Classifying Java plum (Eugenia jambolana) Leaf for Tobacco Cigarette Wrapper using Convolutional Neural Network

James Arnold E. Nogra

Abstract


With the increasing prices of cigarettes and cigars, more and more smokers are looking for an alternative. One way to smoke without the use of traditional cigarettes, cigars, or electronic cigarettes is to roll tobacco leaves with Java plum leaves. It is difficult to select which leaves are to be used as cigarette covers because there are characteristics to be considered such as their texture, color, dryness and shape. This study aims to help or replace human experts in classifying Java Plum leaves using a Convolutional Neural Network Classifier. Tensorflow Inception V3 is retrained to classify the leaves of the Java plum into three categories, and with 1470 test images, the neural network model managed to achieve a 91.2% accuracy in the classification. The system needs more sample photos to get a higher accuracy rate.

Keywords


Java plum leaf classification; Convolutional neural network; Inception V3

Full Text:

JSET009

References


Ayyanar, M., & Subash-Babu, P. (2012). Syzygium cumini (L.) Skeels: A review of its phytochemical constituents and

traditional uses. Asian Pacific Journal of Tropical Biomedicine, 2(3), 240–246.

Boffetta, P., Pershagen, G., Jockel, K. H., Forastiere, F., Gaborieau, V., Heinrich, J., Jahn, I., Kreuzer, M., Merletti, F., Nyberg, F., Rosch, F., Simonato, L.(1999). Cigar and pipe smoking and lung cancer risk:

A multicenter study from Europe. Journal of the National Cancer Institute, 91(8), 697–701.

Charepalli, V., Reddivari, L., Vadde, R., Walia, S., Radhakrishnan, S., & Vanamala, J. K. P. (2016). Eugenia jambolana (Java plum) Fruit Extract Exhibits Anti-Cancer Activity against Early Stage Human HCT-116 Colon Cancer Cells and Colon Cancer Stem Cells. Cancers, 8(3), 29.

Guo, T., Dong, J., Li, H., & Gao, Y.(2017). ”Simple convolutional neural network on image classification. Paper presented at

IEEE 2nd International Conference on Big Data Analysis (ICBDA. China

Hedjazi, M. A., Kourbane, I., & Genc, Y. (2017). On identifying leaves: A comparison of CNN with classical ML methods. Paper presented at

the 2017 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey.

Katta S. & Babu M. S. P. (2017). ”An approach to classify flue-cured tobacco leaves using deep convolutional neural networks. Paper presented at the 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.

Raj, B., (2018, May). A simple Guide to the versions of the

Inception network. Retrieved from https://towardsdatascience.com/a- simple-guide-to-the-versions-of-the-inception-network-7fc52b863202

Shah, M. P., Singha , S., & S. P. Awate. (2017). Leaf classification using marginalized shape context and shape+texture dual-path deep convolutional neural network. Presented at the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.

Xia, X., Xu, X., & Nan, B. (2017). Inception-v3 for flower classification. Paper presented at the 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.