aspg-3-3982-1791943109 20261014015829 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2026 21 1 Nilam Sachin Patil Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India E. 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5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} Early and precise detection of corn leaf diseases is important for maintaining crop yield and quality. This work suggests a new end-to-end system Hybrid Adaptive Swarm-enhanced Vision Transformer (HAS-ViT) to overcome the limitations of current techniques such as poor accuracy, high computational expense, and overfitting and inefficient feature extraction. The suggested framework combines a three-stage pipeline such as segmentation, classification and optimization to overcome the issues. First, Adaptive Gradient Masking with Color Entropy (AGM-CE) is a novel segmentation technique that isolates diseased areas through an integration of local color entropy and gradient energy in the LAB color space. This guarantees accurate area selection and removal of the background. Then, a transformer model is constructed named Vision Transformer with Enhanced Visual Attention (ViT-EVA). It integrates depthwise attention layers as well as lesion-aware region concentration, enhancing separation of disease classes and model simplification. Finally, Adaptive Bio-Inspired Gradient Tuning (ABGT) optimizer integrates the Bat Algorithm, AdamW and gradient sign flipping for effective learning and convergence. The mechanism speeds up convergence, prevents local minima and maintains exploration exploitation trade-offs at training. The performance of proposed work is measured on a corn disease dataset and performs at 98.1% accuracy and 0.12 loss than conventional and current transformer-based models. 2026 214 234 3982 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.210116 https://americaspg.com/journal/3/article/3982 https://americaspg.com/storage/81753732197.pdf