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Journal articles on the topic 'Grain Quality Detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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Zeng, Lei, Huai Jian Tang, Feng Jia, and Jin Shui Wang. "Rational Allocation of China’s Grain Resources by near-Infrared Spectroscopy." Advanced Materials Research 524-527 (May 2012): 2298–301. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.2298.

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Grain is a vital commodity that has significant effects on people’s livelihood. Grain is not an infinite renewable resource due to population growth, shrinking cultivated land, limited water supply, ongoing climate change, and so on. In spite of multiple varieties, grain faces serious challenges now and in the coming decades in china. Where there is short of individual grain breed suitable for end-use performance. The farrago of different varieties leads to inefficient use of grain. Improved detection methods are so scarce that grain is not graded in the light of end-use quality. NIR provides

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Shedole, Seema, Sowmya B J, and Neha Arthi V.P. "A Convolution Neural Network-Based Wheat Grain Classification System." Journal of Scientific Research 66, no. 02 (2022): 22–29. http://dx.doi.org/10.37398/jsr.2022.660204.

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In most developing countries, agriculture is the primary economic sector. Seed categorization and evaluation could provide detailed information for seed germination, seed quality management, and impurity detection. Many academics are working on a range of seed image analysis systems using several categorization algorithms. India is the world's second-largest wheat grower. The importance of detecting the wheat quality cannot be overstated. Manually determining wheat quality necessitates skilled knowledge and is time demanding. The healthy and poor wheat growth is a complete indication of biolog

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Kim, Bong-Kyu, Nam Hoon Goo, Jong Hyuk Lee, and Jun Hyun Han. "Reconstruction and Size Prediction of Prior Austenite Grain Boundary (PAGB) using Artificial Neural Networks." Korean Journal of Metals and Materials 58, no. 12 (2020): 822–29. http://dx.doi.org/10.3365/kjmm.2020.58.12.822.

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To automatically reconstruct the prior austenite grains from as-quenched martensitic structure, we applied a deep learning algorithm to recognize the prior austenite grains boundaries hidden in the martensitic matrix. The FC-DenseNet architecture based on FCN (fully convolutional networks) was used to train the martensite and ground truth label of the prior austenite grain boundaries. The original martensite structures and prior austenite grain boundaries were prepared using different chemical etching solutions. The initial PAGS detection rate was as low as 37.1%, which is not suitable for qua

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Cai, Zhiqi, Yangjun Deng, Xinghui Zhu, Bo Li, Chenglin Xu, and Donghui Li. "High-Throughput 3D Rice Chalkiness Detection Based on Micro-CT and VSE-UNet." Agronomy 15, no. 2 (2025): 450. https://doi.org/10.3390/agronomy15020450.

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Rice is a staple food for nearly half the global population and, with rising living standards, the demand for high-quality grain is increasing. Chalkiness, a key determinant of appearance quality, requires accurate detection for effective quality evaluation. While traditional 2D imaging has been used for chalkiness detection, its inherent inability to capture complete 3D morphology limits its suitability for precision agriculture and breeding. Although micro-CT has shown promise in 3D chalk phenotype analysis, high-throughput automated 3D detection for multiple grains remains a challenge, hind

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Dubey, Srishti, Ritika Mukherjee, S. Yashaswi, and Dr Naveen Kumar Dewangan. "RICE GRAIN CLASSIFICATION SYSTEM." International Journal of Engineering Applied Sciences and Technology 09, no. 12 (2025): 63–66. https://doi.org/10.33564/ijeast.2025.v09i12.006.

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Rice grain Classification plays a vital role in agricultural industry. Rice is a staple food consumed Globally. Rice grain Classification is necessary to maintain quality, food safety and consumer satisfaction. Traditional methods which are employed for classification and identification of rice grains are time consuming and may lead to errors. The proposed system uses image processing techniques to identify and segregate rice grains into different types. The categorization and grading of rice grains is done on the basis of size, colour and texture. Various image processing methods such as imag

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Li, Jiming, Jinhua Xiao, Silvana Grandillo, et al. "QTL detection for rice grain quality traits using an interspecific backcross population derived from cultivated Asian (O. sativa L.) and African (O. glaberrima S.) rice." Genome 47, no. 4 (2004): 697–704. http://dx.doi.org/10.1139/g04-029.

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An interspecific advanced backcross population derived from a cross between Oryza sativa 'V20A' (a popular male-sterile line used in Chinese rice hybrids) and Oryza glaberrima (accession IRGC No. 103544 from Mali) was used to identify quantitative trait loci (QTL) associated with grain quality and grain morphology. A total of 308 BC3F1 hybrid families were evaluated for 16 grain-related traits under field conditions in Changsha, China, and the same families were evaluated for RFLP and SSR marker segregation at Cornell University (Ithaca, N.Y.). Eleven QTL associated with seven traits were dete

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Prabira, Kumar Sethy, Kanta Barpanda Nalini, Kumar Rath Amiya, and Kumari Behera Santi. "Rice false smut detection based on faster R-CNN." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1590–95. https://doi.org/10.11591/ijeecs.v19.i3.pp1590-1595.

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Rice false smut is one of the most dangerous diseases in rice at the ripening phase caused by Ustilaginoidea Virens. It is one of the most important grain diseases in rice production worldwide. Its epidemics not only lead to yield loss but also reduce grain quality because of multiple mycotoxins generated by the causative pathogen. The pathogen infects developing spikelets and specifically converts individual grain into rice false smut ball. Rice false smut balls seem to be randomly formed in some grains on a panicle of a plant in the paddy field. In this study, we suggest a novel approach for

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Markovskyi, O. V. "Detection of Genes Determining the Quality Characteristics of Maize Grain and its Resistance to Stress Factors." Science and innovation 10, no. 1 (2014): 41–53. http://dx.doi.org/10.15407/scine10.01.041.

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Zhang, Wan-Ru, Qiang Zhao, and Hao-Jia He, et. al. "Detection and Analysis of Quality Traits of Different Black Rice Varieties in the Southern Henan Rice Region." Journal of Biotechnology Research, no. 103 (May 18, 2024): 30–40. http://dx.doi.org/10.32861/jbr.103.30.40.

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Using 14 black rice varieties imported from Southern Henan as experimental materials, the appearance quality, grinding quality, nutritional quality, cooking and eating quality of grain in Southern Henan were tested and analyzed by means of rice huller, rice mopping machine and near-infrared grain analyzer, combined with biochemical and physical analysis techniques. The results showed that there were abundant variation types in grain length, grain width, length-to-width ratio, brown rice rate, milled rice rate, head rice rate, soluble protein content, starch content, gelatinization temperature,

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Liang, Junling, Jianpin Chen, Meixuan Zhou, et al. "An Intelligent Detection System for Wheat Appearance Quality." Agronomy 14, no. 5 (2024): 1057. http://dx.doi.org/10.3390/agronomy14051057.

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In the realm of commercial trade, the appearance quality of wheat is a crucial metric for assessing its value and grading. Traditionally, evaluating wheat appearance quality is a manual process conducted by inspectors, which is time-consuming, laborious, and error-prone. In this research, we developed an intelligent detection system for wheat appearance quality, leveraging state-of-the-art neural network technology for the efficient and standardized assessment of wheat appearance quality. Our system was meticulously crafted, integrating high-performance hardware components and sophisticated so

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Udhay, Chityala. "Quality Analysis and Classification of Rice using Image processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2002–7. http://dx.doi.org/10.22214/ijraset.2022.44227.

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Abstract: Grain quality analysis is a huge challenge in agricultural industries. Internal control is critical in the food industry because food products are characterized and rated into various categories after quality data has been collected. Grain quality assessment is performed by hand, but the results are subjective, lengthy, and pricey. To overcome the limitations and drawbacks of image processing techniques, different resolutions are used for grain quality analysis. Using image processing techniques, this paper proposes a method for grading and analyzing rice based on grain size and form

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Huang, Zhiyu, Luyao Li, and Likun Lu. "Design of LED grain image recognition and positioning system." Journal of Physics: Conference Series 2187, no. 1 (2022): 012016. http://dx.doi.org/10.1088/1742-6596/2187/1/012016.

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Abstract LED grain image recognition and location technology is an important technology in LED grain manufacturing and sorting process. Whether the LED grain can be accurately identified and positioned will greatly affect the production speed and efficiency of LED grain. At present, the research on this technology in China is in its infancy. In this paper, a LED grain image recognition and location system based on rectangle detection algorithm is proposed to solve the problem of LED grain image recognition and location. The system uses image grayscale, binarization, edge extraction, contour de

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Al-Azawi, Nagham Majeed, Ziyad Asmail Abed, and Mohammed Al-Issawi. "Detection of genes associated with qualitative characteristics of gluten." RUDN Journal of Agronomy and Animal Industries 14, no. 3 (2019): 196–208. http://dx.doi.org/10.22363/2312-797x-2019-14-3-196-208.

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The research was aimed at analyzing allelic variants of protein in wheat varieties used in Iraqi bakery and evaluating these varieties via genetic source using grain quality selection. Variety tests were carried out at field experimental station of Russian State Agrarian University - Moscow Timiryazev Agricultural Academy. The analysis of wheat grain quality was made after harvesting in mid August. Allele state of genes controlling the quality of gluten in wheat grain was determined using the PCR method. Samples of Iraqi wheat varieties 12 (soft wheat) and One (durum wheat) are characterized b

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Sethy, Prabira Kumar, Nalini Kanta Barpanda, Amiya Kumar Rath, and Santi Kumari Behera. "Rice false smut detection based on faster R-CNN." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1590. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1590-1595.

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<span lang="EN-IN">Rice false smut is one of the most dangerous diseases in rice at the ripening phase caused by Ustilaginoidea Virens. It is one of the most important grain diseases in rice production worldwide. Its epidemics not only lead to yield loss but also reduce grain quality because of multiple mycotoxins generated by the causative pathogen. The pathogen infects developing spikelets and specifically converts individual grain into rice false smut ball. Rice false smut balls seem to be randomly formed in some grains on a panicle of a plant in the paddy field. In this study, we sug

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Krukovskii, K. V., A. I. Lotkov, V. N. Grishkov, A. A. Gusarenko, and D. I. Bobrov. "Features of the grain-subgrain structure of TI49.8NI50.2 alloy after megaplastic deformation by abc pressing and subsequent annealing." Perspektivnye Materialy 12 (2023): 59–70. http://dx.doi.org/10.30791/1028-978x-2023-12-59-70.

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The features of the grain-subgrain structure of Ti49.8Ni50.2 (at. %) alloy after megaplastic deformation by multi-axial forging at 573 K and subsequent annealing were investigated by the method of backscattered electron diffraction. It is shown that the best detection of the patterns of the grain-subgrain structure of the Ti49.8Ni50.2 alloy after a given true deformation e = 9.55 and annealing at 773 K for 2 hours, it is observed in areas without martensitic relief on the surface of the samples. It was found that the clearest diffraction patterns of backscattered electrons are observed in the

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Arkhipov, M. V., Yu A. Tyukalov, T. A. Danilova, and S. L. Beletskiy. "The relevance of creating operational control of grain quality based on indicators of its hidden integrity." Tovaroved prodovolstvennykh tovarov (Commodity specialist of food products), no. 12 (December 5, 2024): 745–48. https://doi.org/10.33920/igt-01-2412-10.

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The article describes the relevance of developing a digital system for early detection and express assessment of hidden grain heterogeneity based on the use of methods of non-destructive testing of the integrity of the internal structures of the grain in a comprehensive assessment of the quality of grain batches for various purposes.

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Lin, Lei, Yong He, Zhitao Xiao, Ke Zhao, Tao Dong, and Pengcheng Nie. "Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy." Applied Sciences 9, no. 8 (2019): 1654. http://dx.doi.org/10.3390/app9081654.

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Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral

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Liquan Shen, Yiwen Sun, Zhi Liu, and Zhaoyang Zhang. "Efficient SKIP Mode Detection for Coarse Grain Quality Scalable Video Coding." IEEE Signal Processing Letters 17, no. 10 (2010): 887–90. http://dx.doi.org/10.1109/lsp.2010.2066966.

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Zhao, Wenyi, Shiyuan Liu, Xinyi Li, Xi Han, and Huihua Yang. "Fast and accurate wheat grain quality detection based on improved YOLOv5." Computers and Electronics in Agriculture 202 (November 2022): 107426. http://dx.doi.org/10.1016/j.compag.2022.107426.

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Mai, Zhi Wei, Chang You Li, Ye Zhang, Feng Ying Xu, and Jian Min Li. "Application of Wireless Data Transmission Technique in Drying Equipment." Advanced Materials Research 422 (December 2011): 262–67. http://dx.doi.org/10.4028/www.scientific.net/amr.422.262.

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The application of online detection technology for grain moisture is one of the most effective ways to improve the quality of grain drying. The online detection system for grain moisture consists of three parts: online remote module of ingoing grain moisture (charging), online remote module of outgoing grain moisture (discharging), and the host controller. It adopts JZ873 wireless transparent transmission module to realize the wireless data communication among various modules, and to formulate the communication drive. In practice, this system has characteristics such as flexible use, reliable

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Adejugbe, I. T., J. A. Olowonubi, T. I. Olorunsola, et al. "Design and Development of a Double-layer Grain Sieving Machine." Asian Journal of Advanced Research and Reports 18, no. 8 (2024): 141–47. http://dx.doi.org/10.9734/ajarr/2024/v18i8715.

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Sieving grains is a fundamental process in the agricultural and food industries and is crucial for enhancing cereal products' quality and safety. However, manual grain sieving results in drudgery, fatigue, and time wastage during the processing. The importance of sieving grains spans several dimensions, including improving grain purity by removing foreign materials and impurities such as stones, dust, and chaff. This process also ensures uniformity in grain size, which is vital for consistent cooking and processing characteristics, thereby enhancing product quality and consumer satisfaction. A

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Fan, Chenlong, Ying Liu, Tao Cui, et al. "Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy." Foods 13, no. 24 (2024): 4173. https://doi.org/10.3390/foods13244173.

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Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions. This study aims to explore a rapid detection method for protein content in maize grain powder based on near-infrared spectroscopy. A method for determining protein content in maize grain powder was established using

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Wang, Zhu, Zhen Chen, Yao Zhang, et al. "GrainSense: A Wireless Grain Moisture Sensing System Based on Wi-Fi Signals." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 3 (2024): 1–25. http://dx.doi.org/10.1145/3678589.

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Grain moisture sensing plays a critical role in ensuring grain quality and reducing grain losses. However, existing commercial off-the-shelf (COTS) grain moisture sensing systems are either expensive, inconvenient or inaccurate, which greatly limit their widespread deployment in real-world scenarios. To fill this gap, we develop a system called GrainSense which leverages COTS Wi-Fi devices to detect the grain moisture without the need for dedicated sensors. Specifically, we propose a wireless grain moisture detection model based on the refraction phenomenon of Wi-Fi signals and the Multiple-In

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Wang, Xiang, Yuxuan Chen, Jiaxin Jiang, et al. "Design and simulation of capacitive sensor for grain moisture detection." Journal of Physics: Conference Series 2740, no. 1 (2024): 012018. http://dx.doi.org/10.1088/1742-6596/2740/1/012018.

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Abstract The high moisture content of grain would result in quality deteriorations such as mold and mildew during storage and transportation. For effective detection of grain moisture, a forked-finger capacitive sensor in the form of triangular prism is proposed. The effect of the sensor structural parameters on its capacitance, electric field distribution, sensitivity and penetration depth are investigated using finite element simulations. The results show that an optimised effective penetration depth of 2.73 mm for grain moisture detection can be achieved when the finger width, number, lengt

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Ma, Yuyang, Xiujuan Hu, Zhenlin Hu, et al. "Simultaneous Compositional and Grain Size Measurements Using Laser Opto-Ultrasonic Dual Detection for Additive Manufacturing." Materials 13, no. 10 (2020): 2404. http://dx.doi.org/10.3390/ma13102404.

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Metal-based additive manufacturing (AM) is a disruptive technique with great potential across multiple industries; however, its manufacturing quality is unstable, leading to an urgent requirement for component properties detection. The distribution of grain size has an important effect on many mechanical properties in AM, while the distribution of added elements, such as titanium (Ti), has a measurable effect on the grain size of an aluminum (Al) alloy. Therefore, the detection of the distributions of grain size and elements is of great significance for AM. In this study, we investigated the d

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Markov, Yu F. "Hygrothermal probes are an effective and available tool for early detection of degradation and self- heating of stored grain." Khleboproducty 30, no. 2 (2021): 42–45. http://dx.doi.org/10.32462/0235-2508-2021-30-2-42-45.

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The prerequisites for the mass use of hygrothermal probes as an affordable and effective tool for remote operational control of grain quality preservation of grain quality during its storage are considered as an alternative to the use of classical thermo probe, which are still widely used to monitor the state and identify centers of self-heating of grain stored in floor storage warehouses. Hygrothermal probe, with their budgetary availability, provide increased sensitivity in comparison with thermo probe and allow detecting self- heating of grain at its initial stages.

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Liu, Hanruyi, Yushuo Bai, Jiaxuan Cao, Yinpeng Li, and Jiayue Liu. "A Wheat Grade Identification and Bad Grain Detection System Based on EasyDL." Journal of Intelligence and Knowledge Engineering 1, no. 3 (2023): 69–74. http://dx.doi.org/10.62517/jike.202304310.

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At present, there are many problems of insufficient professional testing personnel, low testing equipment and low accurate identification. The wheat grade identification and bad grain testing system based on EasyDL will bring profound changes to the agricultural product quality testing industry. This system is implemented in the AI development platform of Baidu fly pulp EasyDL, and the innovative image recognition technology is used in wheat detection. The image features are extracted through the convolutional neural network, and the supervised learning algorithm is used for model training. Us

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Yang, Haiying, Yanyu Li, Liyong Xin, et al. "MCSNet+: Enhanced Convolutional Neural Network for Detection and Classification of Tribolium and Sitophilus Sibling Species in Actual Wheat Storage Environments." Foods 12, no. 19 (2023): 3653. http://dx.doi.org/10.3390/foods12193653.

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Insect pests like Tribolium and Sitophilus siblings are major threats to grain storage and processing, causing quality and quantity losses that endanger food security. These closely related species, having very similar morphological and biological characteristics, often exhibit variations in biology and pesticide resistance, complicating control efforts. Accurate pest species identification is essential for effective control, but workplace safety in the grain bin associated with grain deterioration, clumping, fumigator hazards, and air quality create challenges. Therefore, there is a pressing

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Zhiqiang, Wan, Zhang Qingqing, and Xu Liang. "Researches on the Technology of Measuring Grain Moisture based on 555 Integrated Circuit." Journal of Physics: Conference Series 2065, no. 1 (2021): 012021. http://dx.doi.org/10.1088/1742-6596/2065/1/012021.

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Abstract Grain is the material basis of human existence and development. Food quality has an important effect on people’s physical health for our country. Moisture content of grain has been an important factor in affecting food quaility. Every year, a substantial part of the food production is lost because of too much water contained in the food without drying. According to the annual statistics of the country’s harvest, loss of food due to drying time caused by water take up 500 - 10000000 tons, according to approximate 1.5% - 3% of the total grain output.With the development of science and t

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Li, Dengshan, Rujing Wang, Chengjun Xie, et al. "A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network." Sensors 20, no. 3 (2020): 578. http://dx.doi.org/10.3390/s20030578.

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Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized

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Tan, Yongyu, Hong Cheng, Zhiying Zheng, Xuekun Zhang, and Guangsheng Zhou. "The effects and mechanisms of individual and combined drought-resistant genes on wheat grain quality." Engineering Innovation and Practice 1 (February 7, 2025): eip1v0207a. https://doi.org/10.5281/zenodo.15584111.

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Wheat is a globally important cereal crop, and its yield and quality are significantly constrained by drought stress. Drought resistance, as a complex trait regulated by multiple genes, exhibits varying effects under different environmental conditions. This study focuses on drought-resistant genes A, B, and C and their combinations, systematically evaluating their effects on wheat grain quality, particularly thousand-kernel weight, under both normal irrigation and drought stress conditions. Using KASP molecular marker technology, precise detection and genotyping of the three drought-resistant

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Leonova, Julia, Svetlana Malakhova, Konstantin Petrov, Margarita Tyutyunkova, and Maria Tikhonova. "Agroecological assessment of the use of non-traditional fertilizers in oat crops." АгроЭкоИнфо 2, no. 62 (2024): 23. http://dx.doi.org/10.51419/202142223.

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The article examines the effect of non-traditional fertilizers on the yield and quality of oat grain, as well as the content of heavy metals in the grain. It is proved that with the combined use of sewage sludge and organic mineral fertilizer HUMITON, the content of heavy metals in oat grain is below the detection limit, and the highest yield of the studied crop is also noted. The maximum content of protein and ash elements in oat grain was revealed when using HUMITON. With the combined use of non-traditional fertilizers, grain quality indicators remain at the level of the control variant. Key

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Seraj, S., L. Hassan, SN Begum, and MM Sarker. "Physico-chemical attributes and correlation among grain quality traits of some exotic aromatic rice lines." Journal of the Bangladesh Agricultural University 11, no. 2 (2014): 227–32. http://dx.doi.org/10.3329/jbau.v11i2.19899.

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Forty three rice genotypes were used to evaluate their aroma detection through sensory test. Aroma was detected by 1.7% KOH as a sensory test. Thirteen rice genotypes were detected having strong aroma; ten had moderate aroma; fifteen had slight aroma and five had no aroma. In case of grain shape study, 37 genotypes were evaluated as slender and six as medium. In this study, grain aroma had significant and positive association with grain length width ratio; significant and negative association with grain width, significant and negative association with gelatinization temperature, and significan

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Mair, David, Ariel Henrique Do Prado, Philippos Garefalakis, Alessandro Lechmann, Alexander Whittaker, and Fritz Schlunegger. "Grain size of fluvial gravel bars from close-range UAV imagery – uncertainty in segmentation-based data." Earth Surface Dynamics 10, no. 5 (2022): 953–73. http://dx.doi.org/10.5194/esurf-10-953-2022.

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Abstract. Data on grain sizes of pebbles in gravel-bed rivers are of key importance for the understanding of river systems. To gather these data efficiently, low-cost UAV (uncrewed aerial vehicle) platforms have been used to collect images along rivers. Several methods to extract pebble size data from such UAV imagery have been proposed. Yet, despite the availability of information on the precision and accuracy of UAV surveys as well as knowledge of errors from image-based grain size measurements, open questions on how uncertainties influence the resulting grain size distributions still persis

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Mendoza, Querriel Arvy, Lester Pordesimo, Mitchell Neilsen, Paul Armstrong, James Campbell, and Princess Tiffany Mendoza. "Application of Machine Learning for Insect Monitoring in Grain Facilities." AI 4, no. 1 (2023): 348–60. http://dx.doi.org/10.3390/ai4010017.

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In this study, a basic insect detection system consisting of a manual-focus camera, a Jetson Nano—a low-cost, low-power single-board computer, and a trained deep learning model was developed. The model was validated through a live visual feed. Detecting, classifying, and monitoring insect pests in a grain storage or food facility in real time is vital to making insect control decisions. The camera captures the image of the insect and passes it to a Jetson Nano for processing. The Jetson Nano runs a trained deep-learning model to detect the presence and species of insects. With three different

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Zhang, Lihui, Helei Cui, Hongli Li, Feng Han, Yaqiu Zhang, and Wenfu Wu. "Parameters Online Detection and Model Predictive Control during the Grain Drying Process." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/924698.

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In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitatio

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Saklani, Honeyily, Disha Sugha, and Vinay Thakur. "Conditional based Edge Detection Algorithm Enhancing the Quality of Clustered Grain Seeds." International Journal of Computer Applications 178, no. 33 (2019): 12–15. http://dx.doi.org/10.5120/ijca2019919203.

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Barboza da Silva, Clíssia, Alysson Alexander Naves Silva, Geovanny Barroso, et al. "Convolutional Neural Networks Using Enhanced Radiographs for Real-Time Detection of Sitophilus zeamais in Maize Grain." Foods 10, no. 4 (2021): 879. http://dx.doi.org/10.3390/foods10040879.

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The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we p

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Shen, Susu, Hua Zhang, Keke Huang, Huanwen Chen, Wenxin Shen, and Xiaowei Fang. "Differentiation of cultivation areas and crop years of milled rice using single grain mass spectrometry." New Journal of Chemistry 43, no. 5 (2019): 2118–25. http://dx.doi.org/10.1039/c8nj02740d.

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Xu, Zhi Fu, Liang Qi Zhu, Xiao Yan Shi, et al. "Research on Rice Detection Technology Based on Machine Vision." Advanced Materials Research 1030-1032 (September 2014): 1788–91. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1788.

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Detection of rice kernel domestic mainly rely on manual measurement using a ruler or vernier caliper tool, which use the ruler measurement of human error, and the measurement of the twisted grain rice vernier caliper is limited. Manual measurement are difficult problems, but also low efficiency. This study analyzes the current research on appearance quality of rice by using machine vision technology mainly focuses on the aspects of rice kernel, chalkiness, yellow rice and other characteristics, realized the accurate detection and obtain rice information quickly by using the machine vision tech

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