  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
