## 1 Introduction  
India is the leading exporter of the basmati rice (_Oryza sativa_) to the global market. The annual export of basmati rice was \(\sim\)4.05 million MT to the global market during the year 2015-2016 [1]. Basmati rice is a protracted slender grain variety of aromatic rice grown in the Indian sub-continent. It has a high product value due to its flavour, delicate texture, delightful fragrance, and softness. The length of basmati rice grain is longer than the width, and it grows even longer during cooking [2]. The high-value basmati rice grain has to go through several operations (such as threshing, handling, de-hasking, milling and whitening of grains) starting from harvesting of paddy to final production of rice grains by means of several mechanical systems [3]. Thereby, the grade of the produced grain exclusively depends on the adjustment of the equipment used in the various mentioned operations. In general, in a rice milling facility, the quality grade of product is being monitored by visual inspection by experienced quality control personnel at 2-3 h intervals, rather utilizing a continuous operational measurement method. This means that the operator, based on his experience and proficiency with the processing machinery, assesses the quality grade of the product by mere visual inspection of rice grain appearance and making the required adjustments which are time-consuming and subjective. Alternatively, image-based grading approaches are nondestructive and rapid. With suitable statistical or machine learning techniques, the image-based approaches are proven to be an efficient way to achieve automatic inspection and grade evaluation efficiently [4; 5]. During last decade, researchers have investigated several techniques based on machine vision and digital image processing for quality assessment of rice kernels which are fast, non-destructive, accurate, and cost-effective as compared to traditional methods [6; 7]. Image-based approaches have been applied for characterizing rice grains using either one of the morphological, colour, and textural features, or a combination. However, in order to find suitable rice grain descriptor and to improve the classification accuracy, it is imperative that some key features should be selected to describe grain feature exactly. The marketing value of rice depends on its physical qualities after processing. Major axis/minor axis ratio of the rice kernel is reported as a key feature of basmati rice which might identify the adulteration of basmati rice with other rice varieties [8]. [PERSON] and [PERSON] [8] reported the major axis/minor axis ratio of 3.92-4.09 as an indicator of pure Basmati-370 variety. In the context of grading of rice, the percentage of the head or whole grain and broken grain is a most important factor which determines the milling efficiency. Till date, several studies reported improvement in classification accuracy of rice grain using machine vision and image processing techniques [9; 10; 11; 12]. [PERSON] et al. [13] illustrated that determining grain variety using a simple mathematical function is difficult because the grain has various morphologies, colours, and textures. Alternatively, artificial neural network (ANNs) techniques have been applied for grain quality control and discrimination of grain variety. [PERSON] et al. [14] proposed a methodology to identify five corn varieties with the accuracy of more than 90% using pattern recognition techniques and neural networks. In a comparative analysis in [11] of artificial neural networks, support vector machines, decision trees and Bayesian Networks to classify milled rice samples, it has produced highest classification accuracy with ANN. Despite promising results, there are several problems might arise with ANN's training and designing [15; 16; 17; 18]. The assignment of the weights in ANN structure is one of the most important problems [19] which has a direct effect on its performance. Moreover, the uncertainties in ANN output is proven to be a challenging issue [20].