As one of the top ten rice producing and consuming countries in the world, Bangladesh is heavily dependent on rice for its economy and to meet its food needs. To ensure healthy and good growth of the rice plants, it is essential to detect any diseases early and before applying the required treatment to the affected plants. Since manual detection of diseases takes a lot of time and labor, it is inevitably wise to have an automated system. Since the past 10 years, the diagnosis of plant diseases in the approach of image processing techniques has remained of great interest among the researchers. A number of methods for the detection, identification and quantification of diseases have been developed and applied in a wide variety of crops. This paper presents a rice leaf disease detection system using machine learning approaches. In this work, three of the most common diseases in rice plants are detected, namely leaf spot, bacterial leaf blight and brown spot diseases. Clear images of infested rice leaves with white background were used as input. The main idea of this work is to create a rice leaf disease detection model using machine learning algorithms that can be useful for disease recognition. The data for this task is collected from the UCI Machine Learning Repository. After the necessary preprocessing, the dataset was trained with a range of different machine learning algorithms, such as random forest and CNN.

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