Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion
In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimension...
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doaj-aadd03e6bb12416895b5217abc5694d02021-04-05T17:23:23ZengIEEEIEEE Access2169-35362019-01-01714319014320610.1109/ACCESS.2019.29434548847623Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN FusionGuoxiong Zhou0https://orcid.org/0000-0002-8295-3862Wenzhuo Zhang1Aibin Chen2Mingfang He3Xueshuo Ma4College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, ChinaCollege of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, ChinaCollege of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, ChinaCollege of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, ChinaCollege of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, ChinaIn this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification.https://ieeexplore.ieee.org/document/8847623/Chaos theoryfaster R-CNNfirefly algorithmOtsu threshold segmentationK-means clustering algorithmrice disease detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guoxiong Zhou Wenzhuo Zhang Aibin Chen Mingfang He Xueshuo Ma |
spellingShingle |
Guoxiong Zhou Wenzhuo Zhang Aibin Chen Mingfang He Xueshuo Ma Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion IEEE Access Chaos theory faster R-CNN firefly algorithm Otsu threshold segmentation K-means clustering algorithm rice disease detection |
author_facet |
Guoxiong Zhou Wenzhuo Zhang Aibin Chen Mingfang He Xueshuo Ma |
author_sort |
Guoxiong Zhou |
title |
Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion |
title_short |
Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion |
title_full |
Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion |
title_fullStr |
Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion |
title_full_unstemmed |
Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion |
title_sort |
rapid detection of rice disease based on fcm-km and faster r-cnn fusion |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
In this paper, a method for detecting rapid rice disease based on FCM-KM and Faster R-CNN fusion is proposed to address various problems with the rice disease images, such as noise, blurred image edge, large background interference and low detection accuracy. Firstly, the method uses a two-dimensional filtering mask combined with a weighted multilevel median filter (2DFM-AMMF) for noise reduction, and uses a faster two-dimensional Otsu threshold segmentation algorithm (Faster 2D-Otsu) to reduce the interference of complex background with the detection of target blade in the image. Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K-Means clustering algorithm (FCM-KM) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem. Combined with the R-CNN algorithm for the identification of rice diseases, FCM-KM analysis is conducted to determine the different sizes of the Faster R-CNN target frame. As revealed by the application results of 3010 images, the accuracy and time required for detection of rice blast, bacterial blight and blight were 96.71%/0.65s, 97.53%/0.82s and 98.26%/0.53s, respectively, indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R-CNN algorithm, while reducing the time required for identification. |
topic |
Chaos theory faster R-CNN firefly algorithm Otsu threshold segmentation K-means clustering algorithm rice disease detection |
url |
https://ieeexplore.ieee.org/document/8847623/ |
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