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Record W3196768319 · doi:10.18280/ijdne.160409

Design of the Humidity and Temperature Controller Using the Moistures of Leaf and Soil

2021· article· en· W3196768319 on OpenAlexvenueno aff
Walaa Kareem Khalaf, Yong Tae Kim

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGreenhouse Technology and Climate Control
Canadian institutionsnot available
FundersKorea International Cooperation Agency
KeywordsGreenhouseHumidityEnvironmental scienceIrrigationWater contentRelative humidityAir temperatureMoistureController (irrigation)Materials scienceAgronomyMeteorologyEngineeringGeographyComposite materialGeotechnical engineering

Abstract

fetched live from OpenAlex

A controlling of air temperature and humidity is important issue because environment change has effects on growing stages of plants. In this study a (DHT22, AH-300u, YL-69) sensors and a microcontroller (arduino Uno) have been used to monitoring and controlling the temperature, humidity of air and control irrigation process based on monitoring the moistures of (soil, plant leaf) data inside the greenhouse prototype. The findings during the experiment time (4 days) indicates that the air temperature maintain at reference value 27℃ and relative humidity has a slightly increase by 0.7 from reference value (82). The output voltage of soil moisture sensor has been monitor at a small range (0.8 v - 1 v) roughly stable, and it is near to field capacity. The output voltage of leaf moisture sensor is slightly increased. From this study it has been observed that the proposed system in a greenhouse is a good procedure to maintain air temperature and humidity inside the greenhouses and effective for monitoring air factors as well as soil and leaf moistures. regarding irrigation process, a decision table has been suggested to control the irrigation water flow rate according to output voltage of each of soil and leaf moistures sensors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.233
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicGreenhouse Technology and Climate ControlFrench-language works237,207