Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks
This addresses the problem of crop loss for Filipino rice farmers by providing a tool for disease detection, but it is incremental as it applies existing methods to a specific agricultural context.
The researchers developed a mobile application prototype to detect rice leaf diseases, specifically bacterial leaf blight, using convolutional neural networks trained on a public dataset, achieving improved accuracy through image augmentation.
Rice is the number one staple food in the country, as this serves as the primary livelihood for thousands of Filipino households. However, as the tradition continues, farmers are not familiar with the different types of rice leaf diseases that might compromise the entire rice crop. The need to address the common bacterial leaf blight in rice is a serious disease that can lead to reduced yields and even crop loss of up to 75%. This paper is a design and development of a rice leaf disease detection mobile application prototype using an algorithm used for image analysis. The researchers also used the Rice Disease Image Dataset by Huy Minh Do available at https://www.kaggle.com/ to train state-of-the-art convolutional neural networks using transfer learning. Moreover, we used image augmentation to increase the number of image samples and the accuracy of the neural networks as well