Web enabled paddy disease detection using Compressed Sensing
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 S...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2019-08-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/10.3934/mbe.2019387?viewType=HTML |
| _version_ | 1818179953289592832 |
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| author | T. Gayathri Devi A. Srinivasan S. Sudha D. Narasimhan |
| author_facet | T. Gayathri Devi A. Srinivasan S. Sudha D. Narasimhan |
| author_sort | T. Gayathri Devi |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-12-11T21:12:04Z |
| format | Article |
| id | doaj.art-42e0bf1ea4b345d39f0d1654b4b01f14 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-11T21:12:04Z |
| publishDate | 2019-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-42e0bf1ea4b345d39f0d1654b4b01f142022-12-22T00:50:41ZengAIMS PressMathematical Biosciences and Engineering1551-00182019-08-011667719773310.3934/mbe.2019387Web enabled paddy disease detection using Compressed SensingT. Gayathri Devi 0A. Srinivasan1S. Sudha 2D. Narasimhan31. Department of ECE, Srinivasan Ramanujan Centre, SASTRA Deemed University, Kumbakonam 612001, India1. Department of ECE, Srinivasan Ramanujan Centre, SASTRA Deemed University, Kumbakonam 612001, India1. Department of ECE, Srinivasan Ramanujan Centre, SASTRA Deemed University, Kumbakonam 612001, India2. Department of Mathematics, Srinivasan Ramanujan Centre, SASTRA Deemed University, Kumbakonam 612001, IndiaIn 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%.https://www.aimspress.com/article/10.3934/mbe.2019387?viewType=HTMLk-means clusteringcompressed sensingstatistical gray level co-occurrence matrixsupport vector machinearduino |
| spellingShingle | T. Gayathri Devi A. Srinivasan S. Sudha D. Narasimhan Web enabled paddy disease detection using Compressed Sensing Mathematical Biosciences and Engineering k-means clustering compressed sensing statistical gray level co-occurrence matrix support vector machine arduino |
| title | Web enabled paddy disease detection using Compressed Sensing |
| title_full | Web enabled paddy disease detection using Compressed Sensing |
| title_fullStr | Web enabled paddy disease detection using Compressed Sensing |
| title_full_unstemmed | Web enabled paddy disease detection using Compressed Sensing |
| title_short | Web enabled paddy disease detection using Compressed Sensing |
| title_sort | web enabled paddy disease detection using compressed sensing |
| topic | k-means clustering compressed sensing statistical gray level co-occurrence matrix support vector machine arduino |
| url | https://www.aimspress.com/article/10.3934/mbe.2019387?viewType=HTML |
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