On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Ahmed, Sheikh Bilal; Ali, Syed Ahmad; Khan, Aadil Zia

doi:10.1109/access.2021.3130472

Cited by 14 publications

(5 citation statements)

References 89 publications

(88 reference statements)

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“…Algorithms which were used are Support Vector Machine(SVM), Random Forest(RF), K-Nearest Neighbor( KNN), Deep Learning, CNN (Convolution neural network) . The authors of [3] discussed, At the forefront of analysis of rice grain along with classification and quality grading is a new technology which can determine the quality of a grain of rice. This technology can help to ensure that only the best rice is exported to other countries.…”

Section: IImentioning

confidence: 99%

Literature Survey on Grain Type Prediction Using Machine Learning

Reddy¹,

Gupta²,

Raj³

et al. 2023

IJRASET

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Classification of grains according to their type becomes very much necessary because there are multiple grains available in the market today. Classification of Grains according their types manually is not efficient. Classification becomes a really wearisome task when it comes to doing it hand-operated instead of automating the process. Manually classifying would be a grueling task. There is a need for mentally keen and quick system which can get better of this difficulty by automating these processes. It should be able to recognize and label the every single grain according to their respective type automatically. Therefore we conducted a literature survey on how various seeds were classified and various analysis which were used to obtain the various desired result. Here we observed that various researchers used k-means, CNN and various other algorithms.

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Section: IImentioning

confidence: 99%

Literature Survey on Grain Type Prediction Using Machine Learning

Reddy¹,

Gupta²,

Raj³

et al. 2023

IJRASET

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“…Ganesan et al [25] the primary goal of this project is to create a novel model for detecting rice leaf illness utilizing multiple DL approaches. B. Ahmed et al [26] the approaches to deep learning that have shown the most potential for further study. It separates the three eras of rice-related writing.…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced Hybrid Model for Multi Paddy diseases detection using Deep Learning

Dixit,

Verma

2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Rapid developments in deep learning (DL) techniques have made it possible to find and recognize objects in pictures. To create a network that is significantly more successful than a single CNN, GAN, RNN, etc., we can mix various neural network models (CNN, GAN, RNN).this combination is known as hybrid model. Hybrid model of deep leaning is give more accurately result for detection and identification of paddy diseases. OBJECTIVES: I have studies outcome of hybrid model 1(DCNN+SVM) and Hybrid model 2 (DCNN + Transfer Learning) to increase accuracy of Rice plant disease detection and classification. The Researched model detects multiple rice plant diseases and it is giving same result in multiple data sets. METHODS: The Proposed System have used Deep Learning Image Processing algorithm and neural Network Like DCNN ,SVM and Transfer Learning .The brand new model is DST where D stands for DCNN, S stands for SVM and T stands for transfer learning. RESULTS: The Researched DST model achieved 95% Training accuracy and 85% validation Accuracy. The Researched model detect multiple rice plant diseases and it is giving same result in multiple data set. CONCLUSION: The proposed model combined 2 existing model and developed hybrid model that a detect various rice plant diseases with better accuracy from available existing model.

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“…Rice is usually processed into commercial rice by drying, husking, milling, polishing, grading and color sorting (Riaz et al, 2017). Polishing, grading and color sorting are rice finishing, which can increase quality of rice and improve the competitiveness of rice products (Ahmed et al, 2021). The traditional rice finishing machinery design mainly relies on the processing experience, which is difficult to guide the optimization design of the processing equipment.…”

Section: Introductionmentioning

confidence: 99%

Calibration of White Rice Simulation Parameters Based on Discrete Element Method

XIE,

BAI,

YAN

et al. 2023

INMATEH

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Aiming at the lack of discrete element simulation models and parameters for rice polishing, grading, color sorting and other technologies and equipment, and the difficulty of guiding equipment design and optimization through simulation, this paper calibrates the simulation parameters of white rice based on angle of repose (AOR) test and simulation methods. Huanghuazhan and Dongnong 429 white rice were selected as research object. Numerical model of white rice was established by multi-sphere filling. According to physical test and references, the simulation parameter range of white rice particles was determined. Plackett-Burman test was used to screen parameters, and it was found that the particle-particle static friction coefficient and particle-particle rolling friction coefficient had significant effects on the AOR of white rice. The regression model between the AOR and the significance parameter was established according to the central composite design method. The simulation parameter combination that has significant influence on the physical AOR was determined through the optimization design, and verified by the simulation test. The simulation AOR was compared with the physical AOR, and the relative error of the two kinds of white rice was less than 3%. The results show that the calibration method proposed in this study can accurately simulate the physical AOR test, which can provide reference for discrete element simulation of white rice processing.

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On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Cited by 14 publications

References 89 publications

Literature Survey on Grain Type Prediction Using Machine Learning

Literature Survey on Grain Type Prediction Using Machine Learning

Advanced Hybrid Model for Multi Paddy diseases detection using Deep Learning

Calibration of White Rice Simulation Parameters Based on Discrete Element Method

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