(Technological Institute of the Philippines Manila, Batangas State University, Philippines)(2) Alexander Arsenio Hernandez
(Technological Institute of the Philippines Manila, Philippines)*corresponding author
AbstractThis work presents the application of recent Deep Convolutional Models (DCM) to classify Barako leaf diseases. Several selected DCMs performed image classification tasks using Transfer Learning and Fine-Tuning, together with data preprocessing and augmentation. The collected dataset used totals to 4,667. Each labeled into four different classes, which included Coffee Leaf Rust (CLR), Cercospora Leaf Spots (CLS), Sooty Molds (SM), and Healthy Leaves (HL). The DCMs were trained using the partial 4,023 images and validated with the remaining 644. The classification results of the trained models VGG16, Xception, and ResNetV2-152 attained overall accuracies of 97%, 95%, and 91%, respectively. By comparing in terms of True Positive Rate (TPR), we found that Xception has the highest number of correct classifications of CLR, VGG16 with SM, and CLS, while ResNetV2-152 with the lowest TPR for CLR. The evaluated results indicate that the use of Deep Convolutional Models with an adequate amount of data, proper fine-tuning, preprocessing, and transfer learning can yield efficient classifiers for identifying several Barako leaf diseases. This work primarily contributes to the growing field of deep learning, specifically for helping farmers improve their diagnostic process by providing a solution that can automatically classify Barako leaf diseases.
KeywordsDeep learning; Convolutional neural networks; Classification; Leaf disease; Barako coffee
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DOIhttps://doi.org/10.26555/ijain.v6i2.495 |
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References
[1] M. Sanders, "Breeding for Coffee Leaf Rust Resilience in Coffea sp," Natural Sciences Education, vol. 48, no. 1, 2019, doi: 10.4195/nse2019.01.0102.
[2] “2017-2022 Philippine Coffee Industry Roadmap,” https://da.gov.ph, 2017. [Online], available at: http://www.da.gov.ph/wp-content/uploads/2019/06/Philippine-Coffee-Industry-Roadmap-2017-2022.pdf. [Accessed: 5-Mar-2020].
[3] Bd, "Kapeng Barako Coffee Liqueur: Keeping The Spirit Of Batangas Coffee," Business Diary Philippines, Feb. 2019. [Online], available at: https://businessdiary.com.ph/16278/kapeng-barako-coffee-liqueur-keeping-the-spirit-of-batangas-coffee/. [Accessed: 17-Mar-2020].
[4] J. L. Badel and L. Zambolim, "Coffee bacterial diseases: a plethora of scientific opportunities," Plant Pathology, vol. 68, no. 3, pp. 411–425, Aug. 2019, doi: 10.1111/ppa.12966.
[5] R. Balodi, S. Bisht, A. Ghatak, and K. H. Rao, "Plant Disease Diagnosis: Technological Advancements and Challenges," Indian Phytopathology, vol. 70, no. 3, Jan. 2017, doi: 10.24838/ip.2017.v70.i3.72487.
[6] M. Riley, M. Williamson, and O. Maloy, "Plant Disease Diagnosis," Plant Health Instructor, 2002, doi: 10.1094/PHI-I-2002-1021-01.
[7] S. A. Miller, F. D. Beed, and C. L. Harmon, "Plant Disease Diagnostic Capabilities and Networks," Annual Review of Phytopathology, vol. 47, no. 1, pp. 15–38, 2009, doi: 10.1146/annurev-phyto-080508-081743.
[8] Y. Lecun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
[9] L. Yuan, Y. Huang, R. W. Loraamm, C. Nie, J. Wang, and J. Zhang, "Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects," Field Crops Research, vol. 156, pp. 199–207, 2014, doi: 10.1016/j.fcr.2013.11.012.
[10] A. Singh, B. Ganapathysubramanian, A. K. Singh, and S. Sarkar, "Machine Learning for High-Throughput Stress Phenotyping in Plants," Trends in Plant Science, vol. 21, no. 2, pp. 110–124, 2016, doi: 10.1016/j.tplants.2015.10.015.
[11] P. Jiang, Y. Chen, B. Liu, D. He and C. Liang, "Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks," in IEEE Access, vol. 7, pp. 59069-59080, 2019, doi: 10.1109/ACCESS.2019.2914929.
[12] F. J. P. Montalbo and A. A. Hernandez, "An Optimized Classification Model for Coffea Liberica Disease using Deep Convolutional Neural Networks," 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), Langkawi, Malaysia, 2020, pp. 213-218, doi: 10.1109/CSPA48992.2020.9068683.
[13] Z. Zheng, S. Pan and Y. Zhang, "Fruit Tree Disease Recognition Based on Convolutional Neural Networks," 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), Shenyang, China, 2019, pp. 118-122, doi: 10.1109/IUCC/DSCI/SmartCNS.2019.00048.
[14] A. P. Marcos, N. L. Silva Rodovalho and A. R. Backes, "Coffee Leaf Rust Detection Using Convolutional Neural Network," 2019 XV Workshop de Visão Computacional (WVC), São Bernardo do Campo, Brazil, 2019, pp. 38-42, doi: 10.1109/WVC.2019.8876931.
[15] J. G. Esgario, R. A. Krohling, and J. A. Ventura, "Deep learning for classification and severity estimation of coffee leaf biotic stress," Computers and Electronics in Agriculture, vol. 169, p. 105162, 2020, doi: 10.1016/j.compag.2019.105162.
[16] J. G. A. Barbedo, "A new automatic method for disease symptom segmentation in digital photographs of plant leaves," European Journal of Plant Pathology, vol. 147, no. 2, pp. 349–364, 2016, doi: 10.1007/s10658-016-1007-6.
[17] J. Bergstra and Y. Bengio, "Random search for hyper-parameter optimization," Journal of machine learning research, vol. 13, pp. 281-305, 2012, doi: 10.5555/2188385.2188395.
[18] S. Verma, A. Chug, A. P. Singh, S. Sharma, and P. Rajvanshi, "Deep Learning-Based Mobile Application for Plant Disease Diagnosis," Advances in Environmental Engineering and Green Technologies Applications of Image Processing and Soft Computing Systems in Agriculture, pp. 242–271, 2019, doi: 10.4018/978-1-5225-8027-0.ch010.
[19] F. J. P. Montalbo and A. A. Hernandez, "Classification of Fish Species with Augmented Data using Deep Convolutional Neural Network," 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), Shah Alam, Malaysia, 2019, pp. 396-401, doi: 10.1109/ICSEngT.2019.8906433.
[20] G. Castaneda, P. Morris, and T. M. Khoshgoftaar, "Evaluation of maxout activations in deep learning across several big data domains," Journal of Big Data, vol. 6, no. 1, Mar. 2019, doi: 10.1186/s40537-019-0233-0.
[21] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014, available at: https://arxiv.org/abs/1409.1556.
[22] C. Szegedy et al., "Going deeper with convolutions," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 1-9, doi: 10.1109/CVPR.2015.7298594.
[23] F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 1800-1807, doi: 10.1109/CVPR.2017.195
[24] K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 770-778, doi: https://doi.org/10.1109/CVPR.2016.90.
[25] K. He, X. Zhang, S. Ren, and J. Sun, "Identity mappings in deep residual networks," in Proc. Europ. Conf. Comput. Vision, 2016, pp. 630–645, doi: 10.1007/978-3-319-46493-0_38.
[26] R. Atienza, "Advanced deep learning with Keras: apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy tradients, and more", Birmingham: Packt, p. 59, 2018, available at: https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788629416/2/ch02lvl1sec13/resnet-v2.
[27] S. Dodge and L. Karam, "Understanding how image quality affects deep neural networks," in Proc. 8th Int. Conf. Quality Multimedia Exper. (QoMEX), Jun. 2016, pp. 1–6, doi: 10.1109/QoMEX.2016.7498955.
[28] D. Lu and Q. Weng, "A survey of image classification methods and techniques for improving classification performance," Int. J. Remote Sens., vol. 28, no. 5, pp. 823–870, 2007, doi: 10.1080/01431160600746456.
[29] K. K. Pal and K. S. Sudeep, "Preprocessing for image classification by convolutional neural networks," 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, 2016, pp. 1778-1781, doi: 10.1109/RTEICT.2016.7808140.
[30] B. L. Boese, P. J. Clinton, D. Dennis, R. C. Golden, and B. Kim, "Digital image analysis of Zostera marina leaf injury," Aquatic Botany, vol. 88, no. 1, pp. 87–90, 2008, doi: 10.1016/j.aquabot.2007.08.016.
[31] P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár, “Learning to refine object segments,” in Proc. Eur. Conf. Comput. Vis., 2016, pp. 75–91, doi: 10.1007/978-3-319-46448-0_5.
[32] X. Zhang, Y. Qiao, F. Meng, C. Fan and M. Zhang, "Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks," in IEEE Access, vol. 6, pp. 30370-30377, 2018, doi: 10.1109/ACCESS.2018.2844405.
[33] A. Krizhevsky, A., and G. Hinton, "Convolutional deep belief networks on cifar-10," Unpublished manuscript, vol. 40, no. 7, pp. 1-9, 2010, available at: https://www.cs.toronto.edu/~kriz/conv-cifar10-aug2010.pdf.
[34] R. Chauhan, K. K. Ghanshala and R. C. Joshi, "Convolutional Neural Network (CNN) for Image Detection and Recognition," 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, 2018, pp. 278-282, doi: 10.1109/ICSCCC.2018.8703316.
[35] F. Chollet, Deep Learning With Python. Shelter Island, NY, USA: Manning, 2018, pp.138-140, available at: Google Scholar.
[36] K. Kobayashi, J. Tsuji and M. Noto, "Evaluation of Data Augmentation for Image-Based Plant-Disease Detection," 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, pp. 2206-2211, doi: 10.1109/SMC.2018.00379.
[37] N. McLaughlin, J. M. Del Rincon and P. Miller, "Data-augmentation for reducing dataset bias in person re-identification," 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Karlsruhe, 2015, pp. 1-6, doi: 10.1109/AVSS.2015.7301739.
[38] G. C. Cawley and N. L. Talbot, "Over-fitting in model selection and subsequent selection bias in performance evaluation," J. Machine Learn. Res., vol. 11, pp. 2079–2107, Jul. 2010, doi: 10.5555/1756006.1859921.
[39] T. Badriyah, D. B. Santoso, I. Syarif, and D. R. Syarif, "Improving stroke diagnosis accuracy using hyperparameter optimized deep learning," International Journal of Advances in Intelligent Informatics, vol. 5, no. 3, p. 256, 2019, 10.26555/ijain.v5i3.427.
[40] S. Nazir, S. Patel and D. Patel, "Hyper Parameters Selection for Image Classification in Convolutional Neural Networks," 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Berkeley, CA, 2018, pp. 401-407, doi: 10.1109/ICCI-CC.2018.8482081.
[41] N. S. Keskar and R. Socher. (Dec. 2017). "Improving generalization performance by switching from Adam to SGD," [Online]. Available: https://arxiv.org/abs/1712.07628
[42] H. Tercan, A. Guajardo, J. Heinisch, T. Thiele, C. Hopmann, and T. Meisen, "Transfer-learning: Bridging the gap between real and simulation data for machine learning in injection molding," Procedia CIRP, vol. 72, pp. 185–190, 2018, doi: 10.1016/j.procir.2018.03.087.
[43] S. J. Pan and Q. Yang, "A Survey on Transfer Learning," in IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, Oct. 2010, doi: 10.1109/TKDE.2009.191.
[44] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017, doi: 10.1145/3065386.
[45] L. Shao, F. Zhu and X. Li, "Transfer Learning for Visual Categorization: A Survey," in IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 5, pp. 1019-1034, May 2015, doi: 10.1109/TNNLS.2014.2330900.
[46] I. Goodfellow, Y. Bengio, and A. Courville, "Deep Learning". Cambridge, MA, USA: MIT Press, 2016, available at: https://www.deeplearningbook.org/.
[47] R. Y. Rubinstein and D. P. Kroese, "The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning", Springer Science & Business Media, 2013, doi: 10.1007/978-1-4757-4321-0.
[48] D. M. Hawkins, "The Problem of Overfitting," Journal of Chemical Information and Computer Sciences, vol. 44, no. 1, pp. 1–12, 2004, doi: 10.1021/ci0342472.
[49] J. G. A. Barbedo, "Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification," Computers and Electronics in Agriculture, vol. 153, pp. 46–53, 2018, doi: 10.1016/j.compag.2018.08.013.
[50] M. Hossin and M. N. Sulaiman, "A review on evaluation metrics for data classification evaluations," Int. J. Data Mining Knowl. Manage. Process, vol. 5, no. 2, pp. 1–11, 2015, doi: 10.5121/ijdkp.2015.5201.
[51] Q. Liang, S. Xiang, Y. Hu, G. Coppola, D. Zhang, and W. Sun, "PD 2 SE-Net: Computer-assisted plant disease diagnosis and severity estimation network," Comput. Electron. Agric., vol. 157, pp. 518–529, Feb. 2019, doi: 10.1016/j.compag.2019.01.034.
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