Deep Learning-Based Pre- trained Model for Classification and Detection of Potato Leaf Disease
Year: 2024
Type: article
Abstract: : From the Holocene period of the earth, formation of physical geography has started and the flaura's and faunna's had played a very huge role in supplementing to the earth. Apart from the whole earth India is the country that contributed about 7.68% of total world's farming output. The potatoes is an essential food crop worldwide, feeding billions of individuals. Nevertheless, the presence of different diseases has a significant impact on potato production and quality, resulting in substantial financial losses for farmers and potential concerns regarding food security. Detecting these diseases accurately and early on is vital for implementing effective strategies to manage them. In recent times, Convolutional Neural Networks (CNNs), a type of deep learning technique, have shown remarkable success in recognizing various objects in images. This work investigates the use of CNNs to identify diseases in potato leaves, aiming to aid in prompt disease diagnosis and protect crops.
Authors: Niharika Sharma, Jaimala Jha
Institution: Madhav Institute of Technology & Science
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4404889909