Metaheuristic-Driven Optimization of a Neural Model Using Tuna Swarm Intelligence for Cognitive Classification of Wheat Species
Bibliographic record
Abstract
Accurate classification of wheat varieties is vital for enhancing agricultural productivity and maintaining food quality standards. However, conventional methods based on manual inspection are time-consuming, labor-intensive, and prone to human error. This paper introduces an innovative methodology that integrates the tuna swarm optimization algorithm with a multi-layer perceptron neural network to achieve high-accuracy classification of wheat species. The SEEDS dataset, containing three wheat species (Kama, Rosa, and Canadian), was used to evaluate the effectiveness of the proposed model. Initially, the tuna swarm optimization algorithm’s robustness was validated using the CEC 2019 benchmark suite, where it demonstrated superior performance against alternative metaheuristics such as the Archimedes optimization algorithm, prairie dog optimization, and Harris hawks optimization. Tuna swarm optimization achieved a 79.29% classification accuracy (Macro and Weighted F1-score = 0.7923) while significantly reducing the mean squared error to 7.52E − 02, outperforming other optimization algorithms. The proposed tuna swarm optimization-based multi-layer perceptron exhibited strong generalization capability, as demonstrated by the ROC-AUC values exceeding 0.77 for all classes, with the highest classification success observed in class "0" (AUC = 0.9851). Compared to conventional gradient-based training methods, the tuna swarm optimization-enhanced multi-layer perceptron model consistently outperformed alternative techniques in convergence stability, classification precision, and robustness across multiple iterations. These results highlight the potential of metaheuristic-driven neural network optimization in agricultural applications, providing a highly efficient and scalable solution for automated wheat classification.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".