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Taylor-student psychology based optimization integrated deep learning in IoT application for plant disease classification

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Abstract

The Internet of Things (IoT) has grown more importance in agriculture production, as it helps to observe and store up information in a large environment. The plant leaf disease condenses the quantity and quality of agricultural products. Hence, the farmer needs to find and discover the plant disease at the beginning stage. The plant disease can be present in any part, like leaves, fruits and stems. Therefore, it is an important research area to detect plant disease automatically to reduce economic or production loss. Without appropriate classification of the disease and the disease-causing mediator, the disease control process can be a waste of time and money and can lead to additional plant losses. This research developed a method named Taylor Student Psychology Based Optimization integrated Deep Q network (TSPBO-based DQN) to detect plant disease in IoT simulated system atmosphere. The nodes are randomly dispersed in the system area to collect plant images. The captured images are routed to the sink node to complete the proposed method's disease recognition scheme. The proposed method is highly efficient in classifying the plant diseases and has shown outstanding performance by acquiring high accuracy, sensitivity, specificity and remaining energy.

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Abbreviations

IoT:

Internet of Things

TSPBO-based DQN:

Taylor student psychology based optimization integrated deep Q network

SVM:

Support vector machine

ANN:

Artificial neural network

RF:

Random forest

KNN:

K nearest neighbour

FLD:

Fisher linear discriminant

SIFT:

Scale invariant feature transform

CNN:

Convolutional neural network

LTP:

Local ternary pattern

SLIF:

Spider local image feature

SPBO:

Student psychology based optimization algorithm

EEG:

Energy-efficient geographic

ResNet:

Residual neural network

GLCM:

Grey-level co-occurrence matrix

BS:

Base station

R-CNN:

Region-based CNN

ROI:

Region of interest

SCA based RideNN:

Sine cosine algorithm based rider neural network

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Prathyusha Engineering College, Tiruvallur, India

    S. Vimala

  2. Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, India

    T. V. Madhusudhana Rao

  3. School of Computing Science and Engineering (SCSE), VIT Bhopal University, Bhopal-Indore Highway, Bhopal, India

    A. Balaji

  4. School of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh, India

    Balajee Maram

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  1. S. Vimala
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  2. T. V. Madhusudhana Rao
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Vimala, S., Madhusudhana Rao, T.V., Balaji, A. et al. Taylor-student psychology based optimization integrated deep learning in IoT application for plant disease classification. Wireless Netw 29, 919–939 (2023). https://doi.org/10.1007/s11276-022-03150-2

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