Abstract:
A large portion of agricultural crop yield is lost due to plant diseases. The impact of this is more severe in developing countries that do not have sufficient trained professionals to identify and treat diseases. Deep learning has shown promising results in the field of image classification and is adopted in fields such as medicine. However, its' adoption in the field of agriculture has been slow in comparison. There are many examples in literature that had trained deep learning models to detect plant diseases by images. However, there is still no successful application developed that works in the real world. In this paper, the authors review the research efforts that have been done in the area of image-based plant disease detection with deep learning and try to analyze the challenges faced in adopting it in the agricultural sector. The authors examine datasets used, image pre-processing conducted and deep learning technologies utilized.
