(untitled) (International Journal of Innovative Research in Engineering & Management, 2021)
Rice has a high nutritional value since it includes several vital elements. It is one of the most widely consumed foods. However, due to the great diversity of rice, judging its quality is difficult. The use of photography and machine learning in this work resulted in a unique method for determining the quality of rice without causing any damage or loss. First, a DSLR camera was used to capture pictures of the Rice leaf samples. The data was separated into healthy. and sick groups and sorted. The CNN was then used to discriminate between different types of rice leaf diseases. Various parameters have been used to train several models. Here we use graphical user interface(GUI) as an interface software. Five classes of leaves ie. Healthy, Bacterial leaf blight, Brown spot, leaf smut, and leaf blast were taken into account, and all the leaves where classified into one of these classes. Finally, the models were evaluated based on the outcomes of the experiments. The finest performance was noticed by CNN. CNN’s classification and average cost time accuracy were 95 percent and 0.01 seconds, respectively. Overall, the results demonstrate that picture data generated by the system may be utilized to assess rice quality quickly, accurately, and safely.