Plant Diseases Classification Using Pre-trained and Transfer Learning Models: A Study on Rice Leaves

Document Type : Research Article

Authors

Department of Computer Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

Ocular detection of pests by phytosanitary specialists, as a very imperative and challenging task, appears to be time-consuming, costly, and associated with human error in today’s farming processes. In modern agriculture, diagnostic softwares by artificial intelligence are advised to be used by farmers themselves with little time and cost and with more accuracy. In this paper, two different datasets of rice leaf disease have been used with two transfer learning methods for diagnosing rice leaf disease. The first method uses a CNN-based output of a pre-trained model with an appropriate classifier. In the second method, freezing bottom layers, fine-tuning weights of the last layers of the pre-trained network, and adding an appropriate classifier to the model are proposed. For this purpose, seven CNN models have been designed and evaluated. Next, the weights of the best model among these seven proposed models is used to train a second dataset of rice leaves with more disease classes and fewer images. Using these weights, an accuracy of over 96% is reached which is higher than other comparing methods. Furthermore, Grad-CAM, heat map, and ROC diagram are used to observe the diagnostic areas of the best model.

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Main Subjects

References

  1. K. Shrivastava, M. K. Pradhan and M. P. Thakur, "Application of Pre-Trained Deep Convolutional Neural Networks for Rice Plant Disease Classification," 2021 Int. Conf. on Artificial Intelligence and Smart Systems (ICAIS), pp. 1023-1030.
  2. Ghosal and K. Sarkar, "Rice Leaf Diseases Classification Using CNN With Transfer Learning," 2020 IEEE Calcutta Conference (CALCON), pp. 230-236.
  3. Andrianto, Suhardi, A. Faizal and F. Armandika, "Smartphone Application for Deep Learning-Based Rice Plant Disease Detection," 2020 Int. Conf. on Information Technology Systems and Innovation (ICITSI), pp. 387-392.
  4. Mekha and N. Teeyasuksaet, "Image Classification of Rice Leaf Diseases Using Random Forest Algorithm," 2021 Joint Int. Conf. on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, pp. 165-169.
  5. E. Pothen and M. L. Pai, "Detection of Rice Leaf Diseases Using Image Processing," 2020 Fourth Int. Conf. on Computing Methodologies and Communication (ICCMC), pp. 424-430.
  6. Kodama and Y. Hata, "Development of Classification System of Rice Disease Using Artificial Intelligence," 2018 IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC), pp. 3699-3702.
  7. Ramesh and D. Vydeki, “Application of machine learning in detection of blast disease in South Indian rice crops,” J. Phytol., vol. 11, pp. 31–37, 2019.
  8. Phadikar and J. Goswami, "Vegetation indices based segmentation for automatic classification of brown spot and blast diseases of rice," 2016 3rd Int. Conf. on Recent Advances in Information Technology (RAIT), 2016, pp. 284-289.
  9. Islam, M. Sah, S. Baral and R. Roy Choudhury, "A Faster Technique on Rice Disease Detectionusing Image Processing of Affected Area in Agro-Field," 2018 Second Int. Conf. on Inventive Communication and Computational Technologies (ICICCT), pp. 62-66.
  10. Hasan Matin, A. Khatun, Md. G. Moazzam and M. Shorif Uddin, "An Efficient Disease Detection Technique of Rice Leaf Using AlexNet," 2020 8th J. of Computer and Communications, pp. 49-57.
  11. Verma, C. Taluja and A. K. Saxena, "Vision Based Detection and Classification of Disease on Rice Crops Using Convolutional Neural Network," 2019 Int. Conf. on Cutting-edge Technologies in Engineering (ICon-CuTE), pp. 1-4.
  12. Hasan, S. Mahbub, S. Alom and A. Nasim, "Rice Disease Identification and Classification by Integrating Support Vector Machine With Deep Convolutional Neural Network," 2019 1st Int. Conf. on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1-6.
  13. A. Burhan, S. Minhas, A. Tariq and M. Nabeel Hassan, "Comparative Study Of Deep Learning Algorithms For Disease And Pest Detection In Rice Crops," 2020 12th Int. Conf. on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1-5.
  14. Sharma, S. Das, M. K. Gourisaria, S. S. Rautaray, and M. Pandey, “A Model for Prediction of Paddy Crop Disease Using CNN,” in Progress in Computing, Analytics and Networking, 2020, pp. 533–543.
  15. J. Bharathi, “Paddy Plant Disease Identification and Classification of Image Using AlexNet Model,” Int. J. Analytical and Experimental Modal Analysis, vol. XII, no. III, pp. 1094–1098, 2020.
  16. Chen, D. Zhang, Y. A. Nanehkaran, D. Li, “Detection of rice plant diseases based on deep transfer learning,” J. of the Science of Food and Agriculture, vol. 100, no. 7, pp. 3246– 3256, 2020.
  17. R. Rahman et al., “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosystems Engineering, vol. 194, pp. 112–120, 2020.
  18. Wadhawan, M. Garg and A. K. Sahani, "Rice Plant Leaf Disease Detection and Severity Estimation," 2020 IEEE 15th Int. Conf. on Industrial and Information Systems (ICIIS), 2020, pp. 455-459.
  19. Sharma, V. Kukreja and V. Kadyan, "Hispa Rice Disease Classification using Convolutional Neural Network," 2021 3rd Int. Conf. on Signal Processing and Communication (ICPSC), 2021, pp. 377-381.
  20. M. S. Sazzad, A. Anwar, M. Hasan and M. I. Hossain, "An Image Processing Framework To Identify Rice Blast," 2020 Int. Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2020, pp. 1-5.
  21. S. Chawathe, "Rice Disease Detection by Image Analysis," 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 2020, pp. 0524-0530.
  22. S. Ghyar and G. K. Birajdar, "Computer vision based approach to detect rice leaf diseases using texture and color descriptors," 2017 Int. Conf. on Inventive Computing and Informatics (ICICI), pp. 1074-1078.
  23. Shrivastava, M.S. Pillai, B. Baranidharan, “Rice Disease Classification using Deep Convolutional Neural Network”, February 2020, Int. J. of Innovative Technology and Exploring Engineering (IJITEE), 9, 2278-3075.
  24. Sethy, N. Barpanda, A. Rath and S. Behera, "Deep feature based rice leaf disease identification using support vector machine," 2020, Computers and Electronics in Agriculture, 175, 105527.
  25. Peng, Y. Wang, P. Jiang , R. Zhang and H. Chen, "c Backgrounds Using a Res-Attention Mechanism," Applied Sciences, vol. 19, 2023.
  26. Zeng , G. Gong, G. Zhou and a. C. Hu, "An Accurate Classification of Rice Diseases Based on ICAI-V4," Plants, vol. 28, 2023.
  27. https://keras.io/api/applications/
  28. R., Selvaraju, et al. "Grad-cam: Visual explanations from deep networks via gradient-based localization," Proceedings of the IEEE international conference on computer vision, 2017.

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