Classification of Corn Seed Quality using Residual Network with Transfer Learning Weight

Meidya Koeshardianto; Wahyudi Agustiono; Wahyudi Setiawan

doi:10.21831/elinvo.v8i1.55763

Classification of Corn Seed Quality using Residual Network with Transfer Learning Weight

Meidya Koeshardianto, University of Trunojoyo Madura, Indonesia
Wahyudi Agustiono, University of Trunojoyo Madura, Indonesia
Wahyudi Setiawan, University of Trunojoyo Madura, Indonesia

10.21831/elinvo.v8i1.55763

Abstract

Corn is one of the main ingredients in farm animal feed. Currently, corn is preferable because widely available and cheaper in the market than others. However, it needs quality control on corn production. The company that manufactures animal feed has certain quality standards to receive corn material. On the other hand, the quality of corn produced varies greatly. Thus, quality control when receiving corn from suppliers greatly affects the quality of animal feed. The quality of feed ingredients is classified into physical properties and analytical values. Physical properties are determined so that the resulting corn can be accepted or rejected, while the analytical value is used as the basis for formulating the diet. The physical properties of corn are determined by the human senses, such as sight and smell, while the analytical value is by chemical analysis. Physical quality control by relying on human senses is certainly limited and takes time. Based on these problems, it needs to make a classification system of corn seeds automatically. This study uses corn seed images as classification data. The system uses public data from Naagar which consists of four classes: pure, discolored, silk cut, and broken. Image classification uses a Convolutional Neural network (CNN) with ResNet152v2 architecture. The hyperparameters used consist of a learning rate of 0.001, a batch size of 512, and an epoch of 25. Adaptive Moment Estimation (Adam) for the optimizer. Percentage of data training vs validation 80:20. The validation results show an accuracy of 65%, precision of 66%, and recall of 64%.

Keywords

Corn Seed, Image Classification, Convolutional Neural Network, ResNet152v2, Transfer Learning

Full Text:

PDF

References

Kementrian Pertanian Republik Indonesia, “Indonesia Ekspor Jagung 372 Ribu Ton dan Menyetop Impor 9,2 Juta Ton,” https://www.pertanian.go.id/, 2018.

S. Panikkai, R. Nurmalina, S. Mulatsih, and H. Purwati, “Analisis Ketersediaan Jagung Nasional Menuju Swasembada Dengan Pendekatan Model Dinamik,” Inform. Pertan., vol. 26, no. 1, p. 41, 2017, doi: 10.21082/ip.v26n1.2017.p41-48.

Adzriral, D. Anggraini, N. Novita, Santosa, and Andasuryani, “Pendugaan Kualitas Fisik Biji jagung untuk Bahan Pakan menggunakan jaringan Syaraf Tiruan berdasarkan Data Citra Digital,” J. Peternak. Indones., vol. 13, no. 3, pp. 183–190, 2011.

S. Huang, X. Fan, L. Sun, Y. Shen, and X. Suo, “Research on Classification Method of Maize Seed Defect Based on Machine Vision,” J. Sensors, vol. 2019, no. 1, 2019, doi 10.1155/2019/2716975.

S. Nagar, P. Pani, R. Nair, and G. Varma, “Automated Seed Quality Testing System using GAN & Active Learning,” pp. 1–9, 2021, [Online]. Available: http://arxiv.org/abs/2110.00777.

S. Javanmardi, S. H. Miraei Ashtiani, F. J. Verbeek, and A. Martynenko, “Computer-vision classification of corn seed varieties using deep convolutional neural network,” J. Stored Prod. Res., vol. 92, p. 101800, 2021, doi: 10.1016/j.jspr.2021.101800.

P. Xu, Q. Tan, Y. Zhang, X. Zha, S. Yang, and R. Yang, “Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning,” Agric., vol. 12, no. 2, 2022, doi: 10.3390/agriculture12020232.

T. Michalik and O. Polska, “How effective is Transfer Learning method for image classification,” in Conference on Computer Science and Information Systems, 2017, vol. 12, pp. 3–9, doi: 10.15439/2017F526.

J. L. Mahendra Kumar et al., “The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline,” ICT Express, vol. 7, no. 4, pp. 421–425, 2021, doi: 10.1016/j.icte.2021.01.004.

H. Salman, A. Ilyas, L. Engstrom, A. Kapoor, and A. Madry, “Do adversarially robust ImageNet models transfer better?,” Adv. Neural Inf. Process. Syst., vol. 2020-Decem, no. NeurIPS, 2020.

K. Pandya, P. Singhal, K. Pandya, and P. Singhal, “Image Classi fi cation using Transfer Learning,” Int. J. Control Theory Appl., vol. 9, no. 40, pp. 899–905, 2016.

R. Hu, S. Zhang, P. Wang, G. Xu, D. Wang, and Y. Qian, “The identification of corn leaf diseases based on transfer learning and data augmentation,” in Proceedings of the 2020 3rd International Conference on Computer Science and Software Engineering, 2020, pp. 58–65, doi: https://doi.org/10.1145/3403746.3403905.

S. Albawi, T. A. M. Mohammed, and S. Alzawi, “Layers of a Convolutional Neural Network,” in ICET2017, 2017, pp. 1–6.

J. Gu et al., “Recent advances in convolutional neural networks,” Pattern Recognit., vol. 77, pp. 354–377, 2018, doi: 10.1016/j.patcog.2017.10.013.

K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” pp. 1–11, 2015, [Online]. Available: http://arxiv.org/abs/1511.08458.

M. Z. Alom et al., “The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches,” 2018, [Online]. Available: http://arxiv.org/abs/1803.01164.

Y. Wu, “Identification of Maize Leaf Diseases based on Convolutional Neural Network,” J. Phys. Conf. Ser., vol. 1748, no. 3, 2021, doi: 10.1088/1742-6596/1748/3/032004.

M. Simon, E. Rodner, and J. Denzler, “ImageNet pre-trained models with batch normalization,” 2016, [Online]. Available: http://arxiv.org/abs/1612.01452.

T. Gevers and A. Smeulders, “Identity Mappings in Deep Residual Networks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9914 LNCS, p. V, 2016, doi: 10.1007/978-3-319-46493-0.

S. Zagoruyko and N. Komodakis, “Wide Residual Networks,” Br. Mach. Vis. Conf. 2016, BMVC 2016, vol. 2016-Septe, pp. 87.1-87.12, 2016, doi: 10.5244/C.30.87.

T. F. Yu, Fisher, Vladlen Koltun, “Segmentation Dilated Residual Networks,” Proc. IEEE Conf. Comput. Vis. pattern Recognit., pp. 472–480, 2017, [Online]. Available: http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_Dilated_Residual_Networks_CVPR_2017_paper.pdf%0Ahttp://openaccess.thecvf.com/content_cvpr_2017/html/Yu_Dilated_Residual_Networks_CVPR_2017_paper.html.

S. M. Rezaeijo, M. Ghorvei, and B. Mofid, “Predicting breast cancer response to neoadjuvant chemotherapy using ensemble deep transfer learning based on CT images,” J. Xray. Sci. Technol., vol. 29, no. 5, pp. 835–850, 2021, doi: 10.3233/XST-210910.

N. M. Elshennawy and D. M. Ibrahim, “Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images,” Diagnostics, vol. 10, no. 9, pp. 1–16, 2020, doi: 10.3390/diagnostics10090649.

M. Huh, P. Agrawal, and A. A. Efros, “What makes ImageNet good for transfer learning?,” pp. 1–10, 2016, [Online]. Available: http://arxiv.org/abs/1608.08614.

R. Marlow et al., “A phase III, open-label, randomized multicentre study to evaluate the immunogenicity and safety of a booster dose of two different reduced antigen diphtheria-tetanus-acellular pertussis-polio vaccines, when co-administered with the measles-mumps-rubella vaccine,” Vaccine, vol. 36, no. 17, pp. 2300–2306, 2018, doi: 10.1016/j.vaccine.2018.03.021.

S. Kornblith, J. Shlens, and Q. V Le, “Kornblith_Do_Better_ImageNet_Models_Transfer_Better_CVPR_2019_paper,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 2661–2671, 2019.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in ICLR, 2015, pp. 1–15, doi: http://doi.acm.org.ezproxy.lib.ucf.edu/10.1145/1830483.1830503.

S. Y. Sen and N. Ozkurt, “Convolutional Neural Network Hyperparameter Tuning with Adam Optimizer for ECG Classification,” Proc. - 2020 Innov. Intell. Syst. Appl. Conf. ASYU 2020, no. 978, 2020, doi: 10.1109/ASYU50717.2020.9259896.

Milan, “Maize Disease using VGG16 and ADAM,” Kaggle, 2019. https://www.kaggle.com/milan400/0-00001adam-cornmaizeleaf-vgg16.

G. E. Hinton, N. Srivastava, and K. Swersky, “Lecture 6a- overview of mini-batch gradient descent,” COURSERA Neural Networks Mach. Learn., p. 31, 2012, [Online]. Available: http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf.

S. Ruder, “An overview of gradient descent optimization,” pp. 1–14, 2017.

P. Goyal et al., “Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour,” 2017, [Online]. Available: http://arxiv.org/abs/1706.02677.

R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, “Performance of deep learning vs machine learning in plant leaf disease detection,” Microprocess. Microsyst., vol. 80, no. October 2020, p. 103615, 2021, doi 10.1016/j.micpro.2020.103615.

A. Ramezani-Kebrya, A. Khisti, and B. Liang, “On the Generalization of Stochastic Gradient Descent with Momentum,” no. 2015, pp. 1–36, 2021, [Online]. Available: http://arxiv.org/abs/2102.13653.

DOI: https://doi.org/10.21831/elinvo.v8i1.55763

Refbacks

There are currently no refbacks.

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

Our Journal indexed by:

ISSN 2477-2399 (online) || ISSN 2580-6424 (print)

View My Stats

Username
Password
Remember me