Recent Advances on UAV and Deep Learning for Early Crop Diseases Identification: A Short Review

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Abstract
The different crop diseases are a serious threat resulting in significant yield losses, where their effective monitoring and accurate early identification techniques are considered crucial to ensure stable and reliable crop productivity and food security. The traditional methods often rely on human expert-based inspection of disease symptoms, which could be effective for small crop fields. However, they require a very long time and great physical effort to cover large crops resulting in very high miss detection rates. Recent innovative advances in remote sensing technologies and computer vision techniques are considered an effective way to solve such problems. To this end, in this paper, we focus on the recent advances in Unmanned Aerial Vehicle platforms and deep learning-based computer vision algorithms to identify crop diseases at their early stage to improve food production.

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