A lightweight mobile system for crop disease diagnosis

Abstract

This paper presents a low-complexity mobile application for automatically diagnosing crop diseases in the field. In an initial pre-processing stage, the system leverages the capability of a smartphone device and basic image processing algorithms to obtain consistent leaf orientation and to remove the background. A number of different features are then extracted from the leaf, including texture, colour and shape features. Nine lightweight sub-features are combined and implemented as a feature descriptor for this mobile environment. The system is applied to six wheat leaf types: non-disease, yellow rust, Septoria, brown rust, powdery mildew and tan spots, which are commonly occurring wheat diseases worldwide. The standalone application demonstrates the possibilities for disease diagnosis under realistic circumstances, with disease/non-disease detection accuracy of approximately 88 %, and can provide a possible disease type within a few seconds of image acquisition.