A Novel Design of Rice Leaf Disease Detection Model Using Machine Learning

Authors

  • Taha Hussain M.Tech Student, RIMT University. Mandi Gobindgarh, Punjab, India Author
  • Jasmeen Gill Associate Professor, Department of Computer Science & Engineering, RIMT University, Mandi Gobindgarh Punjab, India Author
  • Ravinder Pal Singh Technical Head, RIMT-DRI RIMT University Mandi Gobindgarh, Punjab, India Author

Keywords:

Rice, Photography, CNN, detection, Quality, Image data

Abstract

Rice has a high nutritional value since it includes several vital  elements. It is one of the most widely consumed foods.  However, due to the great diversity of rice, judging its quality  is difficult. The use of photography and machine learning in  this work resulted in a unique method for determining the  quality of rice without causing any damage or loss. First, a  DSLR camera was used to capture pictures of the Rice leaf  samples. The data was separated into healthy. and sick groups  and sorted. The CNN was then used to discriminate between  different types of rice leaf diseases. Various parameters have  been used to train several models. Here we use graphical user  interface(GUI) as an interface software. Five classes of leaves  ie. Healthy, Bacterial leaf blight, Brown spot, leaf smut, and  leaf blast were taken into account, and all the leaves where  classified into one of these classes. Finally, the models were  evaluated based on the outcomes of the experiments. The  finest performance was noticed by CNN. CNN's  classification and average cost time accuracy were 95 percent  and 0.01 seconds, respectively. Overall, the results  demonstrate that picture data generated by the system may be  utilized to assess rice quality quickly, accurately, and safely. 

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References

Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. pp. 3642–3649. IEEE (2012)

Cires¸an, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Medical Image Computing and Computer-Assisted Intervention–

MICCAI 2013, pp. 411–418. Springer (2013)

Ciresan, D.C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence. vol. 22, p. 1237 (2011) Introduction to Convolutional Neural Networks 11

Cires¸an, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: Document Analysis and Recognition (ICDAR), 2011 International Conference on. pp. 1135–1139. IEEE (2011)

Egmont-Petersen, M., de Ridder, D., Handels, H.: Image processing with neural net- worksa review. Pattern recognition 35(10), 2279–2301 (2002)

Farabet, C., Martini, B., Akselrod, P., Talay, S., LeCun, Y., Culurciello, E.: Hardware accelerated convolutional neural networks for synthetic vision systems. In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on. pp. 257–260. IEEE (2010)

Hinton, G.: A practical guide to training restricted boltzmann machines. Momentum 9(1), 926 (2010) 8) Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Im- proving neural networks by preventing co-adaptation of feature detectors. arXiv 9) preprint arXiv:1207.0580 (2012)

Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on 35(1), 221–231 (2013)

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large- scale video classification with convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. pp. 1725–1732. IEEE (2014)

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convo- lutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105 (2012)

LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural computation 1(4), 541–551 (1989)

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)

Nebauer, C.: Evaluation of convolutional neural networks for visual recognition.

Neural Networks, IEEE Transactions on 9(4), 685–696 (1998)

Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: null. p. 958. IEEE (2003)

Srivastava, N.: Improving neural networks with dropout. Ph.D. thesis, University of Toronto (2013)

Szarvas, M., Yoshizawa, A., Yamamoto, M., Ogata, J.: Pedestrian detection with convolutional neural networks. In: Intelligent Vehicles Symposium, 2005. Proceedings. IEEE. pp. 224–229. IEEE (2005)

Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems. pp. 2553–2561 (2013)

Tivive, F.H.C., Bouzerdoum, A.: A new class of convolutional neural networks (siconnets) and their application of face detection. In: Neural Networks, 2003

V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proc. 27th International Conference on Machine Learning, 2010.

N. Pinto, D.D. Cox, and J.J. DiCarlo. Why is real-world visual object recognition hard? PLoS computational biology, 4(1):e27, 2008.

N. Pinto, D. Doukhan, J.J. DiCarlo, and D.D. Cox. A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS computational biology, 5(11):e1000579, 2009.

B.C. Russell, A. Torralba, K.P. Murphy, and W.T. Freeman. Labelme: a database and web-based tool for image annotation. International journal of computer vision, 77(1):157–173, 2008.

J. Sánchez and F. Perronnin. High-dimensional signature compression for large-scale image classification. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1665–1672. IEEE, 2011.

Published

2021-11-30

Issue

Vol. 8 No. 6 (2021): International Journal of Innovative Research in Engineering & Management (Nov) 2021

Section

Articles

How to Cite

A Novel Design of Rice Leaf Disease Detection Model Using Machine Learning . (2021). International Journal of Innovative Research in Engineering & Management, 8(6), 94–102. Retrieved from https://acspublisher.com/journals/index.php/ijirem/article/view/11528