AgenticAI HSI an Agentic AI Framework for Automated Seed Classification from Hyperspectral Insights to Maize Seed Models

Nasrulla Khan K; C R Sakthivel

doi:10.36948/ijfmr.2026.v08i01.66430

AgenticAI HSI an Agentic AI Framework for Automated Seed Classification from Hyperspectral Insights to Maize Seed Models

Author(s)	Nasrulla Khan K, Dr. C R Sakthivel
Country	India
Abstract	Hyperspectral imaging (HSI) has emerged as a transformative modality in agricultural informatics, enabling the extraction of rich spectral and spatial information for tasks such as disease detection, crop monitoring, and seed quality assessment. Coupled with deep learning (DL), HSI offers unprecedented accuracy in pattern recognition; however, its adoption remains constrained by several persistent challenges. Chief among these are the high dimensionality of spectral data, scarcity of annotated datasets, limited interpretability of deep models, and the computational overhead associated with large-scale deployment in resource-constrained agricultural settings. Current research, while effective in addressing specific subproblems, lacks a unified and autonomous framework capable of integrating data preprocessing, feature selection, augmentation, model optimization, explainability, and deployment into a coherent pipeline. This review identifies this gap and proposes an Agentic AI paradigm as a novel solution to bridge hyperspectral insights with practical crop and seed classification applications. Specifically, we conceptualize the AgenticAI-HSI Framework, a modular system comprising autonomous agents for data validation, augmentation via generative models, automated model search and training, uncertainty-aware evaluation, explainable decision-making, and lightweight deployment for edge environments. By contextualizing the framework through the case of maize seed classification, we demonstrate how Agentic AI can enhance both the accuracy and trustworthiness of agricultural models, while simultaneously reducing human intervention. Beyond immediate applications, this paper outlines a forward-looking roadmap, emphasizing the integration of multimodal data sources, active human-in-the-loop learning, and deployment on scalable, low-cost platforms. In doing so, it positions Agentic AI not merely as an incremental improvement over existing methods, but as a paradigm shift that transforms agricultural imaging pipelines into self-improving, interpretable, and deployable systems.
Keywords	Agentic AI, Hyperspectral Imaging (HSI), Seed Classification, AutoML, Explainable AI, Agricultural Informatics, Deep Learning
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-17
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.66430

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AgenticAI HSI an Agentic AI Framework for Automated Seed Classification from Hyperspectral Insights to Maize Seed Models

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