2019
DOI: 10.1109/access.2019.2943454
View full text |Buy / Rent full text
|
Sign up to set email alerts
|

Abstract: 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 twodimensional 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… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
36
0
2

Year Published

2020
2020
2021
2021

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 82 publications
(38 citation statements)
references
References 39 publications
(34 reference statements)
0
36
0
2
Order By: Relevance
“…The results show that the overall correct classification rate of this method is 95.48%. Zhou et al [ 61 ] presented a fast rice disease detection method based on the fusion of FCM-KM and Faster R-CNN. The application results of 3010 images showed that: the detection accuracy and time of rice blast, bacterial blight, and sheath blight are 96.71%/0.65 s, 97.53%/0.82 s and 98.26%/0.53 s respectively.…”
Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning
confidence: 99%
“…The results show that the overall correct classification rate of this method is 95.48%. Zhou et al [ 61 ] presented a fast rice disease detection method based on the fusion of FCM-KM and Faster R-CNN. The application results of 3010 images showed that: the detection accuracy and time of rice blast, bacterial blight, and sheath blight are 96.71%/0.65 s, 97.53%/0.82 s and 98.26%/0.53 s respectively.…”
Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning
confidence: 99%
“…In the literature, different methods of plant lea disease severity prediction had been used by other researchers [ 32 , 33 , 34 ]. Symptoms of plant diseases may include a detectable change in colour, shape, or function of the plant as it responds to the pathogen [ 35 ].…”
Section: Methodsmentioning
confidence: 99%
“…This effectively enhances feature propagation, promotes feature reuse, and improves network performance. 25 Aiming at various problems with the rice disease images, such as noise, blurred image edge, large background interference, and low detection accuracy, G Zhou et al 26 proposed a method for detecting rapid rice disease based on fuzzy c-means and k-means (FCM-KM) FCM-KM and Faster R-CNN. However, it is well-known that the deep learning-based algorithms require large annotated data sets on a per-pixel level in order to successfully train the large number of free parameters of the deep network.…”
Section: Related Workmentioning
confidence: 99%