# Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions

arXiv: https://arxiv.org/abs/2310.20301 · HTML: https://mlsanity.com/paper/2310.20301/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 99 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning](https://mlsanity.com/paper/2111.04724/) (2021)
- [Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt](https://mlsanity.com/paper/2008.04060/) (2020)
- [Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2004.05471/) (2020)

## Built on by (most cited first, 1 of 1)

- [LLMs for Enhanced Agricultural Meteorological Recommendations](https://mlsanity.com/paper/2408.04640/) (2024)

## Abstract (arXiv)

Food security, a global concern, necessitates precise and diverse data-driven solutions to address its multifaceted challenges. This paper explores the integration of AI foundation models across various food security applications, leveraging distinct data types, to overcome the limitations of current deep and machine learning methods. Specifically, we investigate their utilization in crop type mapping, cropland mapping, field delineation and crop yield prediction. By capitalizing on multispectral imagery, meteorological data, soil properties, historical records, and high-resolution satellite imagery, AI foundation models offer a versatile approach. The study demonstrates that AI foundation models enhance food security initiatives by providing accurate predictions, improving resource allocation, and supporting informed decision-making. These models serve as a transformative force in addressing global food security limitations, marking a significant leap toward a sustainable and secure food future.