Classification of wild mushrooms based on improved ShuffleNetV2
Bibliographic record
Abstract
This study introduced an improved CHE_ShuffleNetV2 model based on ShuffleNetV2 to address the classification challenge of wild mushrooms in a complex environment. The model incorporated a Cross Stage Partial (CSP) structure to simplify its complexity. Furthermore, it adopted Hybrid Dilated Convolution (HDC) to replace conventional convolution, enhancing the model’s recognition accuracy by expanding its receptive field. In addition, the ECA module was integrated to enhance the focus of the model on crucial feature information. The Hardswish activation function was employed instead of the ReLU activation function to reduce the number of parameters. The experimental results demonstrated that the enhanced model achieved improved accuracy, precision, recall, and F1-Score of 95.02%, 95.19%, 94.56%, and 94.00%, respectively, representing improvements of 2.81%, 3.82%, 3.08%, and 3.65%, correspondingly, over the original model. The enhanced model also reduced the parameters and FLOPs to 0.933 M and 104.08 M, respectively, representing reductions of 26.13% and 30.42% over the original model. Compared with commonly used lightweight models such as EfficientNet, DenseNet, and MobileNetV2, the CHE_ShuffleNetV2 model showed superior performance in solving the wild mushroom classification problem in complex environments, exhibiting its suitability for deployment on resource-constrained devices including mobile terminals. Keywords: classification of wild mushrooms, ShuffleNetV2, deep learning, lightweight DOI: 10.25165/j.ijabe.20251801.9179 Citation: Xu X M, Yang D W, Wei Q Q, Li J Y, Zhang J. Classification of wild mushrooms based on improved ShuffleNetV2. Int J Agric & Biol Eng, 2025; 18(1): 208–218.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".