A Study on Deep Learning in Agriculture

Abstract

We have seen in recent years an amazing improvement in applications using Deep learning. It started with speech recognition then moved on to computer vision, object recognition and natural language processing. Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. Deep learning are machine learning algorithms based on learning multiple level of abstraction. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we explored the platforms that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our research findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.