Research on peanut variety classification based on hyperspectral image

Abstract

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.

Method of Peanut Pod Quality Detection Based on Improved ResNet

“…As a cost-effective quality detection tool, RGB images and deep learning could be successfully applied to peanut pod detection in industry and agriculture. The literature [35] used hyperspectral to study the classification algorithm of different peanut varieties, and the literature [37][38][39][40] used the regular placement of scanners for data acquisition, which limited the practicality of the model. Secondly, in order to improve the ability to capture more features of peanut pods, the CBAM attention module was added.…”

“…Therefore, the study of peanut kernels and peanut pods in this field has attracted the attention of many scholars. Zou et al [38] studied classification algorithms for different peanut varieties based on hyperspectral imaging technology. The designed XGBoost and LightGBM classification models have an accuracy of 99.33%.…”

Hyperspectral imaging with a band matrix reduction method to detect early drought stress in tomato

“…The system automatically closes the shutter to measure the black reference, while for the white reference, it scans the 99% Spectralon reflectance white board (Labsphere, North Sutton, NH). The calculation formula of relative reflectance R is Equation 1 (Zou et al, 2022a):…”

Rapid determination of water content in potato tubers based on hyperspectral images and machine learning algorithms

“…In order to eliminate the effects of factors that had no relationships with the moisture content in the hyperspectral spectrum information, 17 data preprocessing methods were employed to eliminate the noise present in the original spectral data and to identify outliers in the box plots of the moisture contents. such as first derivative (FD), second derivative (SD), box smoothing (BS), L2 norm normalizationL2 (L2NN), moving average method (MAM), multiplicative scatter correction (MSC), min-max standardization (MMS), anti-cotangent normalization (CAN), wavelet threshold denoising (WTD), logarithmic transformation normalization (LTN), exponential smoothing (ES), median filtering (MF), gaussian window smoothing (GWS), z-score standardization (ZSS), local regression-weighted linear least squares and a first order polynomial model (LR1), local regression-weighted linear least squares and a second order polynomial model (LR2) and Savitzky-Golay filtering (SG) were used to preprocess the original spectral data (RD) (Ruszczak & Boguszewska-Mańkowska, 2022;Zou et al, 2022).…”