Determining the Rice Seeds Quality Using Convolutional Neural Network

Sidiq Hidayat - Politeknik Negeri Semarang, Semarang, Indonesia
Dwi Rahmawati - Politeknik Negeri Jember, Jember, Indonesia
Muhamad Prabowo - Politeknik Negeri Semarang, Semarang, Indonesia
Liliek Triyono - Politeknik Negeri Semarang, Semarang, Indonesia
Farika Putri - Politeknik Negeri Semarang, Semarang, Indonesia

Citation Format:



Seed inspection is crucial for plant nurseries and farmers as it ensures seed quality when growing seedlings. It is traditionally accomplished by expert inspectors filtering samples manually, but there are some challenges, such as cost, accuracy, and large numbers. Speed and accuracy were the main conditions for increasing agricultural productivity. Machine learning is a sub-science of Artificial Intelligence that can be applied in research on the classification of rice seed quality. The pipeline of a machine learning system is dataset collection, training, validation, and testing. Model making begins with taking data on the characteristics of rice seeds based on physical parameters in the form of seed shape and color. The dataset used is two thousand images divided into two categories, namely superior seeds and non-superior seeds. Training and Validation was conducted using the Convolutional Neural Network (CNN) algorithm with the concept of cross-validation on Google Collaboratory notebooks. The ratio split of train data and validation data in modeling from a dataset is 80:20. The result of the model formed is a model with the development of a Deep Convolutional Neural Network (Deep CNN) that can classify the digital image data of rice seeds from the results of data calls uploaded into the system. The results of the experiment conducted on 30 test data can be analyzed so that the system can classify superior and non-superior seeds with a precision value of 93% and a recall of 95%.


Artificial intelligence; machine learning; CNN; classification of rice seeds; deep CNN

Full Text:



I. Chatnuntawech, K. Tantisantisom, P. Khanchaitit, T. Boonkoom, B. Bilgic, and E. Chuangsuwanich, “Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network,” Comput. Vis. Pattern Recognit. Cornell Univ., vol. 3, no. 1, pp. 1–22, 2018, [Online]. Available:

K. Kiratiratanapruk et al., “Development of Paddy Rice Seed Classification Process using Machine Learning Techniques for Automatic Grading Machine,” J. Sensors, vol. 2020, 2020, doi: 10.1155/2020/7041310.

S. D. Fabiyi et al., “Varietal Classification of Rice Seeds Using RGB and Hyperspectral Images,” IEEE Access, vol. 8, no. 1, pp. 22493–22505, 2020, doi: 10.1109/ACCESS.2020.2969847.

J. Gupta and Neelam, “Identification and Classification of Rice varieties using Mahalanobis Distance by Computer Vision,” Int. J. Sci. Res. Publ., vol. 5, no. 5, pp. 1–6, 2015.

D. Joshi et al., “Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network,” Opt. Laser Technol., vol. 137, no. January, p. 106861, 2021, doi: 10.1016/j.optlastec.2020.106861.

M. M. Tin, K. L. Mon, E. P. Win, and S. S. Hlaing, “Myanmar Rice Grain Classification Using Image Processing Techniques,” Adv. Intell. Syst. Comput., vol. 744, pp. 324–332, 2019, doi: 10.1007/978-981-13-0869-7_36.

Z. Y. Liu, F. Cheng, Y. Bin Ying, and X. Q. Rao, “Identification of rice seed varieties using neural network,” J. Zhejiang Univ. Sci., vol. 6 B, no. 11, pp. 1095–1100, 2005, doi: 10.1631/jzus.2005.B1095.

Y. Ogawa, Quality Evaluation of Rice, Second Edi. Elsevier Inc., 2016.

P. R. Armstrong et al., “Detection of chalk in single kernels of long-grain milled rice using imaging and visible/near-infrared instruments,” Cereal Chem., vol. 96, no. 6, pp. 1103–1111, 2019, doi: 10.1002/cche.10220.

T. Ahmed, C. R. Rahman, and M. F. M. Abid, “RICE GRAIN DISEASE IDENTIFICATION USING DUAL PHASE CONVOLUTIONAL NEURAL NETWORK BASED SYSTEM AIMED AT SMALL DATASET,” agriRxiv, vol. 2021, no. 1, pp. 1–14, 2021, doi: 10.31220/agrirxiv.2021.00062.

P. Mookdarsanit and L. Mookdarsanit, “PhosopNet: An improved grain localization and classification by image augmentation,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 2, pp. 479–490, 2021, doi: 10.12928/TELKOMNIKA.v19i2.18321.

Y. Toda et al., “Training instance segmentation neural network with synthetic datasets for crop seed phenotyping,” Commun. Biol., vol. 3, no. 1, pp. 1–12, 2020, doi: 10.1038/s42003-020-0905-5.

M. Uddin, M. A. Islam, M. Shajalal, M. A. Hossain, and M. S. I. Yousuf, “Paddy seed variety identification using T20-HOG and Haralick textural features,” Complex Intell. Syst., vol. 8, no. 1, pp. 657–671, 2022, doi: 10.1007/s40747-021-00545-0.

P. T. T. Hong, T. T. T. Hai, L. T. Lan, V. T. Hoang, V. Hai, and T. T. Nguyen, “Comparative Study on Vision Based Rice Seed Varieties Identification,” Proc. - 2015 IEEE Int. Conf. Knowl. Syst. Eng. KSE 2015, pp. 377–382, 2015, doi: 10.1109/KSE.2015.46.

B. Lurstwut and C. Pornpanomchai, “Image analysis based on color, shape and texture for rice seed (Oryza sativa L.) germination evaluation,” Agric. Nat. Resour., vol. 51, no. 5, pp. 383–389, 2017, doi: 10.1016/j.anres.2017.12.002.

A. A. Aznan, R. Ruslan, I. H. Rukunudin, F. A. Azizan, and A. Y. Hashim, “Rice seed varieties identification based on extracted colour features using image processing and artificial neural network (ANN),” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 6, pp. 2220–2225, 2017, doi: 10.18517/ijaseit.7.6.2990.

H. Nguyen-Quoc and V. T. Hoang, “Rice seed image classification based on HOG descriptor with missing values imputation,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 4, pp. 1897–1903, 2020, doi: 10.12928/telkomnika.v18i4.14069.

K. Aukkapinyo, S. Sawangwong, P. Pooyoi, and W. Kusakunniran, “Localization and Classification of Rice-grain Images Using Region Proposals-based Convolutional Neural Network,” Int. J. Autom. Comput., vol. 17, no. 2, pp. 233–246, 2020, doi: 10.1007/s11633-019-1207-6.

S. Kido, Y. Hirano, and N. Hashimoto, “Detection and classification of lung abnormalities by use of convolutional neural network (CNN) and regions with CNN features (R-CNN),” 2018 Int. Work. Adv. Image Technol. IWAIT 2018, pp. 1–4, 2018, doi: 10.1109/IWAIT.2018.8369798.

Kurnianingsih et al., “Segmentation and Classification of Cervical Cells Using Deep Learning,” IEEE Access, vol. 7, no. 1, pp. 116925–116941, 2019, doi: 10.1109/ACCESS.2019.2936017.

B. Zhao, J. Feng, X. Wu, and S. Yan, “A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation,” Int. J. Autom. Comput., vol. 14, no. 2, pp. 119–135, 2017, doi: 10.1007/s11633-017-1053-3.

J. G. A. Barbedo, “Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification,” Elsevier Comput. Electron. Agric., vol. 153, no. July, pp. 46–53, 2018, doi: 10.1016/j.compag.2018.08.013.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, no. 1, pp. 1800–1807, 2017, doi: 10.1109/CVPR.2017.195.

C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.

A. R. Pazoki, F. Farokhi, and Z. Pazoki, “Classification Of Rice Grain Varieties Using Two Artificial Neural Networks (MLP And Neuro-Fuzzy),” J. Anim. Plant Sci., vol. 24, no. 1, pp. 336–343, 2014.

W. Liu, S. Zeng, G. Wu, H. Li, and F. Chen, “Rice seed purity identification technology using hyperspectral image with lasso logistic regression model,” Sensors, vol. 21, no. 13, p. 4384, 2021, doi: 10.3390/s21134384.

B. Filipović, V., Panić, M., Brdar, S., & Brkljač, “Significance of Morphological Features in Rice Variety Classification Using Hyperspectral Imaging,” Int. Symp. Image Signal Process. Anal., vol. IEEE, pp. 171–176, 2021, doi: 10.1109/ISPA52656.2021.9552086.

S. Qadri et al., “Machine vision approach for classification of rice arieties using texture features,” Int. J. Food Prop., vol. 24, no. 1, pp. 1615–1630, 2021, doi: 10.1080/10942912.2021.1986523.

Y. Hamid, S. Wani, A. B. Soomro, A. A. Alwan, and Y. Gulzar, “Smart seed classification system based on MobileNetV2 architecture,” in 2022 2nd International Conference on Computing and Information Technology (ICCIT), 2022, pp. 217–222, doi: 10.1109/ICCIT52419.2022.9711662.

N. A. Mohidem, N. Hashim, R. Shamsudin, and H. Che Man, “Rice for food security: Revisiting its production, diversity, rice milling process and nutrient content,” Agriculture, vol. 12, no. 6, p. 741, 2022, doi: 10.3390/agriculture12060741.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JOIV : International Journal on Informatics Visualization
ISSN 2549-9610  (print) | 2549-9904 (online)
Organized by Society of Visual Informatocs, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W :
E :,,

View JOIV Stats

Creative Commons License is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.