Year: 2024
Type: conference-paper
Abstract: This paper aims to achieve precise identification of diseases and pests affecting pear trees through the integration of YOLOv5, Jetson Nano, big data, and deep learning techniques. The objective is to facilitate timely detection of these issues, thereby enabling early prevention and control measures for pear tree health. The study employs YOLOv5 as the primary model, which is implemented on the embedded device Jetson Nano. By leveraging the GPU parallel processing capabilities of Jetson Nano's deep learning framework, this approach enhances picture analysis and detection speed while improving quality and efficiency. [1] Furthermore, it integrates big data with deep learning methodologies to bolster the accuracy of disease detection and identification. Utilizing these advanced technologies allows for accurate recognition of diseases and pests associated with pear trees through image analysis. This significantly reduces both the complexity involved in detecting such conditions and lowers operational thresholds for practitioners in the field. In comparison to traditional detection methods, YOLOv5 technology exhibits no stringent requirements regarding environmental conditions or backgrounds; thus, it remains less susceptible to variations caused by weather factors—making it a superior choice for pest and disease detection in agricultural settings.
Authors: Yuanyuan Feng, Zhihan Xu, Xiaoyu Yang
Institution: Chengdu University of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4408024997