APPLYING VGG-16 FOR CLASSIFICATION OF RICE VARIETIES INCORPORATING COLOR AND TEXTURE FEATURES

Le résumé fourni par la source

Ensuring rice variety is crucial for meeting client expectations and upholding high-quality standards. However, developing cost-effective and speedy techniques for evaluating rice quality remains a challenge. According to the variation in demand, rice is available in different grades at different prices and rendering to consumer preferences. Deep learning has promising results in various fields of life as their applications are in agriculture. This present study utilized a deep learning based convolutional neural network (CNN) model VGG-16 to identify and classify rice varieties accurately. The applied model is customized, and seven fully connected layers (FCLs) are added on pre-trained VGG-16 as the number of rice classes is seven namely Kachi, Kachi kainat, Seela, Sufaid, super, Ari, and 1508. The most popular libraries namely TensorFlow and Keras are used to develop the proposed neural network model. The steps of this study are image data acquisition using the camera, pre-processing, using the Keras image data generator for transforming data, defining layers, model compilation, training and validation of the model, model optimization and testing on new instances for reliability and accuracy check. The methodology has been evaluated in terms of loss, accuracy, and time duration for better results. The applied algorithm gained 97% accuracy on the real rice image dataset during the training phase.

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