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Prediction and classification of rice leaves using the improved PSO clustering and improved CNN

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Abstract

The world’s main source of food supply in agriculture, and due to the increase in population growth, the requirement for food supply is increasing day by day. Rice is the main food crop in many countries, and rice leaf infections are a common hazard to rice yield. So the early detection of rice leaf infection is crucial for rice crop growth and ensuring sufficient supply for a fast-growing population. Conventional manual identification of rice diseases is time-consuming, ineffective, and costly. To overcome this challenge, we can take images of the affected area of the plants and test them with a pre-trained model to identify and classify the rice diseases. Convolutional Neural Networks (CNN) in deep learning extracts the features from the images and diagnoses the diseases efficiently, which addresses the above issues. The main objective of this study is to minimize the loss and the processing time to predict rice diseases. Optimizers play a crucial role in reducing the loss in the neural network. We proposed an Improved Activation and Optimizer Function (IAOF) in the CNN model to minimize the loss and improve the prediction performance and classification accuracy. The output performance of the proposed IAOF-CNN surpasses the other existing methods and has been verified as the best implementation.

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Abbreviations

CNN:

Convolution neural network

RBF:

Radial basis function

SVR:

Support vector regression

SVM:

Support vector machine

ANN:

Artificial neural network

PCA:

Principal component analysis

RF:

Random forest

SAN:

Self-attention network

NN:

Neural network

KNN:

K-Nearest neighbor

IAOF:

Improved activation and optimizer function

IOF:

Improved optimization function

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

    Usharani Bhimavarapu

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Bhimavarapu, U. Prediction and classification of rice leaves using the improved PSO clustering and improved CNN. Multimed Tools Appl 82, 21701–21714 (2023). https://doi.org/10.1007/s11042-023-14631-7

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  • DOI: https://doi.org/10.1007/s11042-023-14631-7

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