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 nearinfrared 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 ApplicationsThe 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.

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Analyzing the quality of biological products is a difficult task, as their physicochemical properties such as size, shape, color, and texture change over time. Among many quality attributes, automatic classification of defects is a challenging work due to similarity or diversity of defects in terms of shape, color, and texture within intra and inter cultivars. In this work, an image analysis and machine learning‐based method to identify and classify four defects of Kinnow mandarins is proposed. A simple and fast adaptive thresholding technique was used to segment the defects. Defects discriminatory abilities of three prevalent texture descriptors namely local binary patterns, gray level co‐occurrence matrix (GLCM), and gray level run length matrix (GLRLM) were explored. In order to measure the effectiveness of color models in food analysis techniques, texture features were extracted on individual and combined color channels of three popular color models, that is, RGB, HSV, and CIELAB. Two machine‐learning techniques: random forest (RF) and artificial neural networks (ANNs) classifiers were trained with extracted features to predict the defects. The highest accuracies of 93.5 and 89.3% and average accuracies of 88.95 and 80.67% were achieved by ANN and RF classifiers, respectively, for the feature set {GLCM, GLRLM} on {H,S,V} color set.Practical applicationsKinnow mandarins' cultivation area occupies around 50% of the total citrus farming regions in India. Currently, the expert laborers manually carry out the grading of Kinnow fruits based on the existence of external defects. This process is labor‐intensive, inefficient, and time‐consuming. However, now this labor‐intensive task can be replaced with automatic fruit classification machines based on computer vision (CV) and machine learning (ML) technologies. Presently, there is a scarcity of studies as well as algorithms to automate the process of Kinnow fruit defects classification. The developed CV‐ and ML‐based algorithm is capable to identify and discriminate four types of external defects pertaining to Kinnow mandarins. The classification accuracy and other performance measures achieved on the developed algorithm make it ideal for real‐time online Kinnow defects classification systems.