Classification and Recognition of Soybean Quality Based on Hyperspectral Imaging and Random Forest Methods

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 5; p. 1539
• Main Authors: Chen, Man, Chang, Zhichang, Jin, Chengqian, Cheng, Gong, Wang, Shiguo, Ni, Youliang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2025; MDPI
• Subjects: Accuracy; Algorithms; Classification; classification and identification; Detectors; feature extraction; Glycine max - chemistry; Glycine max - classification; Grain; Harvesting; hyperspectral imaging; Hyperspectral Imaging - methods; Imaging systems; Mathematical optimization; Methods; Moisture content; Random Forest; Software; Soybean; Soybeans; Spectrum analysis; Vision systems; United States > US; soybean; random forest; feature extraction; hyperspectral imaging; classification and identification

To achieve the rapid and accurate classification and identification of soybean components, this study selected soybeans harvested by the 4LZ-1.5 soybean combine harvester as the research subject. Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral infor...