Use of Big Data for Assessment of Environmental Pressures from Agricultural Production

Wąs, Adam; Sulewski, Piotr; Majewski, Edward; Kobus, Paweł

doi:10.1201/9781003057291-12

Cited by 1 publication

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“…GIS has the potential to use plants [15]. GIS assists farmers in agriculture in increasing productivity and lowering costs by having better land resource management [16]. GIS encourages farmers in agriculture to boost productivity and cut costs by allowing enhanced land resource management [17].…”

Section: Introduction Of Smart Farming Using Iot and Big Datamentioning

confidence: 99%

Unmanned Aerial Vehicle and Geospatial Analysis in Smart Irrigation and Crop Monitoring on IoT Platform

Zhao

Wang

Pham

2023

Mobile Information Systems

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The geospatial analysis provides high potential for modeling, understanding, and visualizing artificial and natural ecosystems, utilizing big data analytics and the Internet of things as a pervasive sensing infrastructure. Precision agriculture, weed control, fertilizer distribution, and field management benefit from unmanned ariel vehicles (UAVs). Reduced production costs and improved crop quality are some of the benefits of using this method. Smart farming denotes geographical data utilization to identify field variability, guarantee optimal inputs, and enhance a farm’s output. Hence, in this paper, an IoT-assisted Smart Farming Framework (IoT-SFF) with big data analytics has been proposed using geospatial analysis. The use of wireless sensors in IoT devices and communication methods in agricultural applications is thoroughly examined. IoT sensors are available for particular agriculture applications, such as crop status, soil preparation, insect, pest detection, and irrigation scheduled. It is now possible to view our regions in various ways and make accurate agrotechnological decisions, thanks to a computer-generated geographic information system (GIS) for crop irrigation and monitoring. Analytical and monitoring processes that yield timely and accurate decision-making add value to big data, which is a key component for intelligently managing and operating farms. Still, it is constrained by both technical and socioeconomic variables. The simulation findings show that the proposed IoT-SFF model improves the crop yield ratio by 92.4%, prediction ratio by 97.7%, accuracy ratio by 94.5%, the average error by 38.3%, and low-cost rate by 34.4%.

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Section: Introduction Of Smart Farming Using Iot and Big Datamentioning

confidence: 99%