Deep Learning for Soybean Monitoring and Management

Abstract

Artificial intelligence is more present than ever in virtually all sectors of society. This is in large part due to the development of increasingly powerful deep learning models capable of tackling classification problems that were previously untreatable. As a result, there has been a proliferation of scientific articles applying deep learning to a plethora of different problems. The interest in deep learning in agriculture has been continuously growing since the inception of this type of technique in the early 2010s. Soybeans, being one of the most important agricultural commodities, has frequently been the target of efforts in this regard. In this context, it can be challenging to keep track of a constantly evolving state of the art. This review characterizes the current state of the art of deep learning applied to soybean crops, detailing the main advancements achieved so far and, more importantly, providing an in-depth analysis of the main challenges and research gaps that still remain. The ultimate goal is to facilitate the leap from academic research to technologies that actually work under the difficult conditions found in the the field.