Evidence map›Paper›PMID 32962121›Full record

ArticleSensors (Basel, Switzerland)2020

Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management.

Chin-Ying Yang, Ming-Der Yang, Wei-Cheng Tseng, Yu-Chun Hsu, Guan-Sin Li, Ming-Hsin Lai, Dong-Hong Wu, Hsiu-Ying Lu

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.9field-weighted citation impact, top 7% 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

4 citing papers in PubMed, 38 citations in OpenAlex.

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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

8 authors at 1 institution in 1 country.

Chin-Ying YangDepartment of Agronomy, National Chung Hsing University, Taichung 40227, Taiwan.
Ming-Der YangDepartment of Civil Engineering, and Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 40227, Taiwan.ORCID 0000-0003-2904-5838
Wei-Cheng TsengDepartment of Civil Engineering, and Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 40227, Taiwan.
Yu-Chun HsuDepartment of Civil Engineering, and Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 40227, Taiwan.
Guan-Sin LiDepartment of Agronomy, National Chung Hsing University, Taichung 40227, Taiwan.
Ming-Hsin LaiCrop Science Division, Taiwan Agricultural Research Institute, Taichung 41362, Taiwan.
Dong-Hong WuDepartment of Agronomy, National Chung Hsing University, Taichung 40227, Taiwan.ORCID 0000-0001-9795-7172
Hsiu-Ying LuMiaoli District Agricultural Research and Extension Station, Miaoli 36346, Taiwan.
National Chung Hsing University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rice is one of the three major crops in the world and is the major crop in Asia. Climate change and water resource shortages may result in decreases in rice yields and possible food shortage crises. In this study, water-saving farming management was tested, and IOT field water level monitoring was used to regulate water inflow automatically. Plant height (PH) is an important phenotype to be used to determine difference in rice growth periods and yields using water-saving irrigation. An unmanned aerial vehicle (UAV) with an RGB camera captured sequential images of rice fields to estimate rice PH compared with PH measured on site for estimating rice growth stages. The test results, with two crop harvests in 2019, revealed that with adequate image calibration, the correlation coefficient between UAV-PH and field-PH was higher than 0.98, indicating that UAV images can accurately determine rice PH in the field and rice growth phase. The study demonstrated that water-saving farming is effective, decreasing water usage for the first and second crops of 2019 by 53.5% and 21.7%, respectively, without influencing the growth period and final yield. Coupled with an automated irrigation system, rice farming can be adaptive to water shortage situations.

Indexed as

Crops, AgriculturalOryzaAgricultural IrrigationAgricultureAsiaRemote Sensing Technologyimage processingirrigationplant heightUAV

Identifiers

PMID32962121
PMCPMC7571168
OpenAlexW3087510875

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.