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Infected Plant Leaves Detection Using Multilayered Convolutional Neural Network and Quantum Classifier
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Author(s): Damandeep Kaur (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India), Shamandeep Singh (Code Quotient, Mohali, India), Simarjeet Kaur (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India), Gurpreet Singh (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India)and Rani Kumari (Chitkara University Institute of Engineering and Technology, Chitkara University, India)
Copyright: 2024
Pages: 17
Source title:
Quantum Innovations at the Nexus of Biomedical Intelligence
Source Author(s)/Editor(s): Vishal Dutt (AVN Innovations Pvt. Ltd., India), Abhishek Kumar (Department of CSE, UIE, Chandigarh University, Punjab, India), Sachin Ahuja (Chandigarh University, India), Anupam Baliyan (Geeta University, India)and Narayan Vyas (AVN Innovations Pvt. Ltd., India)
DOI: 10.4018/979-8-3693-1479-1.ch007
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Abstract
The objective of this chapter is to establish an accurate and efficient approach to diagnosing symptoms of disease, and therefore develop an optimal method to solve the problem efficiently and cost-effectively. Quantum-based classifiers also play an essential role in detecting the leaf profile features in recognizing leaves using a computer vision system that can be used with a quantum computer. Enhanced double quantum images encryption (EDQRCI) enables automation to produce and create quantum pictures that transform color pixels into well-defined shapes ways. Therefore, a novel model is proposed to identify and diagnose the mango leaves that are infected with Anthracnose fungal disease with the help of a multi-layer convolution neural network (MCNN). The images of unhealthy and healthy leaves of mango trees are taken from a dataset, namely, Plant Village, to determine the efficacy of the model. The simulation of the proposed model is implemented with the help of MATLAB software, and shows a higher classification accuracy of the proposed model MCNN.
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