Analysis of Transfer and Residual Learning for Detecting Plant Diseases Using Images of Leaves

Abstract

The study of plant diseases is critical for alleviating the problem of food security all over the world. The most critical step in mitigating this problem is the correct and appropriate timely identification of the disease. The first step in identification of a disease is visual inspection. The massive scale of this problem and lack of professionals create a need for a automated accurate visual inspection technique. Recent advances in the field of computer vision, primarily through techniques such as use of convolutional neural networks and deep learning have generated impressive results in the field of image classification and object recognition. In this paper, we address the problem of detecting plant diseases using images of leaves using different state-of-the-art approaches. We use the Plant Village dataset comprising of 86,198 images of 25 crops across 57 classes (healthy and specific diseases). The images are of high quality and have been taken manually under appropriate lighting conditions. On this dataset, our model is able to attain a significantly high average accuracy of 99.374% using transfer learning on state-of-the-art models trained on the ILSVRC 2012 dataset having 1.2 million images across 1000 classes.