  Recent introduction of ICT in agriculture has brought a number of changes in
the way farming is done. This means use of Internet of Things(IoT), Cloud
Computing(CC), Big Data (BD) and automation to gain better control over the
process of farming. As the use of these technologies in farms has grown
exponentially with massive data production, there is need to develop and use
state-of-the-art tools in order to gain more insight from the data within
reasonable time. In this paper, we present an initial understanding of
Convolutional Neural Network (CNN), the recent architectures of
state-of-the-art CNN and their underlying complexities. Then we propose a
classification taxonomy tailored for agricultural application of CNN. Finally,
we present a comprehensive review of research dedicated to applications of
state-of-the-art CNNs in agricultural production systems. Our contribution is
in two-fold. First, for end users of agricultural deep learning tools, our
benchmarking finding can serve as a guide to selecting appropriate architecture
to use. Second, for agricultural software developers of deep learning tools,
our in-depth analysis explains the state-of-the-art CNN complexities and points
out possible future directions to further optimize the running performance.
