Research on a Lightweight Method for Maize Seed Quality Detection Based on Improved YOLOv8-Reference-Cited by-同舟云学术

Research on a Lightweight Method for Maize Seed Quality Detection Based on Improved YOLOv8

Published:2024 Issue: Volume:12 Page:32927-32937
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Niu Siqi¹^ORCID,Xu Xiaolin¹,Liang Ao¹,Yun Yuliang²,Li Li³,Hao Fengqi⁴,Bai Jinqiang⁴,Ma Dexin¹^ORCID

Affiliation:

1. College of Animation and Communication, Qingdao Agricultural University, Qingdao, China

2. College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao, China

3. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China

4. Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation

Central Government Guiding Local Science and Technology Development Special Plan

Shandong Province Key Research and Development Plan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10433488.pdf?arnumber=10433488

Reference25 articles.

1. Estimating the global number and distribution of maize and wheat farms

2. Conservation and Use of Latin American Maize Diversity: Pillar of Nutrition Security and Cultural Heritage of Humanity

3. A non-destructive and highly efficient model for detecting the genuineness of maize variety 'JINGKE 968′ using machine vision combined with deep learning

4. Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

5. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems

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