| Title: |
Improving Crop Productivity and Climate Resilience in Nigeria using Generative AI-Based High-Resolution Mapping and Yield Scenario Simulations. |
| Authors: |
Moolu Venture Lab; Ogbonna, Prince; Ikechukwu, Micheal; Akpan, Emem Bassey; Udo, Esther |
| Publisher Information: |
Zenodo |
| Publication Year: |
2025 |
| Collection: |
Zenodo |
| Description: |
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. |
| Document Type: |
text |
| Language: |
unknown |
| Relation: |
https://zenodo.org/communities/moolu/; https://zenodo.org/records/16790234; oai:zenodo.org:16790234; https://doi.org/10.5281/zenodo.16790234 |
| DOI: |
10.5281/zenodo.16790234 |
| Availability: |
https://doi.org/10.5281/zenodo.16790234; https://zenodo.org/records/16790234 |
| Rights: |
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: |
edsbas.9DDAB13 |
| Database: |
BASE |