Evidence map›Paper›PMID 31936040›Full record

ArticleSensors (Basel, Switzerland)2020

Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality.

Feiyang Zhang, Guangxing Wang, Yueming Hu, Liancheng Chen, A-Xing Zhu

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
3.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Feiyang ZhangCollege of Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
Guangxing WangDepartment of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA.
Yueming HuCollege of Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
Liancheng ChenGuangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China.
A-Xing ZhuDepartment of Geography, University of Wisconsin Madison, Madison, WI 53706, USA.
South China Agricultural University · CNSouthern Illinois University Carbondale · USUniversity of Wisconsin–Madison · US

Funding

National Key Research and Development Project of China 2016YFD0800301National Key Research and Development Project of China 2018YFD1100801-01Science and Technology Planning Project of Guangdong Province 2017B030314155Science and Technology Program of Guangzhou, China 201804020034Science and Technology Program of Guangzhou, China 201807010048
6 · The paper itself

Abstract

Quality monitoring is important for farmland protection. Here, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data, obtained by wireless sensor networks (WSNs), are uniquely suited for assessing spatial and temporal changes in farmland quality. However, existing UAV-WSN systems are unable to fully integrate the data obtained from these two monitoring systems. This work addresses this problem by designing an improved UAV-WSN monitoring system that can collect both high-resolution UAV images and long-term WSN data during a single-flight mission. This is facilitated by a newly proposed data transmission optimization routing protocol (DTORP) that selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted, additionally combining individual scheduling algorithms and routing algorithms appropriate for three different distance scales to reduce the energy consumption incurred during data transmission between the nodes in a cluster. The performance of the proposed system is evaluated based on Monte Carlo simulations by comparisons with that obtained by a conventional system using the low-energy adaptive clustering hierarchy (LEACH) protocol. The results demonstrate that the proposed system provides a greater total volume of transmitted data, greater energy utilization efficiency, and a larger maximum revisit period than the conventional system. This implies that the proposed UAV-WSN monitoring system offers better overall performance and enhanced potential for conducting long-term farmland quality data collection over large areas in comparison to existing systems.

Indexed as

integrated designscheduling algorithmsunmanned aerial vehicleswireless sensor network

Identifiers

PMID31936040
PMCPMC7014168
OpenAlexW2998914815

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.