Classification of Hard and Soft Wheat Species Using Hyperspectral Imaging and Machine Learning Models

Nitin Tyagi, Balasubramanian Raman, Neerja Mittal Garg

Abstract: Ensuring the identification and authenticity of wheat seeds are critical tasks in the food grain industry. In this work, twenty wheat varieties were collected from three different locations in India. The near-infrared (NIR) hyperspectral imaging technique (spectral range 900–1700 nm) was employed in conjunction with machine learning models to discriminate twenty different wheat varieties into two classes: hard wheat and soft wheat. The data images were taken from both sides of the seed (ventral and dorsal side). The dataset includes images of 20,160 seeds. Five different machine learning models were used for classification: Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). The models were trained using the mean spectral values extracted from the hyperspectral images. Five preprocessing techniques pretreated the mean spectral values of the hyperspectral image: Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky Golay Smoothing (SG), Savitzky Golay First Derivative (SG-1), and Savitzky Golay Second Derivative (SG-2). The model’s performance was evaluated for both raw and preprocessed data. The Support Vector Machine exhibited exceptional performance, attaining an astonishing accuracy rate of 95.01% for amalgamated data (encompassing both ventral and dorsal side data), 95.05% for exclusively ventral side data, and an impressive 95.37% for exclusively dorsal side data.