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consideration given that agriculture accounts for approximately 70% of global freshwater
withdrawal.
Climate Adaptation and Crop Simulation
Climate change introduces intensifying uncertainty into crop production planning. Generative
models, particularly physics-informed GANs and diffusion-based climate downscaling systems, are
being used to simulate high-resolution regional weather scenarios and extreme event distributions
that exceed the temporal scope of historical meteorological records. When coupled with process-
based crop models such as DSSAT or APSIM, generative AI enables farmers and policymakers to
stress-test agronomic strategies against a wide envelope of plausible future climates, supporting
more resilient long-term land-use and breeding decisions.
Advisory Systems and Knowledge Dissemination
Perhaps the most democratizing application of generative AI in agriculture is the development of
accessible, multilingual advisory systems that can contextualize agronomic knowledge to the specific
conditions of smallholder farms. Large language models fine-tuned on regional crop calendars, soil
databases, and pest encyclopaedia enable conversational chatbots that respond to voice or text
queries in local languages — extending the reach of agricultural extension services to remote
communities that historically lacked access to expert guidance. Pilot deployments in sub-Saharan
Africa and South Asia have reported meaningful improvements in fertilizer use efficiency and pre-
harvest loss reduction among participating smallholders.
Challenges, Limitations, and Ethical Considerations
Despite their considerable promise, deep generative models present a set of challenges that must be
carefully managed to realize their full potential in agricultural contexts.
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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
