This study describes the classification of four varieties of bulk rice grain images using back‐propagation neural network (BPNN). Eighteen colour features, 27 texture features using grey‐level co‐occurrence matrix, 24 wavelet features and 45 combined features (combination of colour and texture) were extracted from the colour images of bulk rice grains. Classification was carried out on three different data set of images under different environmental conditions. It is seen that BPNN is able to classify faithfully the four varieties of rice grain even with a poor image quality. It is also found that classification based on reduced wavelet features outperform the classification using all other features (such as colour, texture features taken separately) for two data set of images with minimum resolution. The authors have further compared the proposed BPNN technique with other classifiers such as support vector machine, k ‐nearest neighbour and naive Bayes classifier on all the three data sets. It is found that the average classification accuracy of more than 96% was able to achieve using BPNN consistently on all different features for each data set.

This review paper critically examines the role of demand response (DR) in energy management, considering the increasing integration of renewable energy sources (RESs) and the rise in electric vehicle (EV) adoption. As the energy landscape shifts toward sustainability, recognizing the synergies and challenges offered by RESs and EVs becomes critical. The study begins by explaining the notion of demand response, emphasizing its importance in optimizing energy usage and grid stability. It then investigates the specific characteristics and possible benefits of incorporating RESs and EVs into DR schemes. This assessment evaluates the effectiveness of DR techniques in leveraging the variability of renewable energy generation and managing the charging patterns of electric vehicles. Furthermore, it outlines important technological, regulatory, and behavioral impediments to DR’s mainstream adoption alongside RESs and EVs. By synthesizing current research findings, this paper provides insights into opportunities for enhancing energy efficiency, lowering greenhouse gas emissions, and advancing sustainable energy systems through the coordinated implementation of demand response, renewable energy sources, and electric vehicles.

This paper describes the classification of four different varieties of rice grain based on four sets of features, namely morphology, colour, texture and wavelet. The classification is carried out on single rice kernel using image pre-processing steps followed by a cascade network classifier. The performance of the classifiers based on the above feature sets is also compared. It is found that morphological feature is more suitable for the classification of rice kernels, as compared to other features. The number of input features is reduced by a feature selection process using statistical analysis system (SAS) software. The classification accuracy based on selected features is compared with that of original features using different classifiers. It is found that the selected features are able to provide classification accuracy very close to the original features. The performance of the proposed cascade classifier is also tested against standard datasets from the University of California, Irvine (UCI), and the results are compared with other classifiers. The results show that the proposed classifier provides better classification accuracy as compared to other classifiers.

Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET‐A, SET‐B and SET‐C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co‐occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K‐nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET‐B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.

Rice holds its position as the most widely cultivated crop worldwide, its demand steadily rising alongside population growth. The precise identification of grains, especially wheat and rice, holds significant importance as their ultimate utilization depends on the quality of grains prior to processing. Traditionally, grain identification tasks have been predominantly manual, relying on experienced grain inspectors and consuming considerable time. However, this manual classification process is susceptible to variations influenced by individual perception, given the subjective nature of human image interpretation. Consequently, there is an urgent need for an automated recognition system capable of accurately identifying grains under diverse environmental conditions, necessitating the application of digital image processing techniques. In this study, we focus on grading five distinct varieties of rice based on their quality, employing a range of convolution neural networks (CNN) namely, Efficientnetb0, Googlenet, MobileNetV2, Resnet50, Resnet101, and ShuffleNet. The performance of CNN towards identification and grading of rice grain is also compared to that of other parametric and Non-parametric classifiers namely, Linear Discriminant Analysis (LDA), K-Nearest Neighbor (K-NN), Naive Bayes (NB), and Back Propagation Neural Network (BPNN) using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) based texture features. The image dataset comprises five grades of rice, each containing 100 images, resulting in a comprehensive collection of 500 samples for analysis. It is observed that, Convolution neural networks can grade five different qualities of rice with highest accuracy of 64.4% in case of GoogleNet. Results show that, rice grading using texture features performs better with highest accuracy of 99.2% (using GLCM) and 93.4% (using GLRLM).