An IoT environmental data collection system for fungal detection in crop fields
An IoT environmental data collection system for fungal detection in crop fields
复制标题
用于农田真菌检测的物联网环境数据采集系统
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
K. Wahid
中科院分区：
文献类型：
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作者：
Thomas Truong;A. Dinh;K. Wahid
需要一种系统，该系统提供农村作物田地中的实时本地环境数据，用于真菌疾病的检测和管理。本文介绍了一种物联网（IoT）系统的设计，该系统包括一个能够将实时环境数据发送到云存储的设备和一个机器学习算法，用于预测真菌检测和预防的环境条件。所存储的关于诸如空气温度、相对空气湿度、风速和降雨量等条件的环境数据由远程计算机访问和处理，以用于分析和管理目的。开发了一种使用支持向量机回归（SVMr）的机器学习算法来处理原始数据，并预测短期（每日）空气温度，相对空气湿度和风速值，以帮助预测有害真菌疾病在当地农田中的存在和传播。总之，这个物联网系统可以轻松访问环境数据和环境预测，最终将通过促进更好的管理和预防真菌疾病传播来帮助农田管理人员。
There is a need for a system which provides real-time local environmental data in rural crop fields for the detection and management of fungal diseases. This paper presents the design of an Internet of Things (IoT) system consisting of a device capable of sending real-time environmental data to cloud storage and a machine learning algorithm to predict environmental conditions for fungal detection and prevention. The stored environmental data on conditions such as air temperature, relative air humidity, wind speed, and rain fall is accessed and processed by a remote computer for analysis and management purposes. A machine learning algorithm using Support Vector Machine regression (SVMr) was developed to process the raw data and predict short-term (day-to-day) air temperature, relative air humidity, and wind speed values to assist in predicting the presence and spread of harmful fungal diseases through the local crop field. Together, the environmental data and environmental predictions made easily accessible by this IoT system will ultimately assist crop field managers by facilitating better management and prevention of fungal disease spread.