Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network

Sensors

Susu Zhu¹  Baohua Wu¹  Chu Zhang¹  Yidan Bao¹  Lei Feng¹  Hangjian Chu¹  Lei Zhou¹  Yue Yu¹  Yong He¹ 

[1] College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China;

关键词: soybean;    hyperspectral imaging technology;    convolutional neural network;    pixel-wise spectra;    a majority vote;   

DOI  :  10.3390/s19194065

来源: DOAJ

【 摘 要 】

Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and average spectra were also obtained. Convolutional neural networks (CNN) using the average spectra and pixel-wise spectra of different numbers of soybeans were built. Pixel-wise CNN models obtained good performance predicting pixel-wise spectra and average spectra. With the increase of soybean numbers, performances were improved, with the classification accuracy of each variety over 90%. Traditionally, the number of samples used for modeling is large. It is time-consuming and requires labor to obtain hyperspectral data from large batches of samples. To explore the possibility of achieving decent identification results with few samples, a majority vote was also applied to the pixel-wise CNN models to identify a single soybean variety. Prediction maps were obtained to present the classification results intuitively. Models using pixel-wise spectra of 60 soybeans showed equivalent performance to those using the average spectra of 810 soybeans, illustrating the possibility of discriminating soybean varieties using few samples by acquiring pixel-wise spectra.

【 授权许可】

Unknown