Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology wa...

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Veröffentlicht in:Frontiers in plant science Jg. 13; S. 1047479
Hauptverfasser: Wu, Qingsong, Xu, Lijia, Zou, Zhiyong, Wang, Jian, Zeng, Qifeng, Wang, Qianlong, Zhen, Jiangbo, Wang, Yuchao, Zhao, Yongpeng, Zhou, Man
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media SA 10.11.2022
Frontiers Media S.A
Schlagworte:
Accuracy
Airborne microorganisms
Algorithms
Cameras
Chromatography
Classification
Crop yield
Ensemble learning
Food
Food quality
Germination
Humidity
Hyperspectral imaging
Imaging techniques
Legumes
Machine learning
Mildew
mildew detection
Mold
nondestructive testing
Nuts
peanut seeds
Peanuts
Plant Science
Real time
Seeds
Spectrum analysis
stacked ensemble learning model
variety classification
Vision systems
Wavelengths
nondestructive testing
variety classification
peanut seeds
stacked ensemble learning model
mildew detection
ISSN:1664-462X, 1664-462X
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Zusammenfassung:Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.
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This article was submitted to Sustainable and Intelligent Phytoprotection, a section of the journal Frontiers in Plant Science
These authors have contributed equally to this work
Edited by: Ning Yang, Jiangsu University, China
Reviewed by: Yongcheng Jiang, Tianjin Agricultural University, China; Yongming Chen, Hubei Normal University, China
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2022.1047479