Corn seed dataset based on hyperspectral and RGB images
Chao LI, Chen Zhang, Wenbo Zhang, Chengzhen LV, Yaqiang Li, Yufen Wang

TL;DR
This paper introduces a dataset of corn seeds using hyperspectral and RGB images for phenotypic analysis and machine learning applications.
Contribution
The paper presents a new multimodal corn seed dataset with hyperspectral and RGB images for agricultural research.
Findings
The dataset includes 2400 corn seed samples across 12 varieties under controlled lab conditions.
Preprocessing steps ensured data quality for classification and phenotypic analysis.
The dataset supports precision agriculture and machine learning research.
Abstract
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400–1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20–25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis,…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRemote Sensing in Agriculture · Smart Agriculture and AI · Spectroscopy and Chemometric Analyses
