Remote Sensing and AI-Driven Methodology for Corn and Bean Area and Yield Estimation in Honduras: A Pilot in the Departments of Olancho and El Paraíso

Project Summary:

The grant aims to develop, validate, and institutionalize an advanced methodology for estimating planted areas and predicting crop yields for corn and beans in Honduras using remote sensing, drone technology, geospatial data, and Artificial Intelligence. The broader goals are to strengthen the Government of Honduras' institutional capacity for agricultural monitoring, improve climate resilience, and support evidence-based policy decisions. It builds on a prior KGGTF-funded initiative (SISAGRO).

It is organized around four activities:

• Remote Sensing Methodology and Pilot Data Collection — Adapting global methodologies to Honduras' agroecological conditions and collecting field data in Olancho and El Paraíso.
• Development of AI-Based Tools — Training AI models to classify crop areas and estimate yields, with a pilot yield prediction report for the 2026/27 harvest.
• Validation of the Pilot Monitoring Module — Calibrating models with field data and integrating the module into the existing SISAGRO platform.
• Institutional Sustainability and Capacity Building — Training government staff (SAG/INFOAGRO) and facilitating two knowledge exchange visits to South Korea.

This grant is aligned as follows:

• The grant is fully aligned with the Rural Competitiveness Innovation Project (COMRURAL III, P174328), specifically under Component 2: Institutional Strengthening for the Improvement of the Agro-industrial Environment.