Special Issue "Smart Farming: Monitoring Sensor Data"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 4805

Special Issue Editors

Advanced Information Systems Laboratory, Aragón Institute of Engineering Research, University of Zaragoza, María de Luna 1, 50018 Zaragoza, Spain
Interests: geographic-based data knoledge; data analysis; innovation in agriculture
Special Issues, Collections and Topics in MDPI journals
Institute of New Imaging Technologies (INIT), Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain
Interests: geospatial technologies; interoperability; sensor web; internet of things; real-time analysis; computational thinking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade, a new movement to implant digital technology into agriculture was born, which is known as precision agriculture. It aims to optimize the yield per unit of farming land by using ICT tools and technologies. The objective of precision agriculture is to achieve the best products concerning quality, quantity and economic conditions. Traditionally, precision agriculture makes use of sensors to monitor environmental conditions. To attain this objective, networks of these sensors are created to cover larger areas. Precision agriculture is not only attached to deploying on-site sensors but involving many areas related to robotics, computer science, and remote sensing. An example of this is the use of smartphones, which have been used to visualize on the field the data provided by sensors and offer the possibility to apply different strategies to improve productivity

This Special Issue will collect contributions on new ICT approaches in the area of precision agriculture (or smart farming) including wireless sensor networks, Internet of Things, smartphones, big data; information infrastructures, open data, location base services, agriculture knowledge models and decision support systems, sensors for agriculture, and geostatistical analysis.

Prof. Francisco Javier Zarazaga-Soria
Dr. Sergio Trilles Oliver
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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A New Crop Spectral Signatures Database Interactive Tool (CSSIT)
Data 2019, 4(2), 77; https://doi.org/10.3390/data4020077 - 24 May 2019
Cited by 6 | Viewed by 4387
In many countries, commodities provided by the agriculture sector play an important role in the economy. Securing food is one aspect of this role, which can be achieved when the decision makers are supported by tools. The need for cheap, fast, and accurate [...] Read more.
In many countries, commodities provided by the agriculture sector play an important role in the economy. Securing food is one aspect of this role, which can be achieved when the decision makers are supported by tools. The need for cheap, fast, and accurate tools with high temporal resolution and global coverage has encouraged the decision makers to use remote sensing technologies. Field spectroradiometer with high spectral resolution can substantially improve crop mapping by reducing similarities between different crop types that have similar ecological conditions. This is done by recording fine details of the crop interaction with sunlight. These details can increase the same crop recognition even with the variation in the crop chemistry and structure. This paper presents a new spectral signatures database interactive tool (CSSIT) for the major crops in the Eastern Mediterranean Basin such as wheat and potato. The CSSIT’s database combines different data such as spectral signatures for different periods of crop growth stages and many physical and chemical parameters for crops such as leaf area index (LAI) and chlorophyll-a content (CHC). In addition, the CSSIT includes functions for calculating indices from spectral signatures for a specific crop and user interactive dialog boxes for displaying spectral signatures of a specific crop at a specific period of time. Full article
(This article belongs to the Special Issue Smart Farming: Monitoring Sensor Data)
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