Recent applications of novel laser techniques for enhancing agricultural production
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.
Bibliographic record
Abstract
Abstract With ever increasing world population, the demands on food safety and security are also expected to substantially increase over the next few decades. As agronomic practices, agricultural mechanization and plant breeding technologies have already been extensively exploited, novel techniques need to be explored and implemented to enhance crop production. To this end, the emerging area of laser-based technologies has shown potential to bring about another revolution in enhancing quantity, quality, and safety of foods. This paper presents an exhaustive review of the use of five non-invasive non-destructive laser-based techniques in agriculture, namely laser biostimulation, light detection and ranging, laser land levelling, laser-induced fluorescence spectroscopy, and Raman spectroscopy. Herein we provide the advantages, status quo and challenges of each of these techniques and conclude with recommendations for future work. A comprehensive review of literature reveals the untapped potential of laser applications in agriculture that has the potential to unleash the next agricultural revolution.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it