Abstract
The use of sensors and the Internet of Things (IoT) is key to moving the world’s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system.
Highlights
To cite this article: Yemeserach Mekonnen et al 2020 J
The use of wireless sensor networks, IoT, robotics, drones, and Artificial intelligence (AI) is on the upswing
Machine learning algorithms enable the extraction of useful information and insights from the deluge of data
Summary
Yield prediction is a vital feature of precision agriculture that utilizes farmland and weather data to help farmers increase crop production. A distributed WSN developed using open-source hardware platforms, Arduino based micro-controller, and ZigBee[55] module to monitor and control parameters critical to crop growth such as soil conditions, environmental and weather conditions is further discussed This experimental testbed, as detailed in Ref. 6, is an offgrid photo-voltaic (PV) supported small-sized smart farm experimental test-bed, which captures energy and water data as well. The sensor board can be interfaced with various sensors to measure soil moisture content, pH level, soil temperature, leaf wetness, ambient temperature, solar radiation, atmospheric pressure, humidity, and weather parameters, including wind direction, precipitation, and wind speed It uses the ZigBee protocol with XBee PRO S2 2.4 GHz to transmit sensor data to the gateway and to communicate among other nodes. Critical information about the concurrently used to predict the crop to be field is sent to farmer’s mobile using GSM grown
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