Instance Segmentation of Lentinus edodes Images Based on YOLOv5seg-BotNet
Bibliographic record
Abstract
The shape and quantity of Lentinus edodes (commonly known as shiitake) fruiting bodies significantly affect their quality and yield. Accurate and rapid segmentation of these fruiting bodies is crucial for quality grading and yield prediction. This study proposed the YOLOv5seg-BotNet, a model for the instance segmentation of Lentinus edodes, to research its application for the mushroom industry. First, the backbone network was replaced with the BoTNet, and the spatial convolutions in the local backbone network were replaced with global self-attention modules to enhance the feature extraction ability. Subsequently, the PANet was adopted to effectively manage and integrate Lentinus edodes images in complex backgrounds at various scales. Finally, the Varifocal Loss function was employed to adjust the weights of different samples, addressing the issues of missed segmentation and mis-segmentation. The enhanced model demonstrated improvements in the precision, recall, Mask_AP, F1-Score, and FPS, achieving 97.58%, 95.74%, 95.90%, 96.65%, and 32.86 frames per second, respectively. These values represented the increases of 2.37%, 4.55%, 4.56%, 3.50%, and 2.61% compared to the original model. The model achieved dual improvements in segmentation accuracy and speed, exhibiting excellent detection and segmentation performance on Lentinus edodes fruiting bodies. This study provided technical fundamentals for future application of image detection and decision-making processes to evaluate mushroom production, including quality grading and intelligent harvesting.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".