Improving Crop Productivity and Climate Resilience in Nigeria using Generative AI-Based High-Resolution Mapping and Yield Scenario Simulations.

Nigeria faces unprecedented agricultural challenges due to climate variability, population growth, and limited technological adoption. This research investigates the application of generative artificial intelligence (AI) for high-resolution crop mapping and yield scenario simulations to enhance agricultural productivity and climate resilience. Using a Theory of Change framework, we develop an integrated approach combining satellite imagery, machine learning algorithms, and predictive analytics to optimize crop production systems. Our methodology employs fine-tuned pre-trained language models (PLMs) for sentiment analysis of agricultural feedback data and generative AI models for scenario simulation. The study demonstrates that AI-driven precision agriculture can increase crop yields by 25-40% while improving climate adaptability. We propose a scalable implementation framework that addresses Nigeria's unique agricultural landscape, contributing to food security and sustainable development goals.