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Convolutional Neural Network, VGG16, ResNet50, DenseNet121, Local threshold, Rice leaf disease
Timely detection of rice diseases can help farmers to take necessary action and thus reducing the yield loss substantially. Automatic recognition of rice diseases from the rice leaf images using computer vision and machine learning can be beneficial over the manual method of disease recognition through visual inspection. During the recent years, deep learning, a very popular and efficient machine learning algorithm, has shown great promise in image classification task. In this paper, a segmentation-based method using deep neural network for classifying rice diseases from leaf images has been proposed. Disease-affected regions of the rice leaves have been segmented using local segmentation method and the Convolutional Neural Network (CNN) has been trained with those images. Proposed method has been applied on three different datasets including the one created by us which consists of the rice leaf images collected from Bangladesh Rice Research Institute (BRRI). Three state-of-the-art CNN architectures VGG, ResNet and DenseNet, used in the proposed method, have been trained with these three datasets for classifying the diseases. Classification performance of the proposed method using the said three CNN architectures for the three datasets have been analyzed and compared. These results show that this model is quite promising in classifying rice leaf diseases. Outcome of this research is an enhancement in the performance of rice disease classification which is quite significant for the viability of this work to be transformed into a real-time application for the farmers.
Anam Islam, Redoun Islam, S. M. Rafizul Haque, S.M. Mohidul Islam, Mohammad Ashik Iqbal Khan, "Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN", International Journal of Intelligent Systems and Applications(IJISA), Vol.13, No.5, pp.35-45, 2021. DOI: 10.5815/ijisa.2021.05.04
Factsheet: Disease pests and management of paddy (Bacterial Leaf Blight). http://knowledgebank-brri.org/Rice_Production_Training_Manual/Day_3/Module_10/Factsheet2%20-%20Patapora%20rog.pdf Accessed Date: May 23, 2021.
Factsheet: Disease pests and management of paddy (Rice Blast). http://knowledgebank-brri.org/Rice_Production_Training_Manual/Day_3/Module_10/Factsheet4%20-%20Blast%20rog.pdf Accessed Date: May 23, 2021.
Factsheet: Disease pests and management of paddy (Sheath Blight). http://knowledgebank-brri.org/Rice_Production_Training_Manual/Day_3/Module_10/Factsheet3%20-%20Kholpora%20rog.pdf Accessed Date: May 23, 2021.
The global staple. http://ricepedia.org/rice-as-food/the-global-staple-rice-consumers. Accessed Date: March 24, 2020.
How to manage pests and diseases. http://www.knowledgebank.irri.org/step-by-step-production/growth/pests-and-diseases. Accessed Date: March 24, 2020.
Rice Leaf Diseases Data Set. https://archive.ics.uci.edu/ml/datasets/Rice Leaf Diseases. Accessed Date: March 25, 2020.
M. Al-Amin, D. Z. Karim, and T. A. Bushra, “Prediction of rice disease from leaves using deep convolution neural network towards a digital agricultural system,” 22nd International Conference on Computer and Information Technology (ICCIT), pp. 1–5, 2019.
aldrin233 RiceDiseases-DataSet. https://github.com/aldrin233/RiceDiseases-DataSet. Accessed Date: March 25, 2020.
Megha P Arakeri, Malavika Arun, Padmini R K,"Analysis of Late Blight Disease in Tomato Leaf Using Image Processing Techniques", International Journal of Engineering and Manufacturing (IJEM), Vol.5, No.4, pp.12-22, 2015.DOI: 10.5815/ijem.2015.04.02
A. Asfarian, Y. Herdiyeni, A. Rauf, and K. H. Mutaqin, “Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum,” International Conference on Computer, Control, Informatics and Its Applications (IC3INA), pp. 77-81, November, 2013.
S. Ghosal and K. Sarkar, “Rice leaf diseases classification using cnn with transfer learning,” IEEE Calcutta Conference (CALCON), pp. 230–236, 2020.
L. P. Gianessi, “Importance of pesticides for growing rice in South and South East Asia,” International Pesticide Benefit Case Study, p. 108, 2014.
W.-j. Liang, H. Zhang, G.-f. Zhang, and H.-x. Cao, “Rice blast disease recognition using a deep convolutional neural network,” Scientific reports, Vol. 9, Is. 1, pp.1–10, 2019.
B. Liu, Y. Zhang, D. He, and Y. Li, “Identification of apple leaf diseases based on deep convolutional neural networks,” Symmetry, Vol. 10, Is. 1, p. 11, 2018.
Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, “Identification of rice diseases using deep convolutional neural networks,” Neurocomputing, 267, pp. 378-384, 2017.
D. Mohapatra, J. Tripathy, and T. K. Patra, “Rice disease detection and monitoring using cnn and naive bayes classification,” Soft Computing Techniques and Applications, pp. 11–29. Springer, Singapore.
F. T. Pinki, N. Khatun, and S.M. M. Islam, “Content based paddy leaf disease recognition and remedy prediction using support vector machine,” 20th International Conference of Computer and Information Technology (ICCIT), pp. 1-5, December, 2017.
H. B. Prajapati, J. P. Shah, and V. K. Dabhi, “Detection and classification of rice plant diseases,” Intelligent Decision Technologies, Vol. 11, Is. 3, pp. 357-373, 2017.
D. Singh, Asir Antony Gnana, E. Jebamalar Leavline, A. K. Abirami, and M. Dhivya. "Plant disease detection system using bag of visual words." IJ Inf Technol Comput Sci, Vol. 8, no. 9, pp. 57-63, 2018.
C. R. Rahman, P. S. Arko, M. E. Ali, M. A. I. Khan, S. H. Apon, F. Nowrin, et al. “Identification and recognition of rice diseases and pests using convolutional neural networks,”. Biosystems Engineering, Vol. 194, pp. 112–120, 2020.
P. Sharma, Y. P. S. Berwal, and W. Ghai, “Performance analysis of deep learning cnn models for disease detection in plants using image segmentation. Information Processing in Agriculture,” Vol. 7, Is. 4, pp. 566–574, 2020.
Jagadeesh D. Pujari, Rajesh.Yakkundimath, Abdulmunaf. Syedhusain. Byadgi,"Automatic Fungal Disease Detection based on Wavelet Feature Extraction and PCA Analysis in Commercial Crops", IJIGSP, vol.6, no.1, pp.24-31, 2014.DOI: 10.5815/ijigsp.2014.01.04
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep neural networks-based recognition of plant diseases by leaf image classification”. Computational intelligence and neuroscience, May, 2016.
Q. Yao, Z. Guan, Y. Zhou, J. Tang, Y. Hu, and B. Yang, “Application of support vector machine for detecting rice diseases using shape and color texture features,” International conference on engineering computation, pp. 79-83, May, 2009.
G. Zhou, W. Zhang, A. Chen, M. He, and X. Ma, “Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion,” IEEE Access, vol. 7, pp. 143190-143206, 2019.