Page 9 - العدد الثالث - الاصدار السادس-شهر يونيو

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Computational requirements represent a primary barrier: training large-scale generative models
demands substantial GPU infrastructure and energy expenditure, placing cutting-edge systems out
of reach for research institutions and governments in low-income agricultural regions. Addressing
this disparity requires targeted investment in cloud-based and federated learning infrastructure that
can deliver model capabilities without requiring local computational resources.
Data quality and representativeness are equally critical concerns. Generative models trained on
datasets skewed toward temperate, industrialized agricultural systems may produce outputs poorly
calibrated to tropical or semi-arid conditions. This risk of distributional mismatch underscores the
importance of geographically and agroecologically diverse training data, as well as domain
adaptation techniques that allow models to be fine-tuned with limited local data.
Hallucination and reliability issues, well-documented in language model research, carry particular
risk in agricultural decision-support contexts. An advisory system that confidently recommends an
inappropriate pesticide dose or irrigation schedule may cause crop loss, financial harm, or
environmental damage. Robust uncertainty quantification, human-in-the-loop validation, and clear
communication of model confidence are therefore essential design requirements for deployed
agricultural AI systems.
Finally, intellectual property, data sovereignty, and fairness considerations deserve attention.
Farmers who contribute observational data to training pipelines should benefit equitably from the
value generated by resulting models. Transparent data governance frameworks and open-access
model repositories can help ensure that generative AI enhances rather than exacerbates agricultural
inequality.

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Address: New Beni-Suef City. Beni-Suef. 62111 Web Site: WWW.fci.bsu.edu.eg

Email: fci@fci.bsu.edu.eg Telephone/Fax: 082 2246796