Raw data collected from NO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:msub><mml:mrow/><mml:mn>2</mml:mn></mml:msub></mml:math>, O<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"><mml:msub><mml:mrow/><mml:mn>3</mml:mn></mml:msub></mml:math> and NO air pollution electrochemical low-cost sensors

Ferrer-Cid, Pau; Barceló-Ordinas, José M.; García-Vidal, Jorge

doi:10.1016/j.dib.2022.108586

Cited by 1 publication

(1 citation statement)

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“…If the goal is to increase yield, then collecting a large amount of data may be necessary to identify the factors that are most important for achieving this goal. The authors of [119] examine in their article the use of affordable air pollution sensors for air quality monitoring and how machine learning techniques can increase the accuracy of the sensor data. The study shows data from two low-cost, self-built air pollution nodes that were installed at a Barcelona official reference station for four months and collected high-resolution data from five electrochemical sensors as well as temperature and humidity measurements.…”

Section: Amount Of Needed Data For Precision Crop Productionmentioning

confidence: 99%

Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review

Alahmad,

Neményi,

Nyéki

2023

Agronomy

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The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production

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Section: Amount Of Needed Data For Precision Crop Productionmentioning

confidence: 99%