Cell Phone Image-Based Plant Disease Classification

Abstract

Modern communication and sensor technology coupled with powerful pattern recognition algorithms for information extraction and classification allow the development and use of integrated systems to tackle environmental problems. This integration is particularly promising for applications in crop farming, where such systems can help to control growth and improve yields while harmful environmental impacts are minimized. Thus, the vision of sustainable agriculture for anybody, anytime, and anywhere in the world can be put into reach. This chapter reviews and presents approaches to plant disease classification based on cell phone images, a novel way to supply farmers with personalized information and processing recommendations in real time. Several statistical image features and a novel scheme of measuring local textures of leaf spots are introduced. The classification of disease symptoms caused by various fungi or bacteria are evaluated for two important agricultural crop varieties, wheat and sugar beet.

High-throughput and point-of-care detection of wheat fungal diseases: Potentialities of molecular and phenomics techniques toward in-field applicability

“…RGB imaging mimics human perception to provide useful data for plant phenotyping applications, such as breeding for interesting plant traits or detection of different stresses. For instance, RGB coupled with machine learning for data analysis has been used to successfully detect plant diseases (Camargo and Smith, 2009;Neumann et al, 2014). The advantage of RGB sensors resides in cost accessibility and ease of operation and maintenance.…”