Sensors, Vol. 19, Pages 4065: Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network

Sensors, Vol. 19, Pages 4065: Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network Sensors doi: 10.3390/s19194065 Authors: Zhu Zhou Zhang Bao Wu Chu Yu He Feng 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, illus...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research