Bibliographic record
Abstract
Peas, as a globally significant legume crop, play a crucial role in food production and human nutrition. However, field pests and diseases, drought, high temperature and nutrient deficiency and other stresses seriously affect the yield and quality of peas. Timely and efficient stress monitoring is of great significance for ensuring pea production. This study reviews the advantages of unmanned aerial vehicle (UAV) remote sensing platforms in farmland stress monitoring, including high-throughput phenotypic acquisition and the roles of multispectral, RGB, and thermal infrared sensors. It also analyzes the applicability of commonly used deep learning models (CNN, RNN, Transformer) in crop stress detection. And the application progress of image classification, object detection, and semantic segmentation technologies in identifying crop stress types, a technical framework combining unmanned aerial vehicles and deep learning was constructed. The data collection and annotation process, data preprocessing and enhancement methods, as well as the pipeline for fusing multi-source images with deep learning models were expounded. The identification, degree classification and spatial distribution visualization of physiological stress (drought, nutrient deficiency, high temperature) and biological stress (diseases, pests) were mainly discussed. In the case of field stress detection of peas, the recognition performance of the deep learning model and its application value in field management decisions were verified. The combination of unmanned aerial vehicle (UAV) remote sensing and deep learning technology can achieve precise detection of field stress in peas. This study aims to provide strong support for precision agricultural management.
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 | 0.000 |
| 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 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".