Disease Detection on Rice Leaves through Deep Learning with InceptionV3 Method

Abstract

The rate of growth in the agricultural sector in Indonesia puts pressure on people who work as farmers to maintain and improve the quality of agriculture. Rice, which is one of the basic needs of the community, is currently in high demand. Therefore, the need for rice continues to increase year by year with the increase in the population of Indonesia. To maintain the quality and quantity of rice, it is necessary to continuously monitor which for developing countries, there are limited tools and costs to develop technology to deal with problems of maintaining rice quality, especially diseases in rice. Rice disease is influenced by various factors, some of which are season, weather, temperature, media, availability of water sources, etc. The purpose of this research is to prevent diseases from spreading and spreading in rice by making disease detectors in rice using a deep learning approach using the InceptionV3 method. There are four classes of rice diseases diagnosed, namely bacterial blight, blast, brown spot, and tungro. The total loaded data set is 5932 images used in this study. The InceptionV3 model used can learn hidden patterns in the image thanks to CNN transfer learning method technology with an accuracy of 97.47%. The results show that InceptionV3 can be one of the choices of various existing CNN methods due to its accuracy.