Evidence map›Paper›PMID 36616618›Full record

ArticleSensors (Basel, Switzerland)2022

Detection of Water Changes in Plant Stems In Situ by the Primary Echo of Ultrasound RF with an Improved AIC Algorithm.

Danju Lv, Jiali Zi, Mingyuan Gao, Rui Xi, Xin Huang

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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
0.3field-weighted citation impact, top 35% 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, 2 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 1 institution in 1 country.

Danju LvCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
Jiali ZiCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
Mingyuan GaoCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
Rui XiCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
Xin HuangCollege of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
Southwest Forestry University · CN

Funding

Education Foundation of Yunnan Province No.2022J0495Kunming Forestry Information Engineering Technology Research Center No.2015FIA02Major scientific and technological projects in Yunnan Province No. 202002AA10007Major Special Projects in Yunnan Province No.202002AD080002National Natural Science Foundation of China No. 31860332
6 · The paper itself

Abstract

The detection of water changes in plant stems by non-destructive online methods has become a hot spot in studying the physiological activity of plant water. In this paper, the ultrasonic radio-frequency echo (RFID) technique was used to detect water changes in stems. An algorithm (improved hybrid differential Akaike's Information Criterion (AIC)) was proposed to automatically compute the position of the primary ultrasonic echo of stems, which is the key parameter of water changes in stems. This method overcame the inaccurate location of the primary echo, which was caused by the anisotropic ultrasound propagation and heterogeneous stems. First of all, the improved algorithm was analyzed and its accuracy was verified by a set of simulated signals. Then, a set of cutting samples from stems were taken for ultrasonic detection in the process of water absorption. The correlation between the moisture content of stems and ultrasonic velocities was computed with the algorithm. It was found that the average correlation coefficient of the two parameters reached about 0.98. Finally, living sunflowers with different soil moistures were subjected to ultrasonic detection from 9:00 to 18:00 in situ. The results showed that the soil moisture and the primary ultrasonic echo position had a positive correlation, especially from 12:00 to 18:00; the average coefficient was 0.92. Meanwhile, our results showed that the ultrasonic detection of sunflower stems with different soil moistures was significantly distinct. Therefore, the improved AIC algorithm provided a method to effectively compute the primary echo position of limbs to help detect water changes in stems in situ.

Indexed as

SoilWaterAlgorithmsPlant StemsUltrasonographySoilWaterAICnon-destructive detectionRFIDultrasonicwater changes in plant stem

Identifiers

PMID36616618
PMCPMC9824858
OpenAlexW4313466144

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.