the emitted light before it passes through the sample. The polarizing filter allows the system to analyze polarized light data, which enhances the detection of surface texture and grain morphology. Said filter is especially useful in applications where detailed surface analysis is required, as it enables the system to differentiate between surface and internal properties of the grain sample. The polarizing filter adds an additional dimension to the optical data collected, providing more comprehensive information about the sample's quality parameters.
[00037] FIG. 1 illustrates a system 100 to determine one or more quality parameters of a grain-based agricultural commodity, in accordance with the embodiments of the present disclosure. The system 100 comprises a container 102 that is configured to receive a sample of a grain-based agricultural commodity over a glass barrier 104. The container 102 is structured to hold the sample securely during the analysis process, ensuring that the sample remains stationary and properly positioned over the glass barrier 104 for accurate measurement. The container 102 is capable of accommodating various types of grain-based agricultural commodities, with the dimensions and structural materials chosen to provide durability and ease of cleaning. The container 102 is positioned to allow light from a light source 106 to pass through the sample and interact with the glass barrier 104, enabling subsequent optical analysis. The container 102 and glass barrier 104 are arranged in a manner that ensures that light transmitted through the sample is directed downward toward an optical tunnel 108. The glass barrier 104 is designed to be transparent, allowing the passage of light without significant distortion, ensuring that the optical data collected is representative of the true characteristics of the sample. The combination of the container 102 and the glass barrier 104 provides a stable platform for the sample, which is critical for ensuring consistent light transmission and accurate analysis throughout the operation of the system 100.
[00038] The light source 106 is disposed over the container 102 and is configured to emit light toward the sample. The light source 106 is arranged to provide uniform illumination across the surface of the sample to ensure that the optical data collected from the transmitted light is consistent and reliable. The light source 106 may use various types of light, such as visible light, ultraviolet light, or infrared light, depending on the specific analysis requirements and the grain-based commodity being tested. The light source 106 is mounted in such a way that it can be adjusted vertically or horizontally, allowing the system 100 to target specific regions of the sample for analysis. The intensity and wavelength of the light emitted by the light source 106 can be modulated depending on the type of sample being analyzed and the desired quality parameters to be determined. The light source 106, being positioned above the container 102, directs light toward the sample, which passes through the grain-based commodity and the glass barrier 104. The emitted light interacts with the sample, allowing the system 100 to analyze the transmitted light for various optical properties, which can then be used to determine one or more quality parameters of the sample.
[00039] The optical tunnel 108 is disposed beneath the glass barrier 104 and is configured to direct light transmitted through the sample toward a sensing unit 110. The optical tunnel 108 is designed to minimize external interference, such as ambient light or reflections from the surrounding environment, that could affect the quality of the optical data collected. The optical tunnel 108 is structured to ensure that the transmitted light is focused toward the sensing unit 110 without significant loss of intensity or distortion. The optical tunnel 108 may include internal reflective surfaces or other optical elements that guide the transmitted light along a predefined path. The tunnel 108 ensures that only the light that has passed through the sample is collected, improving the accuracy of the analysis. The length and configuration of the optical tunnel 108 can be adjusted dynamically to accommodate samples of varying thicknesses or optical properties. The optical tunnel 108 provides a controlled environment for the transmitted light to travel from the sample to the sensing unit 110, ensuring that the optical data collected accurately reflects the properties of the grain-based agricultural commodity being analyzed.
[00040] The sensing unit 110 is configured to receive the transmitted light from the optical tunnel 108 and determine optical data corresponding to the sample. The sensing unit 110 includes one or more sensors that capture the light transmitted through the sample and convert into digital data that can be analyzed. The sensing unit 110 may use different types of sensors depending on the specific optical properties being measured, such as photodetectors, cameras, or spectrometers. The optical data determined by the sensing unit 110 includes information about the absorption, reflection, or transmission characteristics of the sample, which can be correlated with specific quality parameters of the grain-based agricultural commodity. The sensing unit 110 operates in conjunction with the optical tunnel 108 to ensure that the data collected is precise and relevant to the analysis being conducted. The placement of the sensing unit 110 at the end of the optical tunnel 108 allows the system 100 to capture light that has interacted directly with the sample, ensuring that the data reflects the true characteristics of the commodity.
[00041] The processing unit 112 is configured to utilize a machine learning model (such as Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), Neural Networks, etc.) to determine one or more quality parameters of the grain-based agricultural commodity based on the optical data collected by the sensing unit 110. The processing unit 112 receives the optical data and applies the machine learning model, which has been trained using data from various types of grain-based commodities, to identify patterns and correlations in the optical data that are indicative of specific quality parameters. The machine learning model allows the processing unit 112 to make accurate and consistent determinations of quality parameters, such as moisture content, grain size, or contamination levels, based on the optical data. The processing unit 112 operates autonomously, continuously processing data from the sensing unit 110 and updating the analysis in real-time as additional data is collected. The processing unit 112 is capable of handling large volumes of data and can be programmed to recognize a wide range of quality parameters across different types of grain-based agricultural commodities. By leveraging the machine learning model, the processing unit 112 enhances the system 100's ability to provide reliable and repeatable measurements of the quality parameters of the grain-based agricultural commodity.
[00042] The computing device 114 is operatively connected to the processing unit 112 and is configured to receive the determined quality parameters from the processing unit 112. The computing device 114 may display the quality parameters on a user interface, allowing an operator to review the analysis results in real-time. The computing device 114 may also store the data for further analysis or reporting, providing a comprehensive record of the quality parameters for each sample analyzed. The computing device 114 can be connected to other systems or databases, enabling integration with larger quality control or monitoring systems used in agricultural or food processing industries. The computing device 114 may also provide options for controlling the system 100, allowing the operator

to adjust parameters such as the type of analysis to be conducted, the sample size, or the wavelength of light emitted by the light source 106. The connection between the computing device 114 and the processing unit 112 ensures that the system 100 operates efficiently, with the computing device 114 serving as the primary interface for managing the analysis process and reviewing the results.
[00043] In an embodiment, the optical tunnel 108 of the system 100 comprises a dynamic adjustment mechanism that includes a motorized assembly. Said dynamic adjustment mechanism is structured to alter the length of the optical tunnel 108 based on the thickness of the sample. The thickness of the grain-based agricultural commodity may vary, and adjusting the length of the optical tunnel 108 ensures that the light transmitted through the sample is properly directed toward the sensing unit 110 without interference. The motorized assembly allows the system 100 to automatically modify the optical path length in real time during analysis. This adjustment compensates for differences in sample thickness, ensuring that the transmitted light travels through the optical tunnel 108 consistently, regardless of the physical characteristics of the sample. Such a dynamic adjustment mechanism allows the system 100 to handle a wide range of grain-based commodities, providing flexibility in analyzing samples of varying sizes or densities without manual recalibration.
[00044] In an embodiment, the optical tunnel 108 of the system 100 comprises an adaptive focus optical lens positioned between said optical tunnel 108 and the sensing unit 110. The adaptive focus optical lens is structured to adjust its focal length automatically based on the optical property of the sample. Grain-based agricultural commodities may exhibit different optical properties, such as refractive index or absorption characteristics. Said adaptive focus optical lens dynamically changes the focal length in response to these properties, ensuring that the transmitted light is focused precisely on the sensing unit 110. Such an adjustment improves the accuracy of the optical data collected, as the light is concentrated optimally for each specific sample. The adaptive focus optical lens eliminates the need for manual focus adjustments, enabling continuous, uninterrupted analysis. This automatic focusing capability allows the system 100 to handle different types of samples without requiring physical alterations to the setup, streamlining the overall process and improving the consistency of data collection.
[00045] In an embodiment, the processing unit 112 of the system 100 is structured to automatically adjust the wavelength of light emitted by the light source 106 based on the type of grain-based agricultural commodity being analyzed. Different types of grains may require specific wavelengths of light to accurately measure their optical properties and corresponding quality parameters. The processing unit 112 identifies the type of grain-based commodity and adjusts the light source 106 accordingly, emitting light at the optimal wavelength for that specific commodity. This automatic adjustment ensures that the light interacts with the sample in a manner that maximizes the accuracy of the optical data collected. Such wavelength adjustments allow the system 100 to accommodate various grain types without manual intervention, providing a versatile solution for analyzing different commodities. The processing unit 112 manages this process in real-time, ensuring that the light source 106 consistently emits the appropriate wavelength for each sample under examination.
[00046] In an embodiment, the light source 106 of the system 100 is coupled to a rail-based adjustment mechanism that allows vertical and horizontal movement of said light source 106 over the container 102. The rail-based adjustment mechanism enables the precise positioning of the light source 106 relative to the sample within the container 102. By allowing both vertical and horizontal adjustments , the system 100 can direct the light emitted by the light source 106 to specific regions of the sample for targeted analysis. Such a mechanism enhances the flexibility of the system 100, as the light source 106 can be moved to different positions based on the size, shape, or placement of the sample. The movement provided by the rail-based adjustment mechanism ensures that the light exposure is uniform across the entire sample or, alternatively, focused on particular areas of interest, depending on the requirements of the analysis. The rail-based mechanism allows for smooth and controlled adjustments, improving the overall versatility of the system 100.
[00047] In an embodiment, the system 100 further comprises a sample ejection mechanism that automatically removes the grain-based agricultural commodity from the container 102 after the analysis is complete. The sample ejection mechanism is structured to facilitate the efficient handling of multiple samples in succession, allowing the system 100 to operate continuously without the need for manual removal of each sample after analysis. The ejection mechanism may include a motorized assembly or pneumatic system that physically pushes or lifts the sample out of the container 102 once the analysis process concludes. This automatic removal system reduces downtime between analyses and enhances throughput by enabling the system 100 to rapidly switch between samples. The sample ejection mechanism also helps maintain cleanliness within the container 102, as it removes the sample in a controlled manner, preventing spillage or residue that could affect subsequent analyses. This feature improves the system 100's ability to handle large volumes of samples in a streamlined and automated manner.
[00048] In an embodiment, the system 100 further comprises an automated sample loading mechanism that introduces the grain-based agricultural commodity into the container 102. The automated sample loading mechanism is structured to ensure consistent sample placement over the glass barrier 104 within the container 102. This mechanism may include a hopper or conveyor system that delivers the sample directly into the container 102, aligning the sample with the glass barrier 104 for optimal light transmission during analysis. By automating the loading process, the system 100 eliminates manual sample handling, reducing the risk of human error or contamination. The automated sample loading mechanism allows the system 100 to maintain uniform sample positioning across multiple analyses, ensuring consistency in data collection. The mechanism may also control the volume of the sample introduced into the container 102, preventing overfilling or underfilling, which could affect the accuracy of the optical measurements.
[00049] In an embodiment, the optical tunnel 108 of the system 100 is further configured with internal baffles that direct the emitted light toward specific regions of the sample. The internal baffles are structured to manage the path of the light transmitted through the sample, allowing for targeted analysis of particular areas within the sample. By directing light toward specific regions, the system 100 can focus on key characteristics of the sample, such as localized defects, variations in grain size, or contamination. The internal baffles minimize light scattering within the optical tunnel 108, ensuring that the transmitted light follows a controlled path toward the sensing unit 110. Such baffles enhance the accuracy of the optical data collected by isolating certain areas of the sample for analysis, preventing interference from other regions that may not be relevant to the specific quality parameters being measured. The internal baffles allow the system 100 to perform detailed, region-specific analysis of the grain-based commodity.
[00050] In an embodiment, the light source 106 of the system 100 comprises a polarizing filter that polarizes the emitted light before it passes through the sample. The polarizing filter is structured to allow the system 100 to analyze polarized light data, which provides enhanced detection of surface texture and grain morphology. The polarizing filter modifies the light waves in such a way that they interact with the surface of the sample in a more controlled manner, revealing details about the grain’s outer structure. This polarized light data can be used to determine surface-related quality parameters, such as texture, shape, or surface defects, which may not be detectable using non-polarized light. By incorporating a polarizing filter, the system 100 improves its ability to perform detailed surface analysis of grain-based commodities, offering a more comprehensive assessment of the sample’s quality. The polarizing filter is integrated into the light source 106 and operates in conjunction with the rest of the system 100 during the analysis process.
[00051] In an embodiment, the container 102 of the system 100 comprises a removable sample tray that facilitates easy handling of the grain-based agricultural commodity before and after analysis. The removable sample tray is structured to allow operators to load and remove samples from the container 102 efficiently. The sample tray may be constructed from a durable material that is easy to clean, ensuring that residue from previous samples does not interfere with subsequent analyses. The removable sample tray ensures that each sample is positioned consistently over the glass barrier 104 for accurate light transmission. After the analysis is complete, the tray can be detached from the container 102, allowing the sample to be removed quickly without disrupting the setup of the system 100. The removable sample tray simplifies the process of handling multiple samples, reducing downtime between analyses and enhancing the overall operational efficiency of the system 100.
[00052] The system 100 provides the technical effect of accurately determining one or more quality parameters of a grain-based agricultural commodity by integrating various components such as the container 102, light source 106, optical tunnel 108, sensing unit 110, processing unit 112, and computing device 114. The container 102 holds the sample securely over the glass barrier 104, enabling precise light transmission. The light source 106 emits light toward the sample, and the optical tunnel 108 directs the transmitted light to the sensing unit 110, ensuring accurate collection of optical data. The processing unit 112 applies a machine learning model to interpret the data and determine quality parameters. This arrangement allows for reliable, non-destructive analysis of the sample and consistent transmission of results to the computing device 114.
[00053] The optical tunnel 108, with a dynamic adjustment mechanism, provides the technical effect of adapting to varying sample thicknesses. By employing a motorized assembly, the system 100 adjusts the length of the optical tunnel 108 to match the thickness of the sample. This dynamic adjustment ensures that light travels through the sample efficiently and reaches the sensing unit 110 with minimal distortion. The ability to adjust the optical tunnel 108 based on sample thickness improves the precision of the light transmission, resulting in more accurate optical data collection and consistent analysis across different grain-based commodities.
[00054] The adaptive focus optical lens in the optical tunnel 108 provides the technical effect of automatically adjusting the focal length based on the optical properties of the sample. This adjustment enhances the accuracy of the transmitted light reaching the sensing unit 110, ensuring that the light is properly focused for optimal data collection. The system 100 thus accommodates various grain-based commodities with differing optical characteristics, providing flexibility and improving the quality of the analysis.
[00055] The processing unit 112 provides the technical effect of automatically adjusting the wavelength of the light emitted by the light source 106 based on the type of grain-based agricultural commodity being analyzed. Different commodities exhibit different optical properties, and by adjusting the wavelength of the light to the specific commodity, the system 100 can optimize the interaction between the light and the sample. This capability allows the system 100 to capture more relevant and accurate optical data, resulting in improved detection and analysis of the quality parameters for various grain-based commodities.
[00056] The light source 106, coupled to a rail-based adjustment mechanism, provides the technical effect of allowing vertical and horizontal movement of the light source 106 over the container 102. This movement enables the precise positioning of the light source 106 relative to the sample, ensuring consistent illumination across different regions of the sample or targeted analysis of specific areas. The rail-based mechanism improves the system 100’s ability to adapt to varying sample sizes and shapes, ensuring that the emitted light is optimally directed for accurate optical data collection.
[00057] The sample ejection mechanism provides the technical effect of automating the removal of the grain-based agricultural commodity from the container 102 after the analysis is complete. This automation increases the throughput of the system 100 by enabling the continuous handling of multiple samples in succession without the need for manual intervention. The sample ejection mechanism streamlines the analysis process, reduces downtime between samples, and maintains a clean testing environment within the container 102, ensuring the reliability of subsequent analyses.
[00058] The automated sample loading mechanism provides the technical effect of introducing the grain-based agricultural commodity into the container 102 in a consistent and repeatable manner. By automatically placing the sample over the glass barrier 104, the system 100 reduces the risk of human error during sample placement, which could otherwise affect the accuracy of the analysis. The automated sample loading mechanism ensures that each sample is correctly positioned for optimal light transmission, enhancing the reliability and consistency of the optical data collected across different tests.
[00059] The internal baffles within the optical tunnel 108 provide the technical effect of directing the emitted light toward specific regions of the sample for targeted analysis. By controlling the light path within the optical tunnel 108, the internal baffles minimize light scattering and ensure that the light is concentrated on relevant areas of the sample. This allows the system 100 to perform localized analysis of specific regions, improving the accuracy and relevance of the data collected, particularly when examining heterogeneous samples with varying characteristics.
[00060] The polarizing filter integrated into the light source 106 provides the technical effect of allowing the system 100 to analyze polarized light data for enhanced detection of surface texture and grain morphology. The polarized light interacts with the sample in a controlled manner, revealing surface features that are otherwise difficult to detect using non-polarized light. By analyzing the polarized light data, the system 100 can assess quality parameters related to the grain's surface characteristics, such as texture or defects, providing a more detailed evaluation of the sample.
[00061] The removable sample tray in the container 102 provides the technical effect of facilitating easy handling of the grain-based agricultural commodity before and after analysis. By enabling operators to easily load and remove samples from the container 102, the removable sample tray simplifies the analysis process and reduces downtime between tests. The tray also helps maintain consistent sample placement over the glass barrier 104, ensuring uniform light transmission for each analysis, which contributes to the accuracy and repeatability of the optical data collected by the system 100.
[00062] FIG. 2 illustrates an architectural flow diagram of a system 100 to determine one or more quality parameters of a grain-based agricultural commodity, in accordance with the embodiments of the present disclosure. The system 100 begins by placing a sample of the grain-based commodity into a container, which is configured to position the sample over a glass barrier. The light source is disposed above the container and emits light toward the sample. The emitted light interacts with the sample, and a portion of the light is transmitted through the glass barrier. The transmitted light is then directed into an optical tunnel located beneath the glass barrier, which guides the light toward a sensing unit. The optical tunnel is structured to minimize interference and maintain the integrity of the light's transmission. The sensing unit receives the transmitted light and collects optical data that is indicative of the sample’s quality parameters. The collected optical data is then sent to a processing unit, which utilizes a machine learning model to analyze the data and determine one or more quality parameters, such as moisture content or grain texture. Once the processing unit determines the quality parameters, it transmits the results to a computing device. The computing device, operatively connected to the processing unit, is capable of displaying or storing the determined quality parameters via a user interface. This streamlined system architecture enables automated, non-destructive analysis of grain-based agricultural commodities, providing reliable and consistent quality assessments.
[00063] FIG. 3 illustrates multiple views of a system to determine one or more quality parameters of a grain-based agricultural commodity, in accordance with the embodiments of the present disclosure. The system is depicted from various perspectives, each revealing the internal and external components that contribute to its functionality. The system includes a top body and bottom body that house various critical components responsible for the analysis process. Externally, the top body includes a light indicator, which may signal the operational status of the system. A USB-C charging port is provided along the bottom body, alongside indicator LEDs and a power switch. The USB-C port enables connectivity to external power sources or computing devices for data transfer or charging. The hinge groove on the body facilitates secure closure and provides access to internal components when required for maintenance or calibration.
[00064] Internally, the system is designed with a robust architecture that supports precise analysis. At the heart of the system is the container, positioned at the top of the unit, which holds the grain-based agricultural commodity sample during analysis. The sample is placed over a glass window, which serves as a barrier between the sample and the internal optics of the system. The glass window ensures a clear path for light transmission without physical interference from the sample. The container is supported by the container wall, which provides structural integrity while housing the optical components below.
[00065] Beneath the container, the system comprises an optical tunnel, which is responsible for directing light that has passed through the sample toward the sensors for data collection. The optical tunnel is positioned in alignment with a sensor printed circuit board (PCB) located on a stand for the sensor and battery assembly. The optical tunnel is carefully designed to minimize the loss of light and ensure that the transmitted light is accurately directed toward the sensor PCB. The sensor PCB is responsible for converting the transmitted light into electrical signals that correspond to optical data about the sample. This optical data is then processed by the system’s processing board, which houses the necessary computational elements for further analysis.
[00066] The system also includes LED mounting at the top of the container, where an LED light source is positioned to emit light toward the sample. The LED light interacts with the sample, and the transmitted light passes through the optical tunnel for analysis. A reflective dome is included in the system's internal architecture to ensure uniform light distribution across the sample, reducing the possibility of shadowing or uneven illumination during the analysis process. The reflective dome is supported by a lamp holder that maintains the correct positioning of the light source relative to the sample.
[00067] The mounting stand at the base of the system provides structural support for the optics tunnel and the sensor PCB, ensuring that all components remain aligned during operation. Hex screws are used throughout the system to securely fasten the various parts, providing durability and maintaining precise alignment of the internal components over repeated usage. Additionally, the base of the system includes cable management, ensuring that internal wiring is organized and shielded from interference with the system’s moving parts or optical elements.
[00068] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00069] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00070] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00071] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00072] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A system to determine one or more quality parameters of a grain-based agricultural commodity, comprising:
a container configured to receive a sample of said grain-based agricultural commodity over a glass barrier;
a light source disposed over said container to emit light toward said sample;
an optical tunnel disposed beneath said glass barrier, wherein said optical tunnel is configured to direct light transmitted through said sample toward a sensing unit that is configured to receive said transmitted light and determine optical data;
a processing unit configured to utilize a machine learning model to determine said one or more quality parameters based on said determined optical data; and
a computing device operatively connected to said processing unit, wherein said processing unit is configured to transmit said determined one or more quality parameters to said computing device.
2. The system of claim 1, wherein said optical tunnel further comprises a dynamic adjustment mechanism, wherein said dynamic adjustment mechanism comprises a motorized assembly configured to alter a length of said optical tunnel based on a thickness of said sample.
3. The system of claim 1, wherein said optical tunnel comprises an adaptive focus optical lens disposed between said optical tunnel and said sensing unit, said adaptive focus optical lens being configured to adjust a focal length automatically based on an optical property of said sample.
4. The system of claim 1, wherein said processing unit is further configured to automatically adjust the wavelength of light emitted by said light source based on the type of grain-based agricultural commodity detected.
5. The system of claim 1, wherein said light source is coupled to a rail-based adjustment mechanism allowing vertical and horizontal movement of said light source over said container, enabling positioning of said light source relative to said sample.
6. The system of claim 1, wherein said system further comprises a sample ejection mechanism that automatically removes said grain-based agricultural commodity from said container after analysis, allowing for efficient handling of multiple samples in succession.
7. The system of claim 1, further comprises an automated sample loading mechanism that introduces said grain-based agricultural commodity into said container, enabling consistent sample placement over said glass barrier.
8. The system of claim 1, wherein said optical tunnel is further configured with internal baffles that direct the emitted light toward specific regions of said sample, allowing for targeted analysis of specific areas.
9. The system of claim 1, wherein said light source comprises a polarizing filter , which polarizes the emitted light before it passes through said sample, allowing said system to analyze polarized light data for enhanced detection of surface texture and grain morphology.
10. The system of claim 1, wherein said container comprises a removable sample tray that facilitates easy handling of said grain-based agricultural commodity before and after analysis, enabling consistent sample placement across repeated measurements.

SYSTEM FOR DETERMINING QUALITY PARAMETERS OF GRAIN-BASED AGRICULTURAL COMMODITIES
Abstract
The present disclosure discloses a system to determine one or more quality parameters of a grain-based agricultural commodity. The system comprises a container configured to receive a sample of said grain-based agricultural commodity over a glass barrier, a light source disposed over said container to emit light toward said sample, an optical tunnel disposed beneath said glass barrier, wherein said optical tunnel is configured to direct light transmitted through said sample toward a sensing unit that is configured to receive said transmitted light and determine optical data, a processing unit configured to utilize a machine learning model to determine said one or more quality parameters based on said determined optical data, and a computing device operatively connected to said processing unit, wherein said processing unit is configured to transmit said determined one or more quality parameters to said computing device.
Fig. 1 , Claims:Claims
I/We Claim:
1. A system to determine one or more quality parameters of a grain-based agricultural commodity, comprising:
a container configured to receive a sample of said grain-based agricultural commodity over a glass barrier;
a light source disposed over said container to emit light toward said sample;
an optical tunnel disposed beneath said glass barrier, wherein said optical tunnel is configured to direct light transmitted through said sample toward a sensing unit that is configured to receive said transmitted light and determine optical data;
a processing unit configured to utilize a machine learning model to determine said one or more quality parameters based on said determined optical data; and
a computing device operatively connected to said processing unit, wherein said processing unit is configured to transmit said determined one or more quality parameters to said computing device.
2. The system of claim 1, wherein said optical tunnel further comprises a dynamic adjustment mechanism, wherein said dynamic adjustment mechanism comprises a motorized assembly configured to alter a length of said optical tunnel based on a thickness of said sample.
3. The system of claim 1, wherein said optical tunnel comprises an adaptive focus optical lens disposed between said optical tunnel and said sensing unit, said adaptive focus optical lens being configured to adjust a focal length automatically based on an optical property of said sample.
4. The system of claim 1, wherein said processing unit is further configured to automatically adjust the wavelength of light emitted by said light source based on the type of grain-based agricultural commodity detected.
5. The system of claim 1, wherein said light source is coupled to a rail-based adjustment mechanism allowing vertical and horizontal movement of said light source over said container, enabling positioning of said light source relative to said sample.
6. The system of claim 1, wherein said system further comprises a sample ejection mechanism that automatically removes said grain-based agricultural commodity from said container after analysis, allowing for efficient handling of multiple samples in succession.
7. The system of claim 1, further comprises an automated sample loading mechanism that introduces said grain-based agricultural commodity into said container, enabling consistent sample placement over said glass barrier.
8. The system of claim 1, wherein said optical tunnel is further configured with internal baffles that direct the emitted light toward specific regions of said sample, allowing for targeted analysis of specific areas.
9. The system of claim 1, wherein said light source comprises a polarizing filter , which polarizes the emitted light before it passes through said sample, allowing said system to analyze polarized light data for enhanced detection of surface texture and grain morphology.
10. The system of claim 1, wherein said container comprises a removable sample tray that facilitates easy handling of said grain-based agricultural commodity before and after analysis, enabling consistent sample placement across repeated measurements.