aspg-28-4513-1792155038 20261016125038 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2026 11 2 Doaa Sami Khafaga Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Potato leaf diseases represent a persistent threat to global food security, demanding diagnostic systems that are not only accurate but also computationally efficient and deployable in resource-constrained agricultural environments. This study introduces a unified framework that integrates the Feature Tokenizer Transformer (FT-Transformer) with the Improved iHOW Optimization Algorithm, a metaheuristic designed for simultaneous feature selection and hyperparameter tuning. The FT-Transformer alone provides a competitive baseline, achieving 81.61% accuracy and an F1-score of 81.45%. Incorporating iHOW-based feature selection raises accuracy to 90.90% while reducing redundancy and enhancing precision, recall, and generalization. A final stage of iHOW-guided hyperparameter optimization further improves performance to 98.35% accuracy and 98.33% F1-score, outperforming all comparative metaheuristic variants. Beyond predictive gains, the optimized model demonstrates strong efficiency, requiring only 12.45 s of training time and 256.8 MB of memory. Taken together, these results establish iHOW+FT-Transformer as a scalable, interpretable, and resource-conscious solution for real-time plant disease classification. The proposed pipeline advances the state of precision agriculture by enabling robust, low-overhead crop monitoring systems adaptable to diverse and low-resource farming contexts. 2026 115 148 4513 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.110208 https://www.americaspg.com/journal/28/article/4513 https://www.americaspg.com/storage/articles/manuscripts/01M2ZWT00N7P5T7AC3R0D803QE.pdf