The different crop diseases are a serious threatresulting in significant yield losses, where their effective monitoring and accurate early identification techniques are consideredcrucial to ensure stable and reliable crop productivity andfood security. The traditional methods often rely on humanexpert-based inspection of disease symptoms, which could beeffective for small crop fields. However, they require a very longtime and great physical effort to cover large crops resultingin very high miss detection rates. Recent innovative advancesin remote sensing technologies and computer vision techniquesare considered an effective way to solve such problems. Tothis end, in this paper, we focus on the recent advances inUnmanned Aerial Vehicle platforms and deep learning basedcomputer vision algorithms to identify crop diseases at their earlystage to improve food production.
