Development of a Low-Cost and Modular Vertical Farming Rig for Sustainable Farming Process
Bibliographic record
Abstract
Vertical farming is a method of cultivating crops in a controlled environment using a vertical farming rig.This rig automates, monitors, and controls the growing conditions, creating a microclimate that maximizes crop production.The steel cuboid enclosure, constructed with sustainable and locally sourced materials, benefits from the utilization of technical drawings and 3D modeling, ensuring precision and accuracy in its fabrication.The monitoring and control devices like the Raspberry PI Model B with other microcontrollers and IoT (Internet of Things) devices such as sensors and actuators are responsible for automating the vertical farm remotely.The intelligent camera plays a crucial role in a vertical farming system by capturing data and transmitting it to the microprocessor, enabling the optimization of operations within the vertical farm.Different crops, particularly those with fast growth rates responded differently to the vertical farm's automated system.Crop production on the vertical farm produced more growth from the sprouting stage to the seedling stage than traditional farming.The Nutrient Film Technique (NFT), a hydroponics method, was employed for the delivery of the water-nutrient solution containing NPK, ensuring optimal plant growth and maintenance.In comparison to traditional farming, the NFT approach utilized in the vertical farm produced higher levels of oxygen, good water-nutrient absorption, and cleaner crop yields.The results of this project demonstrate the potential for implementing low-cost, modular vertical farms in households, providing a sustainable and efficient means of crop production through advanced monitoring systems and mechanical drives.
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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".