Classification of Sri Lankan paddy varieties using deep learning techniques

Abstract

Rice is a highly consumed staple food in Sri Lanka. From farming phase to distribution phase of paddy, classification of paddy is becoming vital as it provides efficiency to the planning, production, sales and consumption. In Sri Lanka, the evaluation of the classification of paddy varieties is typically overseen by the Rice Research and Development Institute (RRDI). Traditionally, paddy identification is done manually by human inspectors, ensuring some level of accuracy but requiring significant manpower, time, and subjective judgment. This research seeks to transform the categorization of paddy varieties in Sri Lanka. This paper provides an approach to identifying and classifying paddy variety in paddy sample with the help of image processing and CNN model. For this approach, 10 varieties of paddy samples were collected from Rice Research and Development Institute. With these samples a dataset of more than 10,000 images were captured and used in this research. Image preprocessing involved cropping, scaling, and noise removal to standardize the data. Experiments were conducted with nine different CNN models, iterating through various architectures and training parameters to optimize performance. The experiment was performed on ten rice categories to evaluate the suggested solution. The accuracy of classification is of 93.69%.