Authors :
Aryan Chaudhary; Mohit Gupta; Upasana Tiwari
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/mrx4pe29
Scribd :
http://tinyurl.com/nhhp949h
DOI :
https://doi.org/10.5281/zenodo.10432632
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.
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.
