Development of an Automatic Watering System and Plant Growth Monitoring for Hydroponic Chili Production Using Neural Networks
Bibliographic record
Abstract
This study addresses the challenges faced in traditional chili production, where reliance on manual methods often leads to inefficiencies and suboptimal crop yields.To enhance the efficiency of chili production, this research develops an automated monitoring system that integrates watering management and pH adjustment based on IoT.Utilizing Neural Networks (NN) for plant growth monitoring, the system executed 120 automatic watering sessions over a 30-day period, ensuring optimal moisture levels and nutrient absorption.The results revealed a predictive performance characterized by a Root Mean Square Error (RMSE) of 0.49 and a coefficient of determination (R² ) of 0.99, indicating high accuracy in forecasting plant growth dynamics.The novelty of this research lies in its comprehensive approach, combining real-time monitoring and automated adjustments to optimize plant health.For future research, it is recommended to incorporate additional environmental sensors and expand the dataset to improve the model's adaptability and predictive capabilities.This could lead to the development of more advanced smart agriculture systems that can efficiently cater to various crops and environmental conditions, ultimately enhancing overall agricultural productivity.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".