Cloud-Based System for Supervised Classification of Plant Diseases Using Convolutional Neural Networks

Jain, Lorick; A, Harsha Vardhan M; Nishanth, M.; Shylaja, S S

doi:10.1109/ccem.2017.22

Cited by 17 publications

(5 citation statements)

References 8 publications

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“…The DBOQS controller was designed to manage mobile communication between the three CNNs and the users. To detect the health of various crops, including pomegranate trees and firecracker plants, Jain et al [ 70 ] utilized a CNN with a customized architecture hosted on a cloud service and a mobile application for Android smartphones. Picon et al [ 71 ] and Esgario et al [ 72 ] found three wheat illnesses and four coffee leaf diseases and pests, respectively, using a mobile application and a CNN with a modified RetNet-50 architecture hosted on a cloud service.…”

Section: Related Workmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after thae trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.

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Section: Related Workmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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“…The Maxwell Garnet equation is widely used to find the thermal conductivity in the liquid condition and reported to be fundamental for all other models. The thermal conductivity of the BPCMs with nanofiller was calculated based on the Maxwell-Garnett equation [31]. The effective thermal conductivity, k is given by: where kp is thermal conductivity of the nanofiller (Table 1), kf is thermal conductivity of the phase change material (Table 2) and ϕ is the particle volume concentration.…”

Section: Analytical Model For Thermal Conductivity Measurementmentioning

confidence: 99%

Effects of titanium oxide and graphene as nano-fillers on the thermal conductivity of biobased phase change materials as latent thermal heat storage

Hasbi

Norazman

Saharudin

2023

Materials Today: Proceedings

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“…In the field of construction engineering, artificial neural networks are used to predict concrete strength and find the nonlinear input-output relationship between concrete strength and its influencing factors [9,10]. In addition, artificial neural networks are used in the field of plant diseases control [11][12][13], process control and optimization [14][15][16], troubleshooting [17][18][19], intelligent control of industrial product assembly line [20][21][22], robotic surgery [23][24][25], intelligent driving [26][27][28], chemical product development [29][30][31], signal processing [32][33][34], and so on.…”

Section: The Origin and Development Of Artificial Neural Networkmentioning

confidence: 99%

Optimization of Extraction Process Based on Neural Network

Sun

Chen

2022

AJOCS

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Liquid-liquid extraction is a chemical unit operation that utilizes the difference in solubility or distribution ratio of target components in two immiscible solvents to achieve separation, extraction or purification. There are many factors that affect the extraction efficiency, and it is difficult to quickly optimize the process using traditional methods. Artificial neural network is a system structure composed of multiple artificial neuron models, with functions such as self-learning, associative storage and fault tolerance. It can be used for optimization or control of multi-variable complex systems, and has been successfully applied to the extraction process of various products. optimization. This paper discusses the basic situation of artificial neural network, and analyzes the research progress of extraction process optimization based on neural network.

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Cloud-Based System for Supervised Classification of Plant Diseases Using Convolutional Neural Networks

Cited by 17 publications

References 8 publications

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Effects of titanium oxide and graphene as nano-fillers on the thermal conductivity of biobased phase change materials as latent thermal heat storage

Optimization of Extraction Process Based on Neural Network

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