大数据深度学习系统研究进展与典型农业应用
收稿日期: 2019-04-10
网络出版日期: 2019-08-21
基金资助
国家重点研发计划“云计算和大数据"重点专项"数据驱动的云数据中心智能管理技术与平台(共性关键技术类)”(018YFB1003700)
A Survey of Big Data Deep Learning Systems and a Typical Agricultural Application
Received date: 2019-04-10
Online published: 2019-08-21
随着信息时代的快速发展,大数据成为推动人们生产和生活发生重大变革的关键技术,对于包括农业在内的各大领域的发展都起着非常重要的作用。而要想对大数据进行有效的分析和利用,并使其发挥最大价值,深度学习技术的研究和发展起着决定性的影响。在此背景下,本文对大数据深度学习系统领域的主要技术特征及其发展情况进行了详细介绍,包括深度学习模型（如CNN模型和RNN模型）、优化算法、大数据学习框架、硬件配置等方面。本文还对包括PyTorch在内的五种主流的深度学习框架的技术特征和发展历程分别进行了讲解,并对比了不同框架的长处和缺点。此外,本文还提到了大数据深度学习系统在农业领域的典型应用"基于大数据的葡萄叶片霜霉病预报系统",并以其关键步骤"葡萄叶片种类的分类识别过程"为例详细介绍了工作的原理,包括数据收集、样本特征提取、聚类算法、分类算法以及结果分析等过程。该系统运用大数据和深度学习技术,在检测和预防葡萄叶片霜霉病方面有着显著的效果。最后,本文还针对大数据深度学习系统目前的主要发展趋势,以及在农业领域的研究应用中所需注意的问题进行了介绍。到今天,大数据深度学习系统在包括农作物病虫害预测在内的农业数据分析领域发挥着日益重要的作用,并获得了广泛的应用。
张凌栩,韩锐,李文明,史银雪,刘驰 . 大数据深度学习系统研究进展与典型农业应用[J]. 农业大数据学报, 2019 , 1(2) : 88 -104 . DOI: 10.19788/j.issn.2096-6369.190208
With the rapid development of information age, big data has become the key technology to promote people's production and daily life to undergo major changes, and plays a very important part in the development of various fields, including agriculture. In order to effectively analyze and utilize the big data and make it play its maximum value, the research and development of deep learning technology plays a decisive role. In this context, this paper gives a detailed introduction to the main technical characteristics and development of big data deep learning system, including deep learning model (such as CNN model and RNN model), optimization algorithm, big data learning framework, hardware configuration and so on. This paper also explains the technical characteristics and development process of five mainstream deep learning frameworks, including PyTorch, and compares the strengths and weaknesses of these frameworks. In addition, this paper also mentions the typical application of big data deep learning system in agriculture, "Grape Leaf Downy Mildew Forecasting System Based on Big Data", and takes its key step "Grape Leaf Classification and Recognition Process" as an example to introduce its working principle in detail, including data collection, sample feature extraction, clustering algorithms, classification algorithms and result analysis. This system uses big data and deep learning technology to help detect and prevent downy mildew of grape leaves. Finally, this paper introduces the main development trend of big data deep learning system, as well as the problems requiring attention in agricultural research and application. Today, big data deep learning system is playing an increasingly important role and has been widely used in the field of agricultural data analysis, including crop pest prediction.
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