Computer Vision for Monitor and Control of Vertical Farms Using Machine Learning Methods
Bibliographic record
Abstract
Vertical farms have become increasingly popular in today’s society. Its popularity owes to the increasing relevance of vertical farms as a panacea for the effect of desertification and urbanization as proposed by experts and researchers. Urbanization is increasing annually, as at the middle of year 2020 according to statistica.com the rate of urbanization was at 56 percent, and it has been estimated that by the year 2050 about 70-90% of global population will live in cities. The tripod effect of urbanization, desertification and climate change has made vertical farms a convenient alternative. While vertical farm is helping to solve this problem, the interface of computers with vertical farms has exponentially increased the efficiency, it has helped to create convenient environments which can be controlled, as such facilitating and all-around production of food crops all through the year despite changing environmental conditions. In this paper, we are taking a step further to see how computer vision can help in this process. So far IoT has been used to monitor the farm extrinsic factors, and get relevant data, the problem with that method is that only the external factors are being monitored, in this paper we will be exploring how computer vision can monitor intrinsic factors, but beyond that, we will also explore how computer vision and machine learning methods could be used together with IoT for the control of vertical farms as well to create favorable conditions for the planting of vertical farms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".