Metaheuristic-Driven Optimization of a Neural Model Using Tuna Swarm Intelligence for Cognitive Classification of Wheat Species

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.