# Modern CNNs for IoT Based Farms

## TL;DR

This paper reviews the use of modern CNN architectures in IoT-based farming, providing a classification framework, benchmarking insights, and guidance for selecting and optimizing CNN models for agricultural applications.

## Contribution

It offers a comprehensive review and taxonomy of CNN architectures tailored for agriculture, aiding end users and developers in model selection and optimization.

## Key findings

- Benchmarking guides for CNN architecture selection
- Analysis of CNN complexities in agricultural applications
- Future directions for optimizing CNN performance

## Abstract

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.