Big data in precision agriculture: Weather forecasting for future farming

Bendre, Mininath R.; Thool, Ravindra C.; Thool, V. R.

doi:10.1109/ngct.2015.7375220

Cited by 134 publications

(50 citation statements)

References 11 publications

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“…Therefore, it is necessary to develop big data analytics in precision agriculture [156]. It is also essential to analyze the reduction in input cost in water resources, energy consumption and labor cost by adopting precision agriculture practices [73]. Other examples of the big data analytics in precision agriculture are factors affecting crop yield; and demarcation of field zones based on particular application such as productivity, soil moisture, nutrients, harvesting.…”

Section: Cloud and Big Data In Precision Agriculturementioning

confidence: 99%

Internet of underground things in precision agriculture: Architecture and technology aspects

et al. 2018

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The projected increases in World population and need for food have recently motivated adoption of information technology solutions in crop fields within precision agriculture approaches. Internet Of Underground Things (IOUT), which consists of sensors and communication devices, partly or completely buried underground for real-time soil sensing and monitoring, emerge from this need. This new paradigm facilitates seamless integration of underground sensors, machinery, and irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. In this paper, state-of-the-art communication architectures are reviewed, and underlying sensing technology and communication mechanisms for IOUT are presented. Moreover, recent advances in the theory and applications of wireless underground communication are also reported. Finally, major challenges in IOUT design and implementation are identified.

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Section: Cloud and Big Data In Precision Agriculturementioning

confidence: 99%

Internet of underground things in precision agriculture: Architecture and technology aspects

et al. 2018

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“…PA generates structured and unstructured datasets, which needs the extraction of knowledgeable information. Paper [41], uses the big data approach to get insights and explore the potential sources of big data in PA. These big data analytics helps in figuring out the trend and pattern which helps in decision making, future farming etc.…”

Section: Related Workmentioning

confidence: 99%

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Singh

Aernouts

Meyer

et al. 2020

Sensors

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The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design.

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“…The current ratio of agricultural lands respecting to the world population is enough to satisfy the world's nourishment needs; however, the global population is expected to grow exponentially in the coming years, where the total area of agricultural land shrinks day by day [1]. This cause, in addition to other ones such as pollution, misuse of resources, leads agricultural production techniques in different countries to be updated to increase production and generate a secure food supply [6], For them it is essential to collect data from various sources of pA to make predictive decisions due to the increase in production and agricultural management depends, in most cases, the climatic conditions [7].…”

Section: Precision Agriculture (Pa)mentioning

confidence: 99%

“…The planning for agricultural adaptation and mitigation must rely on informed processes of decision-taking [2] [15]. Typically, the data set of precision agriculture refers to the following types: [7] [16] • Historical data.…”

Section: Precision Agriculture (Pa)mentioning

confidence: 99%