A Neural Network-Based Approach to Multiple Wheat Disease Recognition

Abstract

In this paper, modern computer vision methods are proposed for detecting multiple diseases in wheat leaves. The authors demonstrate that modern neural network architectures are capable of qualitatively detecting and classifying diseases, such as yellow spots, yellow rust, and brown rust, even in cases in which multiple diseases are simultaneously present on the plant. For certain classes of diseases, the main multilabel metrics (accuracy, micro-/macro-precision, recall, and F1-score) range from 0.95 to 0.99. This indicates the possibility of recognizing several diseases on a leaf with an accuracy equal to that of an expert phytopathologist. The architecture of the neural network used in this case is lightweight, which makes it possible to use offline on mobile devices.