Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion

Zhou, Guoxiong; Zhang, Wenzhuo; Chen, Aibin; He, Mingyi; Ma, Xueshuo

doi:10.1109/access.2019.2943454

Cited by 194 publications

(84 citation statements)

References 39 publications

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“…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%

Plant diseases and pests detection based on deep learning: a review

2021

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Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

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Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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“…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%

Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Sibiya

Sumbwanyambe

2021

Pathogens

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Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.

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“…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%

Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

Zhang

Rao

Chao

et al. 2021

International Journal of Distributed Sensor Networks

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Due to the complex environments in real fields, it is challenging to conduct identification modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the system of agricultural Internet of things. To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. One two-stage segmentation method was presented to acquire the lesion images by extracting the disease spots from cucumber leaves. Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. Finally, to improve the identification accuracy of cucumber leaf diseases, we proposed dilated and inception convolutional neural network that was trained using the generated training samples. Experimental results showed that the proposed approach achieved the average identification accuracy of 96.11% and 90.67% when implemented on the data sets of lesion and raw field diseased leaf images with three different diseases of anthracnose, downy mildew, and powdery mildew, significantly outperforming those existing counterparts, indicating that it offered good potential of serving field application of agricultural Internet of things.

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Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion

Cited by 194 publications

References 39 publications

Plant diseases and pests detection based on deep learning: a review

Plant diseases and pests detection based on deep learning: a review

Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning

Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things

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