Semi-Supervised Density Estimation with Background-Augmented Data for In Situ Seed Counting

Baek-Gyeom Sung, Chun-Gu Lee, Yeong-Ho Kang, Seung-Hwa Yu () and Dae-Hyun Lee ()
Additional contact information
Baek-Gyeom Sung: Department of Smart Agriculture Systems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Chun-Gu Lee: Department of Agriculture Engineering, National Institute of Agricultural Sciences, Jeonju 54875, Republic of Korea
Yeong-Ho Kang: Department of Crops and Food, Jeonbuk State Agricultural Research and Extension Services, Iksan 54591, Republic of Korea
Seung-Hwa Yu: Department of Agriculture Engineering, National Institute of Agricultural Sciences, Jeonju 54875, Republic of Korea
Dae-Hyun Lee: Department of Smart Agriculture Systems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea

Agriculture, 2025, vol. 15, issue 15, 1-21

Abstract: Direct seeding has gained prominence as a labor-efficient and environmentally sustainable alternative to conventional transplanting in rice cultivation. In direct seeding systems, early-stage management is crucial for stable seedling establishment, with sowing uniformity measured by seed counts being a critical indicator of success. However, conventional manual seed counting methods are time-consuming, prone to human error, and impractical for large-scale or repetitive tasks, necessitating advanced automated solutions. Recent advances in computer vision technologies and precision agriculture tools, offer the potential to automate seed counting tasks. Nevertheless, challenges such as domain discrepancies and limited labeled data restrict robust real-world deployment. To address these issues, we propose a density estimation-based seed counting framework integrating semi-supervised learning and background augmentation. This framework includes a cost-effective data acquisition system enabling diverse domain data collection through indoor background augmentation, combined with semi-supervised learning to utilize augmented data effectively while minimizing labeling costs. The experimental results on field data from unknown domains show that our approach reduces seed counting errors by up to 58.5% compared to conventional methods, highlighting its potential as a scalable and effective solution for agricultural applications in real-world environments.

Keywords: direct seeding; sowing uniformity; seed counting; density estimation; semi-supervised learning; background augmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/15/1682/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/15/1682/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:15:p:1682-:d:1716843

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().