Digital Crop Health Monitoring by Analyzing Social Media Streams

Priyamvada Shankar, Christian Bitter, M. Liwicki
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引用次数: 1

Abstract

This paper introduces the idea of using social media streams like Twitter to identify occurrences of crop diseases. Climate change and changes in agriculture practices have contributed to a change in crop disease dynamics leading to an increase in crop damages. Monitoring crop disease occurrences across regions is helpful for farmers to prepare for such adverse situations and make effective use of crop protection products thus ensuring enough produce for the growing population and protection of the environment. We investigate Machine Learning and Natural Language Processing techniques in order to spot agricultural discussions on Twitter; then analyze, categorize, and group them; so they can be used by a stakeholder to identify crop disease incidences, patterns, and trends at the regional scale. Current systems using keyword based search of agricultural diseases do not always yield agriculturally relevant tweets and those that do could talk on a range of sub-topics. Therefore, text classification forms the core component of this work. A two fold classification process is employed, classifying agriculturally relevant tweets from the rest and then performing fine-grained categorization on them. The resulting model for agricultural tweets classification performs with 93% accuracy and the fine grained categorization model that categorizes tweets into 6 categories gives 75% accuracy. A prototype of an interactive web based disease monitoring application is also presented. The location estimation is not always accurate but nonetheless, this work acts as a proof of concept for the introduction of social media as a novel data source in precision farming.
通过分析社交媒体流进行数字作物健康监测
本文介绍了使用Twitter等社交媒体流来识别作物病害发生的想法。气候变化和农业实践的变化促成了作物病害动态的变化,导致作物损失增加。监测跨区域的作物病害情况有助于农民为这种不利情况做好准备,并有效利用作物保护产品,从而确保为不断增长的人口提供足够的产品并保护环境。我们研究了机器学习和自然语言处理技术,以便在Twitter上发现农业讨论;然后对它们进行分析、分类和分组;因此,利益相关者可以使用它们来确定区域范围内的作物病害发病率、模式和趋势。目前使用基于关键字的农业疾病搜索的系统并不总是产生与农业相关的tweet,而那些能够产生相关tweet的系统可能会讨论一系列子主题。因此,文本分类是这项工作的核心组成部分。采用了双重分类过程,将与农业相关的推文与其他推文进行分类,然后对它们进行细粒度分类。所得的农业推文分类模型的准确率为93%,细粒度分类模型将推文分为6类,准确率为75%。提出了一种基于交互式web的疾病监测应用的原型。位置估计并不总是准确的,但尽管如此,这项工作证明了将社交媒体作为一种新型数据源引入精准农业的概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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