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Record W4415360286 · doi:10.59934/jaiea.v5i1.1602

Automated Fertilizer Spraying System for Purple Eggplant Plants Based on IoT at STMIK KAPUTAMA

2025· article· W4415360286 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2025
Typearticle
Language
FieldComputer Science
TopicMultimedia Learning Systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsFertilizerInternet of ThingsMoistureSoil moisture sensorMicrocontrollerGreenhouse

Abstract

fetched live from OpenAlex

The purple eggplant plant (Solanum melongena L) is a high-value vegetable crop that requires proper fertilization to support optimal growth. However, the manual fertilization methods currently in use are often inefficient and inaccurate, leading to fertilizer waste and suboptimal harvest yields. This study developed an automatic fertilizer spraying system based on the Internet of Things (IoT) using a NodeMCU ESP8266 microcontroller and soil moisture sensors to monitor soil conditions in real-time. The system is equipped with an RTC module and the Blynk app to automatically adjust fertilizer application based on soil moisture levels between 50% and 60%. Test results demonstrate that the system can efficiently activate the pump when moisture drops below the minimum threshold and deactivate it when moisture reaches the maximum threshold. Implementing this system improves fertilizer efficiency compared to manual methods and facilitates remote control, thereby supporting increased productivity and the development of purple eggplant farming.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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