From wireless sensors to field mapping: Anatomy of an application for precision agriculture

Camilli, Alberto; Cugnasca, Carlos Eduardo; Saraiva, Antônio Mauro; Hirakawa, André Riyuiti; Corrêa, Pedro Luiz Pizzigatti

doi:10.1016/j.compag.2007.01.019

Cited by 131 publications

(55 citation statements)

References 6 publications

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“…Krige [8] in the 1950s for the exploration of mineral resources with 1000 or more test drillings. To date, the Kriging method has only been scarcely utilized in sensor networks; see [9] for example. This might be due to the fact that, in general, there are a lower number of probe points in wireless sensor applications than in geological research.…”

Section: Krigingmentioning

confidence: 99%

“…b) The destination values were set according to the trend model in equation (9). The results of the comparison are summarized in Table 1 Ordinary Kriging gave the most accurate prediction with an interpolation error between 0.5 K and 2.2 K. The combination of Kriging with a linear trend model resulted in a slightly higher error.…”

Section: Comparison Of Interpolation Errorsmentioning

confidence: 99%

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The Minimum Number of Sensors – Interpolation of Spatial Temperature Profiles in Chilled Transports

Jedermann¹,

Lang²

2009

Lecture Notes in Computer Science

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Abstract. Wireless sensor networks are an important tool for the supervision of cool chains. Previous research with a high number of measurement points revealed spatial temperature deviations of more than 5 °C in chilled transport, but the number of sensors has to be reduced to an economically useful value for use in regular transport. This paper presents a method to estimate the minimum number of sensors and to compare different sensor positioning strategies. Different methods of interpolating the temperature data of intermediate positions were applied to the experimental data from a delivery truck. The average prediction error for intermediate points was estimated as a function of the number of sensors. The Kriging method, originally developed for the interpolation of geostatistical data, produced the best results.

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Section: Krigingmentioning

confidence: 99%

Section: Comparison Of Interpolation Errorsmentioning

confidence: 99%

The Minimum Number of Sensors – Interpolation of Spatial Temperature Profiles in Chilled Transports

Jedermann¹,

Lang²

2009

Lecture Notes in Computer Science

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“…Scalability, or the ability of a sensor network system to handle a large number of nodes, is critical for intensive data acquisition or achieving site-specific irrigation at the field scale level (Camilli et al, 2007). Sensor systems can be comprised of a number of different sensing devices; scalability is important for achieving multi-data collection (Lee et al, 2010).…”

mentioning

confidence: 99%

Wireless Sensor Network Effectively Controls Center Pivot Irrigation of Sorghum

O’Shaughnessy¹,

Evett²,

Colaizzi³

et al. 2013

Appl. Eng. Agric.

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“…In order to improve agriculture processes, we can acquire field data with sensors, make data analytics, perform analysis and take appropriate decisions and actions. Collecting big data from the field gives us a clearer understanding of product variability and quality of products [2].…”

Section: Introductionmentioning

confidence: 99%

Internet of Plants Application for Smart Agriculture

Aliev¹,

Moazzam²,

Narejo³

et al. 2018

ijacsa

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Abstract-Nowadays, Internet of Things (IoT) is receiving a great attention due to its potential strength and ability to be integrated into any complex system. The IoT provides the acquired data from the environment to the Internet through the service providers. This further helps users to view the numerical or plotted data. In addition, it also allows objects which are located in long distances to be sensed and controlled remotely through embedded devices which are important in agriculture domain. Developing such a system for the IoT is a very complex task due to the diverse variety of devices, link layer technologies, and services. This paper proposes a practical approach to acquiring data of temperature, humidity and soil moisture of plants. In order to accomplish this, we developed a prototype device and an android application which acquires physical data and sends it to cloud. Moreover, in the subsequent part of current research work, we have focused towards a temperature forecasting application. Forecasting metrological parameters have a profound influence on crop growth, development and yields of agriculture. In response to this fact, an application is developed for 10 days ahead maximum and minimum temperatures forecasting using a type of recurrent neural network.

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From wireless sensors to field mapping: Anatomy of an application for precision agriculture

Cited by 131 publications

References 6 publications

The Minimum Number of Sensors – Interpolation of Spatial Temperature Profiles in Chilled Transports

The Minimum Number of Sensors – Interpolation of Spatial Temperature Profiles in Chilled Transports

Wireless Sensor Network Effectively Controls Center Pivot Irrigation of Sorghum

Internet of Plants Application for Smart Agriculture

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