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Plant Disease Detection Using Generative AI and Deep Learning Models
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Author(s): N. Bharanidharan (Vellore Institute of Technology, India), R. S. Sarweshwaran (Vellore Institute of Technology, India), M. Shomesh (Vellore Institute of Technology, India), S. Tharun (Vellore Institute of Technology, India)and Kumar V. Vinoth (Vellore Institute of Technology, India)
Copyright: 2025
Pages: 32
Source title:
Humans and Generative AI Tools for Collaborative Intelligence
Source Author(s)/Editor(s): Jingyuan Zhao (University of Toronto, Canada), V. Vinoth Kumar (Vellore Institute of Technology, India), Polinpapilinho F. Katina (University of South Carolina Upstate, USA)and Joseph Richards (California State University, Sacramento, USA)
DOI: 10.4018/979-8-3693-8332-2.ch009
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Abstract
Cassava Plant scientifically Manihot esculenta which is also known as the tapioca plant, is a shrub that is majorly cultivated in many countries. The major cassava plant diseases are Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Green Mottle (CGM) and Cassava Mosaic Disease (CMD). The objective of this work is to make the Cassava Plant Disease Prediction from the given input image and tell whether the provided sample is infected with the above mentioned disease or not. To meet the main objective, we build the Model with the Convolution Neural Network, Visual Geometry Group 16, ResNet50, EfficientNetB0, Visual Transformer, and Variational Auto-encoder. Variational Auto-encoder model with various latent dimension size and optimizers are tested. Usage of generative variational autoencoder provides the highest accuracy of 95% while the other tested models are providing accuracy less than 90%.
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