부산대학교 도서관

The classification algorithms of different peanut varieties were studied based on hyperspectral imaging technology. Firstly, the spectral images of five peanut species were collected by hyperspectral instrument produced by Zhuolihanguang Co., LTD. Then SpacVIEW was used to correct the spectral images in black and white, and ENVI5.1 was used to extract the interest in the spectral image of each peanut and calculate the mean spectral reflection value of the region. The spectral characteristic curves of the five peanut samples all showed certain differences, which lays a foundation for the feasibility of modeling in the next step. In order to eliminate the influence of non-quality factor information in hyperspectral spectral data, a variety of data preprocessing methods were used to eliminate noise in the original spectral data, and XGBoost, LightGBM, CatBoost and GBDT algorithms were used to extract feature bands. XGBoost and LightGBM were then used for classification modeling of extracted feature bands. In the classification model, both XGBoost and LightGBM can reach 99.33%, while other performance evaluation indexes cannot distinguish these two models well. Therefore, Optuna algorithm was selected to optimize the two algorithms respectively. After optimization, both LightGBM and XGBoost have improved to varying degrees, but LightGBM is relatively obvious, especially in fit_time, which is 11 times faster than XGBoost and 16 times faster than before optimization. Therefore, the best classification algorithm selected in this study is MF-LightGBM-LightGBM-Optuna-LightGBM. The research of peanut classification method provides a strong theoretical basis and technical support for the revitalization of rural industry, the integration of peanut agriculture and industry and the acceleration of the modernization of agricultural industry system, production system and management system.