Identification of peanut storage period based on hyperspectral imaging technology

Abstract

Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage.

Nondestructive Detection of Sunflower Seed Vigor and Moisture Content Based on Hyperspectral Imaging and Chemometrics

“…Hyperspectral technology has gained extensive utilization in the field of nondestructive and rapid assessment of seed vitality owing to its inherent characteristics of rich multispectral information, high spatial resolution, and strong detection sensitivity [20][21][22][23][24]. Furthermore, groundbreaking research by Wang, Zheli et al [25] ingeniously integrated hyperspectral imaging technology with machine learning algorithms, facilitating precise identification and classification of deteriorated maize seeds through meticulous analysis of surface texture features.…”

Honey quality detection based on near-infrared spectroscopy

“…Hyperspectral imaging technology has been applied in many fields, such as peanut (Zou et al, 2022b) germination prediction (Zou et al, 2022c), mildew detection (Zou et al, 2022a), hot pot (Zou et al, 2023) oil detection (Zou et al, 2022d), fruit grading (Zou et al, 2021), etc. Using physical and chemical methods to judge the quality of honey (Kek et al, 2017;Wan et al, 2018), the results are often very accurate (Shamsudin et al, 2019).…”