A Multimodal UAV-Based Pipeline for Precision Agriculture: Aerial Stress Detection with YOLO and High-Fidelity Disease Classification Using DeiT
Why this work is in the frame
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Bibliographic record
Abstract
Precision agriculture requires scalable, accurate monitoring solutions to bridge the gap from macro-level field awareness down to leaf-level diagnostics. This paper presents a novel multimodal pipeline that integrates Unmanned Aerial Vehicle (UAV) imagery with advanced deep-learning models, achieving broad-spectrum stress detection and fine-grained disease classification. A CNN initially established a foundational accuracy but struggled to capture the subtle signatures of different diseases. Switching to Vision Transformers (ViTs) improved performance, yet computational overhead and data requirements posed challenges. Later, we moved to DeiT with extensive hyperparameter tuning and data augmentations that gave an astonishing 99.45% accuracy on the multi-class plant disease dataset. To complement the close-up acumen of DeiT, we used YOLO from an aerial view to rapidly identify stressed versus healthy crop regions from real-time UAV footage. This two-tiered approach, wherein YOLO is used for aerial scanning and DeiT for leaf-level diagnoses, offers an unprecedented level of precision with scalability. Finally, complex output is translated by an LLM into farmer-friendly advisories to ensure immediate actionable insights. Our integrated framework sets a new benchmark in UAV-driven precision agriculture by balancing model sophistication, computational feasibility, and end-user interpretability.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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