Satellite-Based Crop Identification and Risk Profiling for Areawide Management of Whitefly and Tomato Yellow Leaf Curl Virus in Southwest Florida
Bibliographic record
Abstract
Effective management of whitefly (Bemisia tabaci) infestations and tomato yellow leaf curl virus (TYLCV) is vital for sustainable vegetable production in southwest Florida. This study introduces a robust framework that integrates satellite-based crop identification with disease risk profiling to support areawide pest control efforts. Using Sentinel-2 satellite imagery and machine learning, we accurately identified crop types throughout the growing season, allowing us to correlate whitefly populations, TYLCV incidence, and specific crop distributions. Spatial analysis revealed significant autocorrelation up to 1,750 m for both whitefly and TYLCV across the season extending to 5,000 m from January to April, which emphasizes the need for timely management in these zones. Temporal analysis showed a strong influence of temperature on whitefly populations and TYLCV incidence during the February to May period, with positive correlations observed at multiple lag times and window sizes, particularly between 30 and 85 days (P < 0.001) and window sizes from 20 to 50 days. Conversely, rainfall showed weaker correlations, suggesting that temperature is a more critical factor. The dispersal of whitefly populations was further influenced by nearby vegetable fields, with correlations extending up to 9,000 m during peak months. These results underscore the critical role of temperature patterns and spatial crop arrangements in shaping pest dynamics. Our approach offers a scalable model for proactive monitoring and management, promoting resilience and sustainability in agricultural systems facing similar pest and virus challenges globally.
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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 | 0.000 | 0.001 |
| 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 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".