Editorial: Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2012)

Ramaswamy, Lakshmish; Carminati, Barbara; Joshi, James; Pu, Calton

doi:10.1007/s11036-014-0532-9

Cited by 2 publications

(2 citation statements)

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“…To achieve the relative position coordinates dx(A), dy(A) and dw(A) of the bounding box in the case of Ground Truth, a 36-dimensional convolution mechanism is conducted by the accumulative kernel. The original image is mapped with every point on the feature map in which “anchor point” is described by each pixel ( Ramaswamy et al, 2014 ). Each anchor point is utilized to be positioned of multiple anchors of different sizes.…”

Section: Introductionmentioning

confidence: 99%

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

Bari

Islam

Rashid

et al. 2021

PeerJ Computer Science

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The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

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Section: Introductionmentioning

confidence: 99%

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

Bari

Islam

Rashid

et al. 2021

PeerJ Computer Science

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“…The accumulative kernel performs a 36-dimensional convolution operation to obtain the relative position coordinates of the bounding box for Ground Truth, and obtain the relative position coordinates dx(A), dy(A) and dw(A) of the bounding box for Ground Truth. The setting method of the nine bounding boxes corresponding to each feature map element is: The point of the convolved feature map is mapped to the position of the original picture, and each point on the feature map is mapped onto the original picture (each pixel is set as an ''anchor point'') [32]. In this way, several anchors of different sizes (that is, the surrounding bounding boxes) are placed on each anchor point.…”

Section: ) Introduction To Faster R-cnnmentioning

confidence: 99%

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

et al. 2019

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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 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.

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Editorial: Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2012)

Cited by 2 publications

References 0 publications

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

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

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