Plant Disease Identification Using Deep Learning and Transfer Learning
Year: 2025
Type: conference-paper
Abstract: There is a dire need to understand plant diseases and its effect on agricultural productivity. Agriculture takes up substantial amount of India’s economy. The spread of plant disease in India poses a heavy challenge to crop yield, thereby directly impacting livelihood of millions of farmers across India. Through this work, we will be presenting two approaches based on CNN (Convolutional Neural Networks) and VGG19 to improve accuracy of existing solutions. PlantVillage dataset is used for training as well as testing the models, which contains 21,000+ images. The first model based on CNN has a accuracy of 93.82% and the second model based on VGG19 has a accuracy of 88.93%. Our work is mainly focused on comparing the results of our models with some previous manuscripts based on the same dataset and how we achieved greater accuracy.
Source: Procedia Computer Science
Authors: Ravindra Yadav, Anita Seth, Ansh Sharma, Meet Oza
Institution: Devi Ahilya Vishwavidyalaya
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4410774531