Year: 2025
Type: article
Abstract: Agriculture plays a crucial role in global food security, yet crop diseases pose a significant threat, leading to substantial yield losses and economic challenges for farmers.Traditional disease detection methods rely on manual inspections, making them inefficient and inaccurate.This work, titled quot;Agrishield,quot; focuses on developing an automated solution to classify crop diseases using machine learning (ML) and deep learning (DL) techniques.By leveraging advanced algorithms such as decision trees, random forests, and convolutional neural networks (CNNs), the system analyzes leaf images to identify diseases based on visible symptoms like spots and discoloration.Agrishield provides an efficient, realtime diagnostic solution, enabling farmers to detect and manage crop diseases early.This system aims to enhance agricultural productivity, reduce financial losses, and promote sustainable farming practices [1].
Authors: Harshit Porwal, Nidhi Nigam, Chanchal Bansal
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4414923940
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