Rice Disease Recognition Using Effective Deep Neural Networks

Authors

  • S. Mathulaprangsan Department of Computer Engineering, Faculty of Engineering at Kamphaeng Sean, Kasetsart University, Thailand https://orcid.org/0000-0002-8810-4183
  • S. Patarapuwadol Department of Plant Pathology, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University, Thailand https://orcid.org/0000-0002-8337-4096
  • K. Lanthong Department of Computer Engineering, Faculty of Engineering at Kamphaeng Sean, Kasetsart University, Thailand
  • D. Jetpipattanapong Department of Computer Engineering, Faculty of Engineering at Kamphaeng Sean, Kasetsart University, Thailand https://orcid.org/0000-0003-2464-7223
  • S. Sateanpattanakul Department of Computer Engineering, Faculty of Engineering at Kamphaeng Sean, Kasetsart University, Thailand

DOI:

https://doi.org/10.13052/jwe1540-9589.20313

Keywords:

Rice disease recognition, deep neural network, ResNet, DenseNet, image recognition, image augmentation

Abstract

Rice is the most important grain in Thailand for both consuming and exporting. One of the critical problems in rice cultivation is rice diseases, which affects directly to the yield. Early disease recognition is handled by a human, which is difficult to achieve high accuracy and the performance depends on the farmer’s experience. To overcome this problem, we did three folds of contributions. First, an infield rice diseases image dataset, named K5RD, was created. Second, a number of additional techniques to enhance the classification scores including data augmentations and learning rate adjustment strategies were carefully surveyed. Third, a set of selective deep learning models including ResNets and DenseNets were applied to classify such rice diseases. The experimental results reveal that the proposed framework can achieve high performance, which its F1 score is higher than 98% on average, and has the potential to be implemented as a practical system to provide to Thai farmers in the future.

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Author Biographies

S. Mathulaprangsan, Department of Computer Engineering, Faculty of Engineering at Kamphaeng Sean, Kasetsart University, Thailand

S. Mathulaprangsan received the B.S. and M.S. degrees in Computer Engineering from King Mongkut’s University of Technology Thonburi, Thailand in 1999 and 2003, respectively. In 2019, he received Ph.D. degree in applied computer science and information engineering from National Central University, Taiwan. Currently, he is a lecturer in the Department of Computer Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Thailand. His research interests include image processing, deep learning, AI in agriculture, and satellite image processing.

S. Patarapuwadol, Department of Plant Pathology, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University, Thailand

S. Patarapuwadol is currently an Assistant Professor in the Department of Plant Pathology, Faculty of Agriculture at Kamphaeng Saen, Kasetsart University, Thailand. She graduated with first-class honours at Kasetsart University (B.Sc.Agr., 1995) and Ph.D. (Agriculture) in 2008 from The University of Sydney, Australia. Her research focused on plant microbe interactions and molecular detection and identification of phytopathogenic bacteria. Sujin, a Rockefeller Ph.D. fellowship recipient, has been involved in rice research since 2001. In 2018, her research group began to address rice diseases diagnosis using image analysis and artificial intelligence.

K. Lanthong, Department of Computer Engineering, Faculty of Engineering at Kamphaeng Sean, Kasetsart University, Thailand

K. Lanthong received the B.E. degree in computer engineering from Kasetsart University, Thailand in 2013. Currently he is a research assistant in the Department of Computer Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Thailand. His research interests include computer vision, satellite image processing, and deep learning.

D. Jetpipattanapong, Department of Computer Engineering, Faculty of Engineering at Kamphaeng Sean, Kasetsart University, Thailand

D. Jetpipattanapong received Ph.D. degree from Sirindhorn International Institute of Technology, Thammasat University, Thailand in 2017. She is currently a lecturer in Department of Computer Engineering, Faculty of Engineering at Kampheang Saen, Kasetsart University, Thailand. Her research interests are machine learning and numerical computation.

S. Sateanpattanakul, Department of Computer Engineering, Faculty of Engineering at Kamphaeng Sean, Kasetsart University, Thailand

S. Sateanpattanakul received D.Eng. degree from King Mongkut’s Institute of Technology Ladkrabang, Thailand in 2012. He is currently a lecturer in Department of Computer Engineering, Faculty of Engineering at Kampheang Saen, Kasetsart University, Thailand. His research interests are software engineering, Java technology, compiler construction, computer programming language, and artificial intelligence.

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Published

2021-06-10

How to Cite

Mathulaprangsan, S., Patarapuwadol, S., Lanthong, K., Jetpipattanapong, D., & Sateanpattanakul, S. (2021). Rice Disease Recognition Using Effective Deep Neural Networks. Journal of Web Engineering, 20(3), 853–878. https://doi.org/10.13052/jwe1540-9589.20313

Issue

2021: Vol 20 Iss 3

Section

Communication, Multimedia and Learning Technology through Future Web Engineering