StrawberryTalk-v2: Edged-IoT System for Detection of Strawberry Anthracnose
Bibliographic record
Abstract
Anthracnose diseases can severely affect strawberry plant stands and yields, making early detection essential. Manual identification of diseased plants is labor-intensive, prompting the development of IoT-based AI (AIoT) solutions for more efficient detection. While many AIoT methods rely on high-performance servers typically hosted in the cloud, edge solutions are preferable for commercial farms to reduce dependency on network connections. To implement an effective edge-based system, the computation hardware must feature a capable CPU for running the IoT engine and a GPU sufficient for YOLO-based detection, while maintaining code security. This article explores the StrawberryTalk-v2 solution using the NVIDIA Jetson Nano, secured with Winbond’s W77Q TRUSTME® Secure Serial Flash Memory, fulfilling these requirements. The deployment of the StrawberryTalk-v2 IoT solution on this hardware facilitates strawberry anthracnose detection. Choosing a YOLO version that balances edge device efficiency and high detection accuracy presents a significant challenge. While YOLO11 Nano’s computational complexity is well-suited for edge deployment, its baseline accuracy falls short of YOLO11 Extra Large. By incorporating WIoU and DySample into YOLO11 Nano, the detection accuracy exceeds that of the Extra Large version while retaining the Nano version’s low execution cost. These enhancements allow the system to achieve superior performance, with StrawberryTalk-v2 reaching a four-level detection accuracy of 95.5%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".