A Novel Design of Rice Leaf Disease Detection Model Using Machine Learning (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.
Keywords: Rice; Photography; CNN; detection; Quality; Image data