Work

SSANet-Based Lightweight and Efficient Crop Disease Detection

Year: 2025

Type: article

Abstract: Accurately identifying crop pests and diseases ensures agricultural productivity and safety. Although current YOLO-based detection models offer real-time capabilities, their conventional convolutional layers involve high comp... | Find, read and cite all the research you need on Tech Science Press

Source: Computers, materials & continua/Computers, materials & continua (Print)

DOI: 10.32604/cmc.2025.067675

Authors: Hao Sun, Di Cai, Dae-Ki Kang

Institutions: Dongseo University, Weifang University of Science and Technology

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: diamond

Funders: National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea