Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection

Cristin, R.; Kumar, B. Santhosh; Priya, C.; Karthick, K.

doi:10.1007/s10462-020-09813-w

Cited by 71 publications

(22 citation statements)

References 43 publications

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“…In the last few years, lots of researchers have applied the DBN in many application fields such as image recognition, speech recognition, face recognition, and plant leaf classification ( Cheng et al, 2018 ; Goudarzi et al, 2018 ; Liu et al, 2018 ; Cristin et al, 2020 ; Vedantham and Reddy, 2020 ; Veisi and Mani, 2020 ; Chen and Pan, 2021 ). It is a multi-layer network structure, which provides a basic model for feature extraction and subsequent image recognition of original images.…”

Section: Related Workmentioning

confidence: 99%

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

Lü

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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Rice blast, rice sheath blight, and rice brown spot have become the most popular diseases in the cold areas of northern China. In order to further improve the accuracy and efficiency of rice disease diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this study. First, we constructed a training and testing data set including 1,500 images of rice blast, 1,500 images of rice sheath blight, and 1,500 images of rice brown spot, and 1,100 healthy images were collected from the rice experimental field. Second, the deep belief network (DBN) model is designed to include 15 hidden restricted Boltzmann machine layers and a support vector machine (SVM) optimized with switching particle swarm (SPSO). It is noted that the developed DBN and SPSO-SVM can simultaneously learn three proposed features including color, texture, and shape to recognize the disease type from the region of interest obtained by preprocessing the disease images. The proposed model leads to a hit rate of 91.37%, accuracy of 94.03%, and a false measurement rate of 8.63%, with the 10-fold cross-validation strategy. The value of the area under the receiver operating characteristic curve (AUC) is 0.97, whose accuracy is much higher than that of the conventional machine learning model. The simulation results show that the DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.

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Section: Related Workmentioning

confidence: 99%

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

Lü

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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“…CS algorithm [143] was also adapted to optimize ANNs structure and parameters to improve the accuracy and the efficiency of the model. In [144], Cristin et al proposed a DL method for plant disease determination and prediction. The method uses a Deep Belief Network (DBN) model for this task, and the model is optimized by integrating Reiter optimization algorithm and CS algorithm.…”

Section: ) Deep Learning Based On Meta-heuristic Algorithmsmentioning

confidence: 99%

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

et al. 2021

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Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, Parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions. INDEX TERMSCommunity detection; Deep learning; Complex networks; Meta-heuristic algorithms; Parallel computing.

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“…These spatio-spectral data was also known as datacubes as it contained information of two spatial and one spectral dimension. These datacubes were then passed to a deep CNN known as Residual Network (ResNet) [73] that achieved an accuracy of 91.09%. To evaluate the proposed classifier, two different datasets were used; namely processed rice, and paddy rice.…”

Section: ) Deep Learningmentioning

confidence: 99%

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Ahmed

Ali²,

Khan

2021

IEEE Access

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Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection

Cited by 71 publications

References 43 publications

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

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