Spectral reflectance dataset of soybean varieties measured using Vis/NIR spectroscopy with an AS7265X multispectral sensor for quality classification
Description
This dataset contains multispectral reflectance measurements of soybean samples collected using a portable Visible/Near-Infrared (Vis/NIR) spectroscopy system equipped with an AS7265X multispectral sensor. The sensor records spectral responses across 18 discrete wavelength channels ranging from 410 nm to 940 nm, covering the ultraviolet (UV), visible (VIS), and near-infrared (NIR) spectral regions. The dataset was developed to support the analysis and classification of soybean quality using non-destructive spectroscopy combined with machine learning techniques. Each sample entry includes spectral reflectance intensity values from the AS7265X sensor along with categorical labels describing the soybean variety group and quality class. The soybean samples used in this dataset are non-GMO Indonesian soybean varieties, consisting of Anjasmoro and Grobogan. To represent both seed and consumption categories, the samples are grouped into four variety labels: - BA (Anjasmoro soybean seed) - BG (Grobogan soybean seed) - KA (Anjasmoro soybean for consumption) - KG (Grobogan soybean for consumption) In addition to the variety label, each sample is assigned a quality class representing soybean quality levels: - Poor - Fair - Premium Each dataset record contains 18 spectral reflectance intensity values, one Varietas column indicating the soybean group (BA, BG, KA, KG), and one Class column representing the quality category (Poor, Fair, Premium). The spectral values represent the original sensor outputs in arbitrary units (a.u.), enabling flexible preprocessing and modeling for spectral analysis and machine learning applications. This dataset can support research in agricultural engineering, seed quality assessment, spectral feature analysis, and machine learning-based classification of soybean quality. It also provides a basis for developing portable spectroscopy systems for rapid and non-destructive evaluation of soybean seeds and consumption-grade soybeans. The data collection activities were conducted in 2025. This research was financially supported by the Directorate of Research and Community Service, Directorate General of Research and Development, Ministry of Higher Education, Science, and Technology, under Contract No. 125/C3/DT.05.00/PL/2025. Key Features: - Multispectral reflectance data from 18 wavelength channels (410–940 nm). - Non-destructive measurement using portable Vis/NIR spectroscopy. - Includes two Indonesian soybean varieties: Anjasmoro and Grobogan. - Four soybean sample groups representing seed and consumption categories. - Three soybean quality classes: Poor, Fair, and Premium. - Suitable for machine learning classification, seed quality prediction, and spectral analysis.
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Steps to reproduce
The samples used in this dataset consist of non-GMO Indonesian soybean varieties: Anjasmoro and Grobogan. These seeds are categorized into four groups: BA (Anjasmoro seed), BG (Grobogan seed), KA (Anjasmoro consumption), and KG (Grobogan consumption). Each seed sample is labeled with a quality class: Poor, Fair, or Premium. For data acquisition, the AS7265X multispectral sensor (410–940 nm) is used in conjunction with a portable Vis/NIR spectroscopy system. The spectral measurements are taken across 18 wavelength channels for each sample, with 10–15 seeds per sample and 4 replications conducted to ensure accuracy. To minimize environmental variations, the seeds are conditioned for 24 hours at 25–30°C with 60–75% relative humidity before measurements are taken. The data collection is carried out in a controlled indoor environment to reduce the influence of ambient light interference. During the data acquisition process, sensor calibration is performed using a white reference panel after every 20 measurements to maintain consistency in reflectance readings. Finally, the reflectance data is stored in XLSX format, with each record containing 18 spectral reflectance intensity values, along with columns for Variety (BA, BG, KA, KG) and Quality Class (Poor, Fair, Premium).
Institutions
- Universitas Nusa MandiriJakarta, Jakarta
- Universitas Pembangunan Nasional Veteran JakartaJakarta, Jakarta