Moolu Venture Lab
Published January 10, 2025 | Version v2
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Improving Crop Productivity and Climate Resilience in Nigeria using Generative AI-Based High-Resolution Mapping and Yield Scenario Simulations.

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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.

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