Web enabled paddy disease detection using Compressed Sensing

T. Gayathri Devi; A. Srinivasan; S. Sudha; D. Narasimhan; T. Gayathri Devi; A. Srinivasan; S. Sudha; D. Narasimhan

doi:10.3934/mbe.2019387

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 7719-7733. doi: 10.3934/mbe.2019387

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Research article Special Issues

Web enabled paddy disease detection using Compressed Sensing

1.
Department of ECE, Srinivasan Ramanujan Centre, SASTRA Deemed University, Kumbakonam 612001, India
2.
Department of Mathematics, Srinivasan Ramanujan Centre, SASTRA Deemed University, Kumbakonam 612001, India

Received: 04 May 2019 Accepted: 28 July 2019 Published: 23 August 2019

In agricultural industry, paddy diseases play a vital role to cause economic losses. Hence, the detection of diseases of paddy plants and give suggestions to the peasants is beneficial to increase the yield quantity of rice. In this paper, a novel web-based paddy disease detection using Compressed Sensing is proposed to detect and classify paddy diseases. First, the diseased leaf is pre-processed using contrast enhancement, and then LAB color space is applied. The segmentation is done using K-Means clustering. The storage complexity is reduced using the Compressed Sensing technique. The segmented leaf images are compressed and then uploaded to the cloud. In the transmitter section, the Compressed Sensing recovery algorithm is used to reconstruct the segmented image. Then Statistical Gray Level Co-occurrence Matrix (GLCM) method is used to extract the features from the segmented image. Support Vector Machine classifier is used to classify the diseases. The performance of the proposed method is compared with other existing techniques. The proposed system is also experimentally tested with Arduino board. The proposed system achieves the disease recognition rate of 98.38%.

Keywords:

K-Means clustering,
Compressed Sensing,
Statistical Gray Level Co-occurrence Matrix,
Support Vector Machine,
Arduino

Citation: T. Gayathri Devi, A. Srinivasan, S. Sudha, D. Narasimhan. Web enabled paddy disease detection using Compressed Sensing[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 7719-7733. doi: 10.3934/mbe.2019387

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Abstract

References

[1]	S. A. Nanduini, R. Hemalatha, S. Radha, et al., Web enabled Plant Disease detection for Agricultural Application using WMSN, Wireless Pers. Commun., 102 (2018), 725–740.
[2]	P. Sethi and S. R. Sarangi, Internet of Things: Architectures, protocols and applications, J. Electr. Comput. Eng., 2017 (2017), 1–25.
[3]	I. Mat, M. R. M. Kassim, A. N. Harun, et al., IoT in Precision Agriculture applications using Wireless Moisture Sensor Network, IEEE conference on open systems (ICOS), 2016. Available from: https://ieeexplore.ieee.org/abstract/document/7881983.
[4]	V. Singh and A. K. Misra, Detection of plant leaf diseases using image segmentation and soft computing techniques, Inf. Process. Agric., 4 (2017), 41–49.
[5]	S. Mutalib, M. H. Abdullah, S. Abdul-Rahman, et al., A brief study on paddy applications with image processing and proposed architecture, IEEE Conference on Systems, Process and Control (ICSPC), 2016, 124–129. Available from: https://ieeexplore.ieee.org/abstract/document/7920716.
[6]	A. A. Joshi and B. D. Jadhav, Monitoring and controlling rice diseases using Image processing techniques, International Conference on Computing, Analytics and Security Trends (CAST), 2016, 471–476. Available from: https://ieeexplore.ieee.org/abstract/document/7915015.
[7]	A. Khattab, A. Abdelgawad and K. Yelmarthi, Design and implementation of a cloud-based IoT scheme for precision agriculture, 28th International Conference on Microelectronics (ICM), 2016, 201–204. Available from: https://ieeexplore.ieee.org/abstract/document/7847850.
[8]	P. B. Padol and A. A. Yadav, SVM Classifier Based Grape Leaf Disease Detection, Conference on Advances in Signal Processing (CAPS), 2016, 9–11. Available from: https://ieeexplore.ieee.org/abstract/document/7746160.
[9]	Z. N. Reza, F. Nuzhat, N. A. Mahsa, et al., Detecting jute plant disease using image processing and machine learning, 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2016, 22–26. Available from: https://ieeexplore.ieee.org/abstract/document/7873147.
[10]	S. D. Khirade and A. B. Patil, Plant disease detection using image processing, International conference on computing communication control and automation (ICCUBEA), 2015, 768–771. Available from: https://ieeexplore.ieee.org/abstract/document/7155951.
[11]	J. D. Pujari, R. Yakkundimath and A. S. Byadgi, Image Processing Based Detection of Fungal Diseases in Plants, Proc. Comput. Sci., 46 (2015), 1802–1808.
[12]	D. A. Devi and K. Muthukannan, Analysis of segmentation scheme for diseased rice leaves, IEEE International Conference on Advanced Communications, Control and Computing Technologies, 2014, 1374–1378. Available from: https://ieeexplore.ieee.org/abstract/document/7019325.
[13]	A. Tuli, N. Hasteer, M. Sharma, et al., Framework to leverage cloud for the modernization of the Indian agriculture system, IEEE International Conference on Electro/Information Technology, 2014, 109–115. Available from: https://ieeexplore.ieee.org/abstract/document/6871748.
[14]	J. G. A. Barbedo, Digital image processing techniques for detecting, quantifying and classifying plant disease, Springer Plus, 2 (2013), 660–672.
[15]	G. Bhadane, S. Sharma and V. B. Nerkar, Early Pest Identification in Agricultural Crops using Image Processing Techniques, Int. J. Electr. Electron. Comput. Eng., 2 (2013), 77–82.
[16]	S. Bashir and N. Sharma, Remote Area Plant Disease Detection Using Image Processing, IOSR J. Electron. Commun. Eng., 2 (2012), 2278–2834.
[17]	S. Sankarana, A. Mishra, R. Ehsani, et al., A review of advanced techniques for detecting plant diseases, Comput. Electron. Agric., 72 (2010), 1–13.
[18]	G. Anthonys and N. Wickramarachchi, An image recognition system for crop disease identification of paddy fields in Sri Lanka, International Conference on Industrial and Information Systems, 2009, 403–407. Available from: https://ieeexplore.ieee.org/abstract/document/5429828.

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