Klasifikasi Citra Bibit Tanaman Menggunakan Convolutional Neural Network Dan Improved Feature Pyramid Network

MUHAMMAD ICHWAN; RIZKIKA SITI SYIFA

doi:10.26760/mindjournal.v8i1.1-13

Klasifikasi Citra Bibit Tanaman Menggunakan Convolutional Neural Network Dan Improved Feature Pyramid Network

MUHAMMAD ICHWAN, RIZKIKA SITI SYIFA

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Abstrak

Klasifikasi bibit tanaman bertujuan untuk membantu mempermudah pengendalian jenis tanaman. Tugas klasifikasi tanaman menggunakan metode manual rentan terhadap kesalahan manusia. Pada penelitian ini, CNN dan Improved FPN diimplementasikan untuk meningkatkan akurasi pada saat melakukan tugas klasifikasi. Peningkatan FPN dilakukan untuk meningkatkan kualitas informasi yang didapatkan fitur, dengan menerapkan Channel Attention Module dan Augmented Bottom-up Pathway. Arsitektur ResNet50 digunakan sebagai backbone konvolusi FPN untuk meningkatkan kemampuan FPN mengekstraksi fitur. CNN kemudian diterapkan pada setiap peta fitur FPN akhir untuk mengklasifikasikan data. Hasil pengujian menunjukkan model memiliki kinerja lebih baik ketika FPN ditingkatkan dengan Channel Attention Module dan Augmented Bottom-up Pathway dengan rasio pengurangan Channel Attention diatur ke nilai 4 dengan akurasi pengujian yaitu 93,11% dan skor F1 yaitu 93%.

Kata kunci: bibit tanaman, cnn, fpn, resnet50, channel attention module, augmented bottom-up pathway, klasifikasi citra

Abstract

Plant seedlings classification aims to help facilitate plant species control. The plant classification task using manual methods is prone to human error. In this study, CNN and Improved FPN were implemented to increase accuracy when performing the classification task. The FPN improvement was done to improve the quality of information obtained by the features, by implementing Channel Attention Module and Augmented Bottom-up Pathway. ResNet50 architecture was used as the convolutional backbone in FPN to enhance the feature extraction capabilities. CNN was then applied to each of FPN final feature maps to classify the data. The test results showed that the model performed better when the FPN was improved with the Channel Attention Module and Augmented Bottom-up Pathway where the Channel Attention reduction ratio was set to 4 with test accuracy of 93.11% and F1 score of 93%.

Keywords: plant seedlings, cnn, fpn, resnet50, channel attention module, augmented bottom-up pathway, image classification

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Referensi

Alimboyong, C. R., Hernandez, A. A., & Medina, R. P. (2018). Classification of Plant Seedling Images Using Deep Learning. TENCON 2018 - 2018 IEEE Region 10 Conference, 1839â€“1844. https://doi.org/10.1109/TENCON.2018.8650178

Ashqar, B. A. M., Abu-Nasser, B. S., & Abu-Naser, S. S. (2019). Plant seedlings classification using deep learning. International Journal of Academic Information Systems Research (IJAISR), 7â€“14.

Duong, L. T., Nguyen, P. T., di Sipio, C., & di Ruscio, D. (2020). Automated fruit recognition using EfficientNet and MixNet. Computers and Electronics in Agriculture, 171, 105326. https://doi.org/10.1016/j.compag.2020.105326

Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151, 72â€“80. https://doi.org/10.1016/j.biosystemseng.2016.08.024

Giselsson, T. M., JÃ¸rgensen, R. N., Jensen, P. K., Dyrmann, M., & Midtiby, H. S. (2017). A public image database for benchmark of plant seedling classification algorithms. ArXiv Preprint ArXiv:1711.05458.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770â€“778. https://doi.org/10.1109/CVPR.2016.90

Lee, I., Kim, D., Wee, D., & Lee, S. (2021). An Efficient Human Instance-Guided Framework for Video Action Recognition. Sensors, 21(24), 8309. https://doi.org/10.3390/s21248309

Lin, T.-Y., DollÃ¡r, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2117â€“2125.

Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path Aggregation Network for Instance Segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8759â€“8768. https://doi.org/10.1109/CVPR.2018.00913

Mustafa, M. S., Husin, Z., Tan, W. K., Mavi, M. F., & Farook, R. S. M. (2020). Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Computing and Applications, 32(15), 11419â€“11441. https://doi.org/10.1007/s00521-019-04634-7

Nie, X., Duan, M., Ding, H., Hu, B., & Wong, E. K. (2020). Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images. IEEE Access, 8, 9325â€“9334. https://doi.org/10.1109/ACCESS.2020.2964540

Pacifico, L. D. S., Macario, V., & Oliveira, J. F. L. (2018). Plant Classification Using Artificial Neural Networks. 2018 International Joint Conference on Neural Networks (IJCNN), 1â€“6. https://doi.org/10.1109/IJCNN.2018.8489701

Patil, A. (2021). Image Recognition using Machine Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3835625

Phung, V. H., & Rhee, E. J. (2018). A deep learning approach for classification of cloud image patches on small datasets. Journal of Information and Communication Convergence Engineering, 16(3), 173â€“178. https://doi.org/10.6109/jicce.2018.16.3.173

Rahimzadeh, M., Attar, A., & Sakhaei, S. M. (2021). A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset. Biomedical Signal Processing and Control, 68, 102588. https://doi.org/10.1016/j.bspc.2021.102588

Rai, I. N. (2018). Dasar-Dasar Agronomi. Percetakan Pelawa Sari.

Sahli, H. (2020). An Introduction to Machine Learning. In TORUS 1 â€“ Toward an Open Resource Using Services (pp. 61â€“74). Wiley. https://doi.org/10.1002/9781119720492.ch7

Shobha, G., & Rangaswamy, S. (2018). Machine Learning (pp. 197â€“228). https://doi.org/10.1016/bs.host.2018.07.004

Silva, L. O. L. A., Koga, M. L., Cugnasca, C. E., & Costa, A. H. R. (2013). Comparative assessment of feature selection and classification techniques for visual inspection of pot plant seedlings. Computers and Electronics in Agriculture, 97, 47â€“55. https://doi.org/10.1016/j.compag.2013.07.001

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). CBAM: Convolutional Block Attention Module (pp. 3â€“19). https://doi.org/10.1007/978-3-030-01234-2_1

Yan, D., Li, G., Li, X., Zhang, H., Lei, H., Lu, K., Cheng, M., & Zhu, F. (2021). An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images. Remote Sensing, 13(11), 2052. https://doi.org/10.3390/rs13112052

DOI: https://doi.org/10.26760/mindjournal.v8i1.1-13