Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks

Abstract

Analysis of agricultural products is an important area that is widely emphasized today. In this context, with the development of technology, computer-aided analysis systems are also being developed. In this study, a system has been proposed for classifying maize seeds as haploid and diploid using pre-trained convolutional neural networks. For this purpose, AlexNet, GoogLeNet, ResNet-18, ResNet-50, and VGG-16 pre-trained models have been used as feature extractors for the haploid and diploid seed classification process. In the first stage, the deep features of haploid and diploid maize seeds have been obtained in these models. The features have been taken from different layers of network architecture. Instead of softmax classifier in the last layer of the network, classifiers based on decision tree, k-nearest neighbor, and support vector machine have been used. According to the classification results with these features, the achievements in network architectures and classifier methods have been observed. The experiments have been carried out on a publicly available dataset consisting of 3000 haploid and diploid maize seed images. The experimental results revealed that the developed classification systems demonstrate a remarkable performance.

Keywords

• Maize Seed Identification
• Deep Features
• Artificial Learning
• Convolutional Neural Networks
• Image Processing

References

1. 1. Altuntaş, Y, Kocamaz, AF, Yeroğlu, C. Identification of Apricot Varieties Using Leaf Characteristics and KNN Classifier. International Conference on Artificial Intelligence and Data Processing Symposium: IDAP-2019, 2019.
2. 2. Dönmez, E, Zadeh, PV. A modified graph based approach for leaf segmentation with GPGPU support. 23rd Signal Processing and Communications Applications Conference: SIU-2015, 2015.
3. 3. Chidzanga, C, Muzawazi, F, Midzi, J, Hove, T. 2017. Production and use of haploids and doubled haploid in maize breeding: A review. African J. Plant Breed; 201-213.
4. 4. Prasanna, BM, Chaikam, V, Mahuku, G. 2012. Doubled haploid technology in maize breeding: theory and practice. CIMMYT.
5. 5. Röber, FK, Gordillo, GA, Geiger, HH. 2005. In vivo haploid induction in maize - Performance of new inducers and significance of doubled haploid lines in hybrid breeding. Maydica.
6. 6. Nanda, DK, Chase, SS. 1966. An Embryo Marker for Detecting Monoploids of Maize (Zea Mays L.1). Crop Sci; 6: 213-215.
7. 7. Altuntaş, Y, Kocamaz, AF, Cengiz, R, Esmeray, M. Classification of haploid and diploid maize seeds by using image processing techniques and support vector machines. 26th IEEE Signal Processing and Communications Applications Conference: SIU-2018, 2018.
8. 8. Lecun, Y, Bengio, Y, Hinton, G. 2015. Deep learning. Nature; 521: 436-444.

9. 9. Couto, EG de O, Davide, LMC, Bustamante, F de O, Von, Pinho RG, Silva, TN. 2013. Identification of haploid maize by flow cytometry, morphological and molecular markers. Ciência e Agrotecnologia.
10. 10. Boote, BW, Freppon, DJ, Fuente, GN de La, Lübberstedt, T, Nikolau, BJ, Smith, EA. 2016. Haploid differentiation in maize kernels based on fluorescence imaging. Plant Breed.
11. 11. Yu, L, Liu, W, Li, W, Qin, H, Xu, J, Zuo, M. 2018. Non-destructive identification of maize haploid seeds using nonlinear analysis method based on their near-infrared spectra Biosyst. Eng.
12. 12. Lin, J, Yu, L, Li, W, Qin, H. 2018. Method for Identifying Maize Haploid Seeds by Applying Diffuse Transmission Near-Infrared Spectroscopy. Appl. Spectrosc; 72: 611–617.
13. 13. Wang, Y. et al. 2018. Identification of maize haploid kernels based on hyperspectral imaging technology. Comput. Electron. Agric.
14. 14. Fuente, GN de La, Carstensen, JM, Edberg, MA, Lübberstedt, T. 2017. Discrimination of haploid and diploid maize kernels via multispectral imaging. Plant Breed.
15. 15. Wang, XY, Liao, WX, An, D, Wei, Y. 2018. Maize Haploid Identification via LSTM-CNN and Hyperspectral Imaging Technology. CoRR; abs/1805.0.
16. 16. Altuntaş, Y, Kocamaz, AF, Cömert, Z, Cengiz, R, Esmeray, M. Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques. International Conference on Artificial Intelligence and Data Processing, IDAP2018, 2019.
17. 17. Altuntaş, Y, Cömert, Z, Kocamaz, AF. 2019. Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach. Comput. Electron. Agric.
18. 18. Altuntaş, Y, Kocamaz, AF. 2019. Renk Momentleri ve Destek Vektör Makineleri Kullanarak Haploid Mısır Tohumlarının Tanımlanmasında Renk Uzaylarının Sınıflandırma Performansına Etkisinin Karşılaştırılması. Fırat Üniversitesi Mühendislik Bilim. Derg; 31: 551–560.
19. 19. Song, P, Zhang, H, Wang, C, Luo, B, Zhang, JX. 2018. Design and Experiment of a Sorting System for Haploid Maize Kernel. Int. J. Pattern Recognit. Artif. Intell.
20. 20. Krizhevsky, A, Sutskever, I, Hinton, GE. 2012. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems.
21. 21. He, K, Sun, J. Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778, 2016.
22. 22. Szegedy, C. et al. Going deeper with convolutions, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015.
23. 23. Simonyan, K. Zisserman, A. Very deep convolutional networks for large-scale image recognition, 3rd International Conference on Learning Representations: ICLR-2015, Conference Track Proceedings, 1–14, 2015.
24. 24. Zanaty, EA. 2012. Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification. Egypt. Informatics J.
25. 25. Guo, G., Wang, H, Bell, D, Bi, Y, Greer, K. 2013. KNN model-based approach in classification. Lect. Notes Comput. Sci.
26. 26. Kotsiantis, SB. Decision trees: A recent overview. Artificial Intelligence Review.