Oil Palm Fruit Image Ripeness Classification with Computer Vision using Deep Learning and Visual Attention

Authors

  • Herman Herman Computer Science Department, BINUS Graduate Program – Master in Computer Science, Bina Nusantara University,Jakarta, Indonesia 11480.Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia 11480, Indonesia
  • Albert Susanto Computer Science Department, BINUS Graduate Program – Master in Computer Science, Bina Nusantara University,Jakarta, Indonesia 11480.Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia 11480, Indonesia
  • Tjeng Wawan Cenggoro Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480.Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia 11480, Indonesia
  • Suharjito Suharjito Computer Science Department, BINUS Graduate Program – Master in Computer Science, Bina Nusantara University,Jakarta, Indonesia 11480, Indonesia
  • Bens Pardamean Computer Science Department, BINUS Graduate Program – Master in Computer Science, Bina Nusantara University,Jakarta, Indonesia 11480.Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia 11480, Indonesia

Keywords:

Computer Vision, Convolutional Neural Network, Oil Palm Fruit Classification, Visual Attention,

Abstract

Oil palm is one of the leading agricultural industries, especially in the South East Asian region. However, oil palm fruit ripeness classification based on computer vision has not gained many satisfactory results. Therefore, most of the ripeness sorting processes are still done manually by labor works. The objective of this research is to develop a model using a residual-based attention mechanism that could recognize the small detail differences between images. Thus, the model could classify oil palm fruit ripeness better. The dataset consists of 400 images with seven levels of ripeness. Since the number of images in the dataset, Ten Crop preprocessing is utilized to augment the data. The experiment showed that the proposed model ResAtt DenseNet model, which uses residual visual attention could improve the F1 Score by 1.1% compared to the highest F1 Score from other models in the experiment of this study.

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Published

2020-06-30

How to Cite

Herman, H., Susanto, A., Cenggoro, T. W., Suharjito, S., & Pardamean, B. (2020). Oil Palm Fruit Image Ripeness Classification with Computer Vision using Deep Learning and Visual Attention. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 12(2), 21–27. Retrieved from https://jtec.utem.edu.my/jtec/article/view/5543

Issue

Vol. 12 No. 2: April - June 2020

Section

Articles

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