Relevant bibliographies by topics / Grain Quality Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Grain Quality Detection'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Grain Quality Detection"

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Vaishnavi, V. "Rice Grain Quality Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 262–67. http://dx.doi.org/10.22214/ijraset.2021.34867.

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The quality of grain is of great importance for human beings as it directly impacts human health. Hence there is a great need to measure the quality of grain and identifying non-quality elements. Analysing the grain samples manually is a more time-consuming and complicated process, and having more chances of errors with the subjectivity of human perception. To achieve uniform standard quality and precision, machine vision-based techniques are evolved. Rice quality is nothing but a combination of physical and chemical characteristics. So, to get the physical characteristics of the rice grains,
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Liu, Yiming, Jingchao Zhang, Huali Yuan, et al. "Non-Destructive Quality-Detection Techniques for Cereal Grains: A Systematic Review." Agronomy 12, no. 12 (2022): 3187. http://dx.doi.org/10.3390/agronomy12123187.

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Grain quality involves the appearance, nutritional, and safety attributes of grains. With the improvement of people’s living standards, problems pertaining to the quality of grains have received greater attention. Modern quality detection techniques feature unique advantages including rapidness, non-destructiveness, accuracy, and efficiency in detecting grain quality. This review summarizes research progress of these techniques in detection of quality indices of grains. Particularly, the review focuses on detection techniques based on physical properties including acoustic, optical, thermal, e
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Ameenuddin, Md, Bavireddy Vishwanth Kumar, Soma Yashwanth, Kushal Sahu, and Ganjikunta Teja. "Quality Testing of Rice Grains Using Image Processing Applications." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 876–79. http://dx.doi.org/10.22214/ijraset.2022.47468.

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Abstract: Quality Testing of Rice Grains is testing of grain to evaluate the planting value and the authenticity of the certified lot. There are certain limitations to human eye to observe the Grain. So, the electronic world helps us to separate the faulty Grains from quality Grains. The specific target to be achieved is the development of a rice quality detection system that can assess the quality of rice using digital image processing. The evaluation of the rice grains on the basic grain size and shape using image processing edge detection algorithm is used to find the region of boundaries i
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Gunning, James, and Michael E. Glinsky. "Detection of reservoir quality using Bayesian seismic inversion." GEOPHYSICS 72, no. 3 (2007): R37—R49. http://dx.doi.org/10.1190/1.2713043.

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Sorting is a useful predictor for permeability. We show how to invert seismic data for a permeable rock sorting parameter by incorporating a probabilistic rock-physics model with floating grains into a Bayesian seismic inversion code that operates directly on rock-physics variables. The Bayesian prior embeds the coupling between elastic properties, porosity, and the floating-grain sorting parameter. The inversion uses likelihoods based on seismic amplitudes and a forward convolutional model to generate a posterior distribution containing refined estimates of the floating-grain parameter and it
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Bernardo, Myrtel. "Edge Detection Techniques for Rice Grain Quality Analysis using Image Processing Techniques." Journal of Engineering and Emerging Technologies 1, no. 1 (2022): 8–14. http://dx.doi.org/10.52631/jeet.v1i1.78.

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In agricultural countries like the Philippines, rice grain is considered the most important crop in the world for human consumption as daily food and in the food market, thus quality control must be considered. Rice grain quality evaluation is done manually, which is non-reliable, time-consuming and costly. The quality of rice grain is categorized by the combination of physical and chemical characteristics. Grain appearance, color, size and shape, chalkiness, whiteness, degree of milling, bulk density, foreign matter content, and moisture content are some physical characteristics, while amylos
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Rani, K. Sandhya, K. Swetha, K. Amrutha Varshini, and G. Harika. "Rice Grain Quality Analysis Using Image Processing." International Journal of Advances in Artificial Intelligence and Machine Learning 2, no. 2 (2025): 120–27. https://doi.org/10.58723/ijaaiml.v2i2.455.

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Background of study: Rice quality is crucial for global food security and market value, but traditional assessment relies on labor-intensive, inconsistent, and error-prone manual inspection.Aims and scope of paper: This research proposes an automated system using image processing and AI for comprehensive rice grain quality analysis. The goal is to develop a robust, objective, and precise system to classify rice varieties and evaluate quality with minimal human intervention, reducing the effort, cost, and time of traditional methods.Methods: The core contribution is a computerized model that us
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N, Mr Roopesh Kumar B., Rakshitha P, S. Sai Shankari, Vandana N, and Y. Jhansi. "Advancements in Grain Adulteration Detection and Quality Assessment - A Survey." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1042–50. http://dx.doi.org/10.22214/ijraset.2023.56673.

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Abstract: Food security and public health are severely compromised by food adulteration and quality deterioration, which have become pressing concerns. Unfavourable food quality is caused by a multitude of factors, including but not limited to the widespread adulteration of food. Food quality is heavily influenced by environmental variables, such as poor storage conditions, pest infestations, and contaminant exposure. Furthermore, food products may be exposed to adverse circumstances due to the complexities of transportation and distribution, which can result in microbial spoilage and a loss o
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M, Baritha Begum, Kiruthiga S, Subhashree B, T. Sathya N, and Sarojini B. "Cereal Crop Variety Classification Using Deep Learning." International Journal of Multidisciplinary Research Transactions 6, no. 5 (2024): 102–7. https://doi.org/10.5281/zenodo.11180999.

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Grain quality detection is crucial for ensuring safety, nutrition, and marketability of grain-based products. The proposed approach leverages computer vision and deep learning to develop a comprehensive grain quality detection system for rice, corn, and wheat. The methodology involves assembling a diverse dataset, extensive pre-processing, and a deep neural network architecture optimized for grain quality classification The deep learning model incorporates techniques like transfer learning, attention mechanisms, and multi-task learning to leverage the relationships between grain types and thei
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Busi, Ramya Asalatha, Vaka Prasanna, Somala Sai Lakshmi Bhavana, Tallaparthi Yaswanth Kishore, Vanke Santhi, and Yarram Venkata Sainath. "Rice Grains Detection, Classification, and Quality Prediction Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2230–39. http://dx.doi.org/10.22214/ijraset.2024.59321.

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Abstract: Rice, as the most consumed food worldwide, faces a continual demand, necessitating rigorous quality inspection for both local consumption and international trade. Manual quality assessment methods are fraught with issues such as time consumption, high costs, and error susceptibility. This research paper introduces an innovative solution employing Deep Convolutional Neural Networks (CNNs) to automate rice grain detection, classification, and quality prediction from scanned images. The methodology integrates comprehensive image pre-processing and quality assessment techniques, encompas
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Budzan, Buchczik, Pawełczyk, and Tůma. "Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System." Sensors 19, no. 8 (2019): 1805. http://dx.doi.org/10.3390/s19081805.

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This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) are calculated to improve the detection results and processing time by eliminating poor quality samples. Next, detection of copper ore grains is performed, based on region growing, improved by the first and second derivatives with a modified Niblack’s theor
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Dissertations / Theses on the topic "Grain Quality Detection"

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Singh, Chandra B. "Detection of insect and fungal damage and incidence of sprouting in stored wheat using near-infrared hyperspectral and digital color imaging." 2009. http://hdl.handle.net/1993/3217.

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Wheat grain quality is defined by several parameters, of which insect and fungal damage and sprouting are considered important degrading factors. At present, Canadian wheat is inspected and graded manually by Canadian Grain Commission (CGC) inspectors at grain handling facilities or in the CGC laboratories. Visual inspection methods are time consuming, less efficient, subjective, and require experienced personnel. Therefore, an alternative, rapid, objective, accurate, and cost effective technique is needed for grain quality monitoring in real-time which can potentially assist or replace the ma
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Books on the topic "Grain Quality Detection"

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Office, General Accounting. Food safety and quality: Existing detection and control programs minimize aflatoxin : report to the chairman, Subcommittee on Wheat, Soybeans, and Feed Grains, Committee on Agriculture, House of Representatives. The Office, 1991.

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Book chapters on the topic "Grain Quality Detection"

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Zhang, Hao, Xiao Yu Zhu, and Xuan He. "Early Skip Mode Detection by Exploring Extra Skip Patterns for H.264 Coarse Grain Quality Scalable Video Coding." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35728-2_53.

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Singh, Yumnam Kirani, and Amitava Akuli. "Detection and Counting of Connected Lentil Grains Using Convex Deficiency for Quality Estimation." In Proceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1699-3_33.

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Fan, Lei, Yiwen Ding, Dongdong Fan, Yong Wu, Maurice Pagnucco, and Yang Song. "Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230329.

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Cereal grain plays a crucial role in the human diet as a major source of essential nutrients. Grain Appearance Inspection (GAI) serves as an essential process to determine grain quality and facilitate grain circulation and processing. However, GAI is routinely performed manually by inspectors with cumbersome procedures, which poses a significant bottleneck in smart agriculture. In this paper, we endeavor to develop an automated GAI system: AI4GrainInsp. By analyzing the distinctive characteristics of grain kernels, we formulate GAI as a ubiquitous problem: Anomaly Detection (AD), in which heal
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Mareeswari, V., R. Vijayan, Praveen Kumar S., and Aravind P. Dhakshan. "Leather Defect Classification in Footwear Manufacturing Industries." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-4332-7.ch007.

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Leather is a natural and durable material derived from tanning animal skins and hides. The value of leather is contingent upon its quality and surface condition, making it imperative to detect defects accurately. Traditionally, detecting leather defects has been labor-intensive and time-consuming, prone to human error and eye strain. This paper proposes a fully automatic defect detection system that employs the EfficientNetB0 neural network on the leather to provide defect-free leather. The system can classify leather patches into different classes, such as folding marks, grain off, growth mar
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Lachman, Jaromir, Milan Kroutil, and Ladislav Kohout. "Quality and Selected Metals Content of Spring Wheat (Triticum aestivum L.) Grain and Biomass After the Treatment with Brassinosteroids During Cultivation." In Biomass - Detection, Production and Usage. InTech, 2011. http://dx.doi.org/10.5772/18400.

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Groß, Benedikt, Susanne Hintschich, Milenko Tosic, Paraskevas Bourgos, Konstantinos Tsoumanis, and Francesca Bertani. "PhasmaFOOD - A miniaturized multi-sensor solution for rapid, non-destructive food quality assessment." In OCM 2019 - 4th International Conference on Optical Characterization of Materials, March 13th – 14th, 2019, Karlsruhe, Germany : Conference Proceedings. KIT Scientific Publishing, 2019. http://dx.doi.org/10.58895/ksp/1000087509-10.

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PhasmaFOOD is a H2020 project with the goal of building a miniaturized, smart multi-sensor food scanner. Equipped with a NIR sensor, a UV-VIS sensor and a RGB camera it aims to be a portable, highly versatile solution for various food safety issues, ranging from aflatoxin detection in grains and nuts, over shelf-life prediction in meats and fish to detection of adulteration in meat, edible oils and alcoholic beverages. The unique combination of sensors, operation via a smartphone application and sophisticated data analysis methods offer the possibility of rapid, non-destructive measurements th
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"Investigation of amino acids profile of human and donkey milk samples: A comparative analysis for the nutritional value and proteins quality in both type milk samples." In Book of Abstracts - RAD 2025 Conference. RAD Centre, Niš, Serbia, 2025. https://doi.org/10.21175/rad.abstr.book.2025.3.2.

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BACKGROUND: The recognized as a "super food" the milk is the best option for feeding for the newborns, special adapted to their daily nutrient needs. Due to its chemical composition and the abundance of bioactive compounds, milk is classified as one of the most complete and highly nutritious foods. The Word Health Organization recommenders an intensive breastfeeding of young children in the first 4 to 6 months after birth. However, there are a few health conditions affecting mother may cause a temporary or permanent recommendation against breastfeeding. The chronic diseases, prescribed antibio
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Post, Lynn. "Detecting antibiotic residues in animal feed: the case of distiller’s grains." In Ensuring safety and quality in the production of beef Volume 1. Burleigh Dodds Science Publishing, 2017. http://dx.doi.org/10.19103/as.2016.0008.05.

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C R, Chethan, Baldev Dogra, Ritu Dogra, and Derminder Singh. "DETECTION OF CROP FLOW PATH IN AXIAL FLOW PADDY THRESHER BY USING MAGNETOMETER." In Futuristic Trends in Robotics & Automation Volume 3, Book 1. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bbra1p4ch2.

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Threshing is a significant operation in paddy cultivation, which affects quantitative and qualitative losses of grains. The axial flow paddy thresher ensures higher threshing quality by allowing crop to thresh in multiple passes. However, the retention time, over threshing and crop back feeding are some of the major issues in axial flow threshers and are mainly dependent on louvers spacing and inclination angle. Analysis of actual crop flow within the threshing unit will provide an opportunity to suggest improvementin existing axial flow paddy threshers. A magnetometer sensor (Make: Honeywell,
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"Detecting antibiotic residues in animal feed: the case of distiller’s grains Lynn Post, Food and Drug Administration, USA." In Ensuring safety and quality in the production of beef Volume 1. Burleigh Dodds Science Publishing, 2017. http://dx.doi.org/10.4324/9781351114226-18.

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Conference papers on the topic "Grain Quality Detection"

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Li, Guixia, and Lei Pan. "Multispectral fusion and lightweight CNN model framework for nondestructive detection of grain quality." In International Conference on Machine Vision and Deep Learning (MVDL 2025), edited by Chengzhong Xu and Dickson K. W. Chiu. SPIE, 2025. https://doi.org/10.1117/12.3072075.

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Sharma, Deepika, and Sharad D. Sawant. "Grain quality detection by using image processing for public distribution." In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2017. http://dx.doi.org/10.1109/iccons.2017.8250640.

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Mendoza, Querriel Arvy, Lester Pordesimo, and Mitchell Neilsen. "Enhancing grain facility management with AI-based insect detection and identification system." In Sensing for Agriculture and Food Quality and Safety XV, edited by Moon S. Kim and Byoung-Kwan Cho. SPIE, 2023. http://dx.doi.org/10.1117/12.2672253.

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Huang, Chao, YuHong Zhao, HongMing Zhang, et al. "Design of a near-infrared spectroscopic system for automatic detection of grain quality detection." In 5th Optics Young Scientist Summit 2022, edited by Chao-Yang Lu, Feng Chen, Zhaohui Li, and Yangjian Cai. SPIE, 2022. http://dx.doi.org/10.1117/12.2638478.

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Ge, Hongyi, Guofang Wu, Yuying Jiang, Yuan Zhang, and Feiyu Lian. "Research on THz spectrum detection model of stored grain quality based on deep learning." In Terahertz Technology and Applications. SPIE, 2019. http://dx.doi.org/10.1117/12.2547769.

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Simonovski, Igor, and Leon Cizelj. "Some Useful Tests for the Finite Element Meshes of Polycrystals With Explicit Account of the Grains and Grain Boundaries." In ASME 2010 Pressure Vessels and Piping Division/K-PVP Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/pvp2010-26058.

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A growing number of computational material science and computational mechanics research is currently devoted to the explicit modeling of microstructures at various length and time scales. The finite element models of grains and grain boundaries in polycrystals include discretization of the grain interior. In addition, grain boundaries are explicitly discretized as cohesive zones with appropriate damage properties to facilitate the simulation of intergranular cracking. Such finite element models may easily involve hundreds of grains and millions of finite elements. They may also be combined wit
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Grabowski, Theresa, Daniel Gerner, Fardad Azarmi, Martin McDonnell, and Uchechi Okeke. "Microstructural Evaluation of Tungsten Carbide-Cobalt (WC-17Co) Alloy Deposited by Cold Spraying, High Velocity Air Fuel, and High Velocity Oxygen Fuel Spraying Technologies." In ITSC 2023. ASM International, 2023. http://dx.doi.org/10.31399/asm.cp.itsc2023p0112.

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Abstract In this study, microstructural characterization is conducted on WC-17Co coatings produced via High Velocity Oxygen Fuel (HVOF), High Velocity Air Fuel (HVAF), and Cold Spraying (CS). All coatings prepared were observed to be of good quality and with relatively low porosity content. SEM study showed important microstructural features and grain morphologies of each coating. While composition of feedstock material was approximately similar, elemental composition using EDS showed higher Co content and lower WC in the CS deposited coating. XRD experiment identified formation of more comple
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Albers, Albert, Ju¨rgen Fleischer, Peter Bo¨rsting, Hans-Georg Enkler, Pablo Leslabay, and Matthias Schlipf. "Dealing With Uncertainty of Micro Gears: Integration of Dimensional Measurement, Virtual and Physical Testing." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66672.

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Design and quality assurance of micro gear wheels and involute gear wheels involve multiple challenges regarding prediction of functionality and life cycle performance of complex and wear-resistant micromechanical systems. First of all, this is due to the fact that up to now no tolerance system for micro dimension has been defined. In second place, most measurement strategies for the dimensional characterization of involute micro gears cannot be brought forward from the macro world just as they are. There is few knowledge about the relevant quality characteristics for these micro systems, opti
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Andaru, Arkanu, and Sarah Sausan. "Intelligent Detection of SEM Mineralogy Using Dynamic Segmentation Algorithm in Geothermal Sedimentary Reservoir: Case Study with Quantification of Quartz Overgrowth." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215327-ms.

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Abstract Quantifying quartz overgrowth in sedimentary geothermal reservoirs can provide vital information about reservoir quality and drilling success rates. The traditional process, which involves manual inspection of numerous Scanning Electron Microscope (SEM) images, is tedious and time-consuming. This paper introduces an automated approach using computer vision and random forest algorithms to streamline the process, providing a more efficient method for noise reduction, multi-level thresholding, machine learning (ML) model training, and application to SEM images for quartz overgrowth detec
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Palisch, Terry, and Jeremy Zhang. "A Novel Method to Detect Cement through Direct Measurement – Case Histories." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206019-ms.

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Abstract Cement quality is typically determined through the use of sonic logging tools, more commonly known as cement bond logs (CBLs), or more recently ultrasonic imaging tools (USITs). In general, these tools have served the industry well over time, but with the advent of new and exotic cement blends, as well as multistage cement jobs in today's unconventional horizontal wells, the quality and even location of the cement has become more problematic for basic CBL/USIT tools to detect. In addition, these tools are ineffective through multiple uncemented casing strings. A novel method to detect
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