Pipeline to detect Colorado Potato Beetles as tiny objects under field conditions in real-time using deep learning and transfer learning techniques
Bibliographic record
Abstract
This study introduced a novel methodology for real-time identifying Colorado Potato Beetle (CPB), as tiny objects within potato field using cameras mounted on moving sprayer. The methodology consisted of three key steps: targeted image-cropping-based preprocessing, a two-phase transfer learning strategy, and developing an end-to-end detection pipeline system. The image-cropping-based preprocessing step helped preserve the shape and dimension of CPBs during processing by deep learning algorithms, thereby contributing to improved accuracy across the entire detection pipeline. In the two-phase transfer learning approach, the first phase involved training on clear, high-quality focused images, while the second phase fine-tuned the models using field images captured by a camera mounted on a moving sprayer. This approach significantly enhanced the model's robustness to environmental variability, including motion blur. The study compared the performance of three state-of-the-art object detection algorithms, including YOLOv5, YOLOv7, and Faster-RCNN. Thus, the findings from the experiments showed that the image-cropping-based preprocessing approach, when employed in conjunction with transfer learning, substantially increased the detection rates for all studied models. Among all models YOLOv5 emerged as the best choice, offering the best balance between detection accuracy and computational efficiency. At an image size of 640 × 640, YOLOv5 achieved up to 79% detection accuracy and consistently outperformed others in terms of inference time, processing a single image segment (640 × 640) in just 42 milliseconds enabling real-time performance. Further optimization using Tensor-RT reduced the pipeline’s end-to-end latency to 86 milliseconds per HD image (1920 × 1080), facilitating operation at over 10 frames per second. This research enhances the growing use of deep learning and computer vision in agricultural pest control. The results could be valuable for improving site-specific pesticide applications, benefiting farmers, and protecting the environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".