Letters

Development of Machine Vision-Based Algorithm for Counting and Discriminating Filled and Unfilled Paddy Rice in Overlapping Mode

Expand
  • Department of Biosystems Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, P.O.Box 578, Iran

Received date: 2023-11-18

  Accepted date: 2024-04-07

  Online published: 2024-10-11

Cite this article

Mahdieh Hoseingholizadeh-Alashti, Davood Kalantari . Development of Machine Vision-Based Algorithm for Counting and Discriminating Filled and Unfilled Paddy Rice in Overlapping Mode[J]. Rice Science, 2024 , 31(5) : 503 -506 . DOI: 10.1016/j.rsci.2024.04.001

References

[1] Duan L F, Yang W N, Bi K, Chen S B, Luo Q M, Liu Q. 2011. Fast discrimination and counting of filled/unfilled rice spikelets based on bi-modal imaging. Comput Electron Agric, 75(1): 196-203.
[2] Jeyaraj P R, Asokan S P, Samuel Nadar E R. 2022. Computer- assisted real-time rice variety learning using deep learning network. Rice Sci, 29(5): 489-498.
[3] Kalantari D, Jafari H, Kaveh M, Szymanek M, Asghari A, Marczuk A, Khalife E. 2022. Development of a machine vision system for the determination of some of the physical properties of very irregular small biomaterials. Int Agrophys, 36(1): 27-35.
[4] Kumar A, Taparia M, Madapu A, Rajalakshmi P, Marathi B, Desai U B. 2020. Discrimination of filled and unfilled grains of rice panicles using thermal and RGB images. J Cereal Sci, 95: 103037.
[5] Kuo T Y, Chung C L, Chen S Y, Lin H A, Kuo Y F. 2016. Identifying rice grains using image analysis and sparse-representation-based classification. Comput Electron Agric, 127: 716-725.
[6] Lafarge T, Bueno C S. 2009. Higher crop performance of rice hybrids than of elite inbreds in the tropics: 2. Does sink regulation, rather than sink size, play a major role? Field Crops Res, 112(2/3): 238-244.
[7] Liu T, Wu W, Chen W, Sun C M, Chen C, Wang R, Zhu X K, Guo W S. 2016. A shadow-based method to calculate the percentage of filled rice grains. Biosyst Eng, 150: 79-88.
[8] Luo X, Jayas D S, Symons S J. 1999. Identification of damaged kernels in wheat using a colour machine vision system. J Cereal Sci, 30(1): 49-59.
[9] Manickavasagan A, Sathya G, Jayas D S, White N D G. 2008. Wheat class identification using monochrome images. J Cereal Sci, 47(3): 518-527.
[10] Mebatsion H K, Paliwal J, Jayas D S. 2013. Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Comput Electron Agric, 90: 99-105.
[11] Neethirajan S, Jayas D S, Karunakaran C. 2007. Dual energy X-ray image analysis for classifying vitreousness in durum wheat. Postharvest Biol Technol, 45(3): 381-384.
[12] Velesaca H O, Suárez P L, Mira R, Sappa A D. 2021. Computer vision based food grain classification: A comprehensive survey. Comput Electron Agric, 187: 106287.
[13] Venora G, Grillo O, Saccone R. 2009. Quality assessment of durum wheat storage centres in Sicily: Evaluation of vitreous, starchy and shrunken kernels using an image analysis system. J Cereal Sci, 49(3): 429-440.
[14] Xing Y Z, Zhang Q F. 2010. Genetic and molecular bases of rice yield. Annu Rev Plant Biol, 61: 421-442.
[15] Yang J C, Peng S B, Visperas R M, Sanico A L, Zhu Q S, Gu S L. 2000. Grain filling pattern and cytokinin content in the grains and roots of rice plants. Plant Growth Regul, 30(3): 261-270.
[16] Zayas I, Pomeranz Y, Lai F S. 1989. Discrimination of wheat and nonwheat components in grain samples by image analysis. Cereal Chem, 66(3): 233-237.
[17] Zhang Q F. 2007. Strategies for developing Green Super Rice. Proc Natl Acad Sci USA, 104(42): 16402-16409.
Outlines

/

浙ICP备05004719号-15   公安备案号:33010302003355
Copyright © Editorial office of Rice Science
Tel: 0571-63371017 E-mail: crrn@fy.hz.zn.cn; cjrs278@gmail.com
Supported by Beijing Magtech Co., Ltd.
Total visitors:  Visitors of today:  Now online: