Transfer Learning using Hybrid Convolution and Attention Model for Nematode Identification in Soil Ecology
Bibliographic record
Abstract
Nematodes constitute a crucial component of soil ecosystems, contributing significantly to soil ecology.The ability to differentiate between non-parasitic and plant-parasitic species is a critical aspect of efficient crop management.However, manual identification methods are labor-intensive, time-consuming, and susceptible to errors.Recent developments in the realm of machine and deep learning have paved the way for their application in the field of nematode identification.This study implements a hybrid convolution and attention network, termed CoAtNet-0, which integrates a Convolutional Neural Network (CNN) and a transformer for the identification of nematode genera.The performance of this model is anticipated to be robust across various dataset sizes.The current investigation employed a combined dataset, comprising both a self-collected nematode dataset and a public dataset, to evaluate the performance of CoAtNet-0 under single and double data augmentation conditions.Furthermore, it explored the efficacy of the Adam, Stochastic Gradient Descent (SGD), and RMS optimizers to identify the most effective optimizer for the CoAtNet-0 model.Adam was selected due to its typically satisfactory performance, while SGD was chosen as it often delivers superior results in deep learning applications.RMSprop was utilized for performance comparison among adaptive optimizers devoid of momentum.Upon evaluation, it was determined that the highest performance was achieved by the CoAtNet-0 model using the SGD optimizer on the non-augmented dataset, delivering an accuracy of 97.22%.Thus, the selection of suitable data augmentation methods and an appropriate optimizer is instrumental in optimizing the performance of the CoAtNet-0 model for nematode identification.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".