用于基于IoT的农场的现代CNN

摘要：

最近在农业中采用信息和通信技术，给耕作方式带来了一些变化。这意味着使用物联网（IoT）、云计算（CC）、大数据（BD）和自动化来更好地控制农业过程。由于这些技术在农场中的使用随着大量数据的产生而呈指数级增长，因此需要开发和使用最先进的工具，以便在合理的时间内从数据中获得更多的洞察力。在本文中，我们提出了卷积神经网络（CNN）的初步理解，最先进的CNN的最新架构及其潜在的复杂性。然后，我们提出了一个分类分类法适合农业应用的CNN。最后，我们提出了一个全面的审查研究致力于国家的最先进的CNN在农业生产系统中的应用。我们的贡献是双重的。首先，对于农业深度学习工具的最终用户，我们的基准测试发现可以作为选择适当架构的指南。其次，对于深度学习工具的农业软件开发者，我们的深入分析解释了最先进的CNN复杂性，并指出了进一步优化运行性能的未来可能方向。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。