Deep CNNBased Detection for Tea Clone Identification

       Ade Ramdan, Endang Suryawati, R. Budiarianto Suryo Kusumo, Hilman F. Pardede, Oka Mahendra, Rico Dahlan, Fani Fauziah, Heri Syahrian

Abstract


One factor affecting the quality of tea is the selection of plant material that would be planted on the field. Clonal selection is a common way to produce tea with better quality. However, as a natural cross pollination species, tea often consists of various clones or progenies of cross-pollinated process. This commonly occurs on plantations owned by smallholder farmers. To produce a consistent quality tea, the clones or progenies need to be identified. Usually, human experts distinguish the plants from leaves by visual inspection on the physical attributes of the leaves, such as the textures, the bone structures, and the colors. It is very difficult for non-experts or common farmers to do such identifications. In this, we propose a deep learning-based identification of tea clones. We apply deep convolutional neural network (CNN) to identify 3 types of tea clones of Gambung series, a series of tea clones developed at Research Institute of Tea and Cinchona. Our study indicates that the performance of the CNN systems are affected by the depth of the convolutional layers. VGGNet, a popular CNN architectures with 16 layers, achieves better accuracy compared to AlexNet, a CNN with 6 layers.

  http://dx.doi.org/10.14203/jet.v19.45-50

Keywords


Convolutional neural network; deep learning; gambung clone series; tea clones identification

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References


Dedi Soleh Effendi, M. Syakir, M. Yusron, and Wiratno, "Budidaya dan Pasca Panen The," Pusat Penelitian dan Pengembangan Perkebunan, 2010.

(2019) Gambung website [online]. Available: https://www.gamboeng.com/pages/detail/2015/59/146

Xueyan Wu, Jiquan Yang, and Shuihua Wang, "Tea category Identification Based on Optimal Wavelet Entropy and Weighted K-Nearest Neighbors Algorithm," Journal Multimedia Tools and Applications, vol. 77, no. 3, pp. 3745-3759, 2018. Crossref

Shuihua Wang, Preetha Phillips, Aijun Liu, and Sidan Du, "Tea Category Identification using Computer Vision and Generalized Eigenvalue Proximal SVM," Journal Fundamenta Informaticae, vol. 151, no. 1-4, pp. 325-339, 2017. Crossref

Shuihua Wang, Xiaojun Yang, Yudong Zhang, Preetha Phillips, Jianfei Yang, and Ti-Fei Yuan, "Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine," Journal Entropy, vol. 17, no. 10, pp. 6663-6682, 2015. Crossref

B. Karunamoorthy, and D. Somasundereswari, "A DefectTea Leaf Identification Using Image Processing," Journal Przeglad Elektrotechniczny, vol. 89, no. 9, pp. 318-320, 2013.

Christophorus Candra Kusumadewaand Supatman, "Identifikasi Citra Daun Teh Menggunakan Metode Histogram untuk Deteksi Dini Serangan Awal Hama Empoasca," Journal Multimedia & Artificial Intelligence, vol. 2, no. 1, pp. 27-36, 2018. Crossref

Qing Xia, Hao-Dong Zhu, & Yong Gan, andLi Shang, "Plant Leaf Recognition Using Histograms of Oriented Gradients," in International Conference on Intelligent Computing, vol. 8589, 2014, pp. 369-374. Crossref

D. G. Tsolakidis, D. I. Kosmopoulos, and G. Papadourakis, "Plant leaf recognition using zernike moments and histogram of oriented gradients," in Hellenic Conference on Artificial Intelligence, 2014, pp. 406-417. Crossref

Voncarlos A., Alceu S. B., Andre L. B., Alessandro L. K., and Rosane F., "Multiple Classifier System for Plant Leaf Recognition," in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp 1880-1885. Crossref

Yogesh Dandawate and Radha Kokare, "An automated approach for classification of plant diseases towards development of futuristic decision supportsystem in indian perspective," in 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015, pp. 794-799. Crossref

Anne-Katrin Mahlein, "Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping," Plant Disease, vol. 100, no. 2, pp. 241-251, 2016. Crossref

Godliver Owomugisha, Friedrich Melchert, Ernest Mwebaze, John A. Quinn, and Michael Biehl, "Machine Learning for Diagnosis of Disease in Plants using Spectral Data," in International Conference on Artificial Intelligence (ICAI), 2018, pp. 9-15.

Yu Sun, Yuan Liu, Guan Wang, and Haiyan Zhang, "Deep Learning for Plant Identification in Natural Environment," Computational Intelligence and Neuroscience, vol. 2017, 2017. Crossref

Ajeet Ram Pathak, Manjusha Pandey, and Siddharth Rautaray, "Application of Deep Learning for Object Detection," in Procedia Computer Science, vol. 132, pp. 1706-1717, 2018. Crossref

T. L. I. Sugata, and C. K. Yang, "Leaf App: Leaf Recognition with Deep Convolutional Neural Networks," in IOP Conference Series: Materials Science and Engineering, vol. 273, 2017. Crossref

Agustinus Kristiadi and Pranowo, "Deep Convolutional Level Set Method for Image Segmentation," Journal of ICT Research and Applications, vol. 11, no. 3, pp. 284-298, 2017. Crossref

Russakovsky, O., Deng, J., Su, H. et al., "ImageNet Large Scale Visual Recognition Challenge," International Journal of Computer Vision, vol. 115, no. 3, pp 211-252, 2015. Crossref

Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Neural Information Processing Systems, pp. 1097-1105, 2012.

Srdjan Sladojevic, Marko Arsenovic, Andras Anderla, Dubravko Culibrk, and Darko Stefanovic, "Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification," Journal Computational Intelligence and Neuroscience, vol. 2016, 2016. Crossref

Sharada P. Mohanty, David P.Hughes and Marcel Salathé, "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, 2016.

Yu-Dong Zhang, Khan Muhammad, and Chaosheng Tang, "Twelve-layer Deep Convolutional Neural Network with Stochastic Pooling for Tea Category Classification on GPU Platform," Journal Multimedia Tools and Applications, vol. 77, no. 17, pp. 22821-22839, 2018. Crossref

Guan Wang, Yu Sun, and Jianxin Wang, "Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning," Journal Computational Intelligence and Neuroscience, vol. 2017, 2017. Crossref

Endang Suryawati, Rika Sustika, R. Sandra Yuwana, Agus Subekti, and Hilman Pardede, "Deep Structured Convolutional Neural Network for Tomato Diseases Detection," in International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2018, pp. 385-390. Crossref

Karen Simonyan and Andrew Zisserman, "Very Deep Convolutional Networks forLarge-Scale Image Recognition," arXiv preprint arXiv:1409.1556, 2014.

Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, "Backpropagation Applied to Handwritten Zip Code Recognition," Neural Computation, vol. 1, no. 4, pp. 541-551, 1989. Crossref

Diederik P. Kingma and Jimmy Lei Ba, "Adam: A Method for Stochastic Optimization," arXiv preprint arXiv:1412.6980, 2014.

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," arXiv preprint, 2017.

Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Richard S. Zemel, and Yoshua Bengio, "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention," in International Conference on Machine Learning, 2015, pp. 2048-2057.

Yoshua Bengio, Aaron Courville, and Pascal Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, 2013. Crossref

Yan Xu, Tao Mo, Qiwei Feng, Peilin Zhong, Maode Lai, and Eric I-Chao Chang, "Deep Learning of Feature Representation with Multiple Instance Learning for Medical Image Analysis," in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014, pp. 1626-1630. Crossref

Frédéric Li, Kimiaki Shirahama, Muhammad Adeel Nisar, Lukas Köping, and Marcin Grzegorzek, "Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors," Sensors, vol.18, no. 2, 679, 2018. Crossref


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