High-Resolution Stress Detection in Crops: Integrating Satellite and Drone Remote Sensing for Resilient Agriculture
Bibliographic record
Abstract
Satellite and drone-based remote sensing technologies are transforming how we detect plant stress by offering wide-area, non-contact monitoring tools. These systems can identify early signs of drought, heat, salinity, and nutrient stress using vegetation indices like NDVI, NDWI, and red-edge reflectance. In many developing regions, food systems remain vulnerable, yet field-based monitoring continues to be the standard, often too slow and limited to fully capture what is occurring across farms. The review is based on a structured synthesis of peer-reviewed studies and technical reports published between 2007 and 2025, selected through targeted keyword searches across Scopus, Web of Science, and Google Scholar, with emphasis on field-level applications in stress-prone agricultural systems. It examines how remote sensing is currently being applied to track abiotic stress across major crops, using case studies from India, China, Mali, and Sudan. These case studies help illustrate what is effective and where gaps still exist. Notably, NDVI and canopy temperature indices have shown strong correlations with drought severity and crop losses. Key challenges include the lack of stress indices tailored to local crops and soils, a limited connection between detected stress and yield outcomes, and the high costs or technical barriers associated with drone use. The review also outlines specific future research needs, such as how to detect multiple stressors simultaneously, enhance drought detection in drylands, and develop low-cost, accessible remote sensing tools that can assist smallholder farmers. These findings underscore the need for localized, affordable remote sensing solutions to bridge the gap between research and practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".