Next Article in Journal
Temperature Mapping of 3D Printed Polymer Plates: Experimental and Numerical Study
Next Article in Special Issue
Assessing the Spectral Properties of Sunlit and Shaded Components in Rice Canopies with Near-Ground Imaging Spectroscopy Data
Previous Article in Journal
An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors
Previous Article in Special Issue
Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(3), 453; doi:10.3390/s17030453

eFarm: A Tool for Better Observing Agricultural Land Systems

Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 22 November 2016 / Revised: 14 February 2017 / Accepted: 16 February 2017 / Published: 24 February 2017
(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)
View Full-Text   |   Download PDF [11665 KB, uploaded 24 February 2017]   |  

Abstract

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies. View Full-Text
Keywords: smartphone; human sensing; social sensing; crowdsourcing; agriculture; land use; citizen science smartphone; human sensing; social sensing; crowdsourcing; agriculture; land use; citizen science
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yu, Q.; Shi, Y.; Tang, H.; Yang, P.; Xie, A.; Liu, B.; Wu, W. eFarm: A Tool for Better Observing Agricultural Land Systems. Sensors 2017, 17, 453.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top