Publication Date: 2023/12/26
Abstract: Detecting plant diseases during the growth of plants is a critical challenge in agriculture, as late detection can lead to reduced crop yields and lower profits for farmers. To tackle this issue, researchers have developed advanced frameworks based on Neural Networks[1]. However, many of these methods suffer from limited prediction accuracy or require a vast number of input variables. This project comprises of CNN and LSTM models, the CNN component of the project has demonstrated remarkable accuracy, achieving a 98.4% success rate in identifying plant diseases from static images.
Keywords: CNN Architecture, VGG Architecture, Fully Connected Layers, VGG-19, Neural Networks, CNN, LSTM, Convolutional Layers.
DOI: https://doi.org/10.5281/zenodo.10432632
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23DEC614.pdf
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