Hyperparameter Optimization in Convolutional Neural Networks for Maize Seed Classification

Main Article Content

Sertuğ FİDAN
https://orcid.org/0000-0002-3458-7618
Ali Murat Tiryaki
https://orcid.org/0000-0001-8224-6319

Abstract

Corn farming is of great importance for the continuity of our society. Because corn is a cheap and efficient food, especially for animal feeding. However, with the Doubled-haploid technique, the selection of the haploid seeds necessary for this job to be done efficiently creates a problem. Today, the selection of haploid seeds is usually done by trained technicians. With the development of machine learning methods, the parts expected from technicians can be made by machines. In this study, a new model architecture based on a convolutional neural network (CNN) was produced to perform the selection of haploid seeds and the hyperparameters of this model were optimized with the use of tree-structured parzen estimator algorithm. The newly produced model achieved a 94.66% validation score, higher than the VGG-19 model, which proved to be relatively efficient.

Downloads

Download data is not yet available.

Article Details

How to Cite
FİDAN, S., & Tiryaki, A. M. (2023). Hyperparameter Optimization in Convolutional Neural Networks for Maize Seed Classification. The European Journal of Research and Development, 3(1), 139–149. https://doi.org/10.56038/ejrnd.v3i1.254
Section
Articles

References

I. Cerıt, G. Comertpay, R. Oyucu, B. Cakir, R. Hatipoglu, and H. Ozkan. (2016). Melez mısır islahında in-vivo katlanmıs ̧ haploid tekniginde kullanılan farklı inducer genotiplerin haploid ̇Indirgeme oranların belirlenmesi, Tarla Bitkileri Merkez Araştırma Enstitüsü Dergisi, vol. 25, no. OZEL SAYI-1, pp. 52 – 57. DOI: https://doi.org/10.21566/tarbitderg.280162

Y. Altuntas ̧, Z. Comert, and A. F. Kocamaz. (2019). Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach, Computers and Electronics in Agriculture, vol. 163, p. 104874,[Online]. Available: https://www.sciencedirect.com/science/article/pii/S0168169919300481 DOI: https://doi.org/10.1016/j.compag.2019.104874

H. H. Geiger, G. Andr ́es Gordillo, and S. Koch. (2013). Genetic correlations among haploids, doubled haploids, and testcrosses in maize, Crop Science, vol. 53, no. 6, pp. 2313–2320, [Online]. Available: https://acsess.onlinelibrary.wiley.com/doi/abs/10.2135/cropsci2013.03.0163 DOI: https://doi.org/10.2135/cropsci2013.03.0163

Le Cun BB, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD. (1990). Handwritten digit recognition with a back-propagation network, In: Advances in Neural Information Processing Systems. Denver: Morgan-Kaufmann.

B. Veeramani, J. W. Raymond, and P. Chanda. (2018). Deepsort: deep convolutional networks for sorting haploid maize seeds, BMC bioinformatics, vol. 19, no. 9, pp. 1–9. DOI: https://doi.org/10.1186/s12859-018-2267-2

E. Dönmez. (2020). Classification of haploid and diploid maize seeds based on pre-trained convolutional neural networks, Celal Bayar University Journal of Science, vol. 16, no. 3, pp. 323–331. DOI: https://doi.org/10.18466/cbayarfbe.742889

Bergstra, J., Yamins, D., Cox, D. D. (2013). Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures, TProc. of the 30th International Conference on Machine Learning (ICML 2013), pp. I-115 to I-23.

J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl. (2011). Algorithms for Hyper-Parameter Optimization, in Advances in Neural Information Processing Systems 24, J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, Eds. Curran Associates, Inc., pp. 2546–2554.

J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller. (2014). Striving for simplicity: The all convolutional net, [Online].Available: https://arxiv.org/abs/1412.6806

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi. (2016). Inception-v4,inception-resnet and the impact of residual connections on learning,[Online]. Available:https://arxiv.org/abs/1602.07261 DOI: https://doi.org/10.1609/aaai.v31i1.11231