Nondestructive identification of barley seeds variety using near-infrared hyperspectral imaging coupled with convolutional neural network

Nondestructive inspection of varietal purity of seeds plays an important role in crop improvement, agricultural production, and plant breeding. In the present study, a rapid and nondestructive technique, that is, near-infrared hyperspectral imaging (NIR-HSI) was applied to discriminate the barley seeds variety. A large dataset of 35,280 seeds was collected from different locations and years incorporating 35 Indian barley varieties (29 hulled and 6 naked barley varieties). The hyperspectral reflectance images of the ventral side and dorsal side of seeds were acquired in the near-infrared range of 900-1700 nm. Mean spectra were extracted and pretreated by six preprocessing techniques (standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, SG first derivative, SG second derivative, and detrending). Subsequently, raw and preprocessed spectral data were fed as input to the convolutional neural network (CNN) including traditional machine learning models (partial least squares discriminant analysis (PLS-DA), K-nearest neighbors (KNN), and support vector machines (SVM)). It was observed that the end-to-end CNN model built on raw spectra overperformed the model using the preprocessing strategies. In addition, the CNN model outperformed the three traditional models with a testing set accuracy of greater than 98%. The results demonstrated that NIR-HSI coupled with end-to-end CNN could be a robust way to quickly, accurately, and nondestructively identify the variety of barley seeds. Practical Applications The commercial price and quality of barley mainly depend upon its varietal purity. Identification of barley seeds variety is an important step to select the seeds for different purposes such as food, malt, and fodder. Traditional methods for the identification of barley seeds variety are time-consuming, expensive, and destructive. Near-infrared hyperspectral imaging, as an emerging fast and nondestructive technique, looks promising for seed quality and safety evaluation. Moreover, the convolutional neural network has a better capability to accurately discriminate spectra extracted from seeds of different varieties of barley. The results in this study can provide a reference and theoretical basis to develop a real-time inspection system for fast, accurate, and nondestructive barley seeds purity testing.

Automated summary

The study utilized near-infrared hyperspectral imaging combined with machine learning for barley seeds variety discrimination, demonstrating high accuracy and speed. By building an end-to-end convolutional neural network, the method shows better capability in identifying seeds of different barley varieties compared to traditional approaches.