Evidence map›Paper›PMID 36040983›Full record

ArticlePloS one2022

From crop specific to variety specific in crop modeling for the smart farm: A case study with blueberry.

Gyung Deok Han, Jeong Min Choi, Inchan Choi, Yoonha Kim, Seong Heo, Yong Suk Chung

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Gyung Deok HanDepartment of Practical Course Education, Cheongju National University of Education, Cheongju, Republic of Korea.
Jeong Min ChoiDepartment of Practical Course Education, Cheongju National University of Education, Cheongju, Republic of Korea.
Inchan ChoiDepartment of Agricultural Engineering, National Institute of Agricultural Sciences, Jeonju, Republic of Korea.
Yoonha KimDepartment of Applied Biosciences, Kyungpook National University, Daegu, Republic of Korea.
Seong HeoDepartment of Horticulture, Kongju National University, Yesan, Republic of Korea.
Yong Suk ChungDepartment of Practical Course Education, Cheongju National University of Education, Cheongju, Republic of Korea.ORCID 0000-0003-3121-7600

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Facility cultivation has been evolved from greenhouses to smart farms using artificial intelligence (AI) that simulates big data to maximize production. However, the big data for AI in smart farm is not studied well; the effect of differences among varieties within a crop remains unclear. Therefore, the response of two varieties of blueberry, 'Suziblue' and 'Star', to light was tested using SAPD meter in order to demonstrate the environmental responses could be different among varieties within the same species. The results showed that those two varieties had significant differences in SPAD values based on the leaf's position and time, whereas 'Star' did not. This indicates that the effect of light depends on the variety, which implies that other traits and other crops may show similar differences. These results are based on a simple experiment. However, it is enough to elucidate that it is extremely important to characterize responses to the environment not only for each crop but also for each variety to collect data for smart farming to increase accuracy for modeling; consequently, to maximize the efficiency of these facilities.

Indexed as

Blueberry PlantsAgricultureArtificial IntelligenceCrops, AgriculturalFarms

Identifiers

PMID36040983
PMCPMC9426892

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.