African Journal of Biotechnology - Article Cited by

African Journal of
Biotechnology

Abbreviation: Afr. J. Biotechnol.
Language: English
ISSN: 1684-5315
DOI: 10.5897/AJB
Start Year: 2002
Published Articles: 12484

Full Length Research Paper

Classification system for rain fed wheat grain cultivars using artificial neural network

Alireza Pazoki1* and Zohreh Pazoki2

1Department of Agronomy and Plant breading, Shahr- e -Rey Branch, Islamic Azad University, PO Box 18155.144, Tehran, Iran. 2Department of Electrical & Electronic Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Email: [email protected]; [email protected]

Article Number - 00CC01D26053
Vol.10(41), pp. 8031-8038 , August 2011
https://doi.org/10.5897/AJB11.488

Accepted: 09 June 2011
Published: 03 August 2011

Copyright © 2024 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0.

CrossRef Citations (25)

See more citations on Google Scholar

1.

da Silva André Geovane,Coradi Paulo Carteri,Teodoro Larissa Pereira Ribeiro,Teodoro Paulo Eduardo, . (2022) Predicting the quality of soybean seeds stored in different environments and packaging using machine learning . Scientific Reports

2.

Rodrigues Dágila Melo,Coradi Paulo Carteri,Teodoro Larissa Pereira Ribeiro,Teodoro Paulo Eduardo,Moraes Rosana dos Santos,Leal Marisa Menezes, . (2024) Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models . Scientific Reports

3.

de Oliveira Carneiro Letícia,Coradi Paulo Carteri,Rodrigues Dágila Melo,Lima Roney Eloy,Teodoro Larissa Pereira Ribeiro,Santos de Moraes Rosana,Teodoro Paulo Eduardo,Nunes Marcela Trojahn,Leal Marisa Menezes,Lopes Lhais Rodrigues,Vendrusculo Tiago Arabites,Robattini Jean Carlos,Soares Anderson Henrique,dos Santos Bilhalva Nairiane, . (2023) Characterizing and Predicting the Quality of Milled Rice Grains Using Machine Learning Models . AgriEngineering

4.

Tańska Małgorzata,Buczek Jan,Jarecki Wacław,Wasilewska Alicja,Konopka Iwona, . (2018) Grain morphology, texture and colour-related compounds of bread wheat cultivars in relation to cultivation regimes and growing location . Zemdirbyste-Agriculture

5.

Singh Ksh. Robert,Chaudhury Saurabh,Datta Subir,Deb Subhasish, . (2022) Gray level size zone matrix for rice grain classification using back propagation neural network: a comparative study . International Journal of System Assurance Engineering and Management

6.

Smart Trends in Information Technology and Computer Communications

7.

Asefa Bezuayehu Gutema,Tsige Fikadu,Mehdi Mina,Kore Tamirat,Lakew Aschalew, . (2023) Rapid classification of tef [Eragrostis tef (Zucc.) Trotter] grain varieties using digital images in combination with multivariate technique . Smart Agricultural Technology

8.

Soft Computing Applications and Intelligent Systems

9.

Garg Raghu,Aggarwal Himanshu,Centobelli Piera,Cerchione Roberto, . (2019) Extracting Knowledge from Big Data for Sustainability: A Comparison of Machine Learning Techniques . Sustainability

10.

Zhang Haolei,Ji Jiangtao,Ma Hao,Guo Hao,Liu Nan,Cui Hongwei, . (2023) Wheat Seed Phenotype Detection Device and Its Application . Agriculture

11.

Taner Alper,Öztekin Yeşim Benal,Tekgüler Ali,Sauk Hüseyin,Duran Hüseyin, . (2018) Classification of Varieties of Grain Species by Artificial Neural Networks . Agronomy

12.

Robert Singh Ksh,Chaudhury Saurabh, . (2020) A cascade network for the classification of rice grain based on single rice kernel . Complex & Intelligent Systems

13.

Ropelewska Ewa, . (2019) Evaluation of wheat kernels infected by fungi of the genus Fusarium based on morphological features . Journal of Food Safety

14.

Zhao Liang,Haque S.M. Rafizul,Wang Ruojing, . (2022) Automated seed identification with computer vision: challenges and opportunities . Seed Science and Technology

15.

Intelligent Techniques and Applications in Science and Technology

16.

Robert Singh Kshetrimayum,Chaudhury Saurabh, . (2020) Comparative analysis of texture feature extraction techniques for rice grain classification . IET Image Processing

17.

Alighaleh Pejman,Pakdel Reyhaneh,Ghanei Ghooshkhaneh Narges,Einafshar Soodabeh,Rohani Abbas,Saeidirad Mohammad Hossein, . (2023) Detection and Classification of Saffron Adulterants by Vis-Nir Imaging, Chemical Analysis, and Soft Computing . Foods

18.

Singh Ksh. Robert,Chaudhury Saurabh, . (2016) Efficient technique for rice grain classification using back‐propagation neural network and wavelet decomposition . IET Computer Vision

19.

Liu Hong-Yan,Wadood Syed Abdul,Xia Yu,Liu Yi,Guo Huan,Guo Bo-Li,Gan Ren-You, . (2023) Wheat authentication：An overview on different techniques and chemometric methods . Critical Reviews in Food Science and Nutrition

20.

Fayyazi Saeideh,Abbaspour-Fard Mohammad Hossein,Rohani Abbas,Monadjemi S. Amirhassan,Sadrnia Hassan, . (2017) Identification and Classification of Three Iranian Rice Varieties in Mixed Bulks Using Image Processing and MLP Neural Network . International Journal of Food Engineering

21.

Yasar Ali,Golcuk Adem,Sari Omer Faruk, . (2024) Classification of bread wheat varieties with a combination of deep learning approach . European Food Research and Technology

Copyright © 2024 Author(s) retain the copyright of this article.

This article is published under the terms of the Creative Commons Attribution License 4.0

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