Volume 11 • Issue 2 • PP: 115–148 • 2026
Potato Disease Detection via Joint Feature Selection and Hyperparameter Optimization of the Feature Tokenizer Transformer with iHOW
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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.
Keywords
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