Expression of Interest for Modeling and Reporting Consulting Firm for Crop Monitoring and Production Pilot in Honduras The Agriculture and Food Global Practice (AGF GP) of the World Bank Group, with financial support from the Korea Green Growth Trust Fund (KGGTF), is implementing an initiative between January 2026 and May 2027 aimed at strengthening the capacities of the Government of Honduras for remote monitoring and identification of staple crop (maize and beans), as well as area and yield prediction, using remote sensing technologies, geospatial data, drone technologies, artificial intelligence (AI), field measurements and surveys. The project will strengthen the Government of Honduras institutional capacity for agricultural monitoring and reporting, enhance climate resilience and food security, and improve evidence-based decision-making in the agri-food sector. This initiative is framed under Component 2 (Institutional Support to the Ministry of Agriculture and Livestock (SAG)) of the Innovation for Rural Competitiveness Project COMRURAL III (P174328) and contributes to the first phase of the development and operation of the Agri-food Integrated Information System (SISAGRO) focusing on producing and disseminating agrometeorological, and crop production-related information to improve decision making. Maize (Zea mays L.) and beans (Phaseolus vulgaris L.) are the crops of greatest relevance to Honduras food security and have been prioritized for monitoring and reporting under SISAGRO. They are produced in both commercial and small-scale production systems across Honduras each year from early May to late February. In 2025 there were 309,979 maize farmers that planted 283,067 Ha in three seasons (primera, postrera and late postrera) and produced 640,421 MT of white maize for human consumption. Also, there were 136,109 bean farmers that planted 102,141 Ha in two seasons (primera and postrera) and produced 149.928 MT of small red beans, particular type of beans preferred by consumers in Hondurans, Salvadorians and Nicaraguans (Ag Census, 2025). Currently, SAG crop monitoring has limited coverage, lacks representative sampling and/or survey methodologies, is conducted with centralized, limited human resources and does not fully use the technical resources at its disposal (including late-generation drones and satellite imaging monitoring software). Area and yield estimations continue to rely on limited use of non-field validated remote-sensing tools (for commercial maize monitoring only), traditional field data collection methods and a heavy reliance on anecdotal yield and area reporting (from a limited number of trusted farmers) that are costly, slow, and of limited accuracy. thereby constraining the States capacity to plan timely interventions in the sector. To date, SAG does not produce annual or seasonal maize and bean production reports. In response to this need, SAG-SISAGRO proposes the implementation of a pilot project in the departments of Olancho and El Paraíso, selected due to their productive importance and the diversity of agricultural systems they host, to remotely identify and estimate the planted area and production volume of maize and beans using remote sensing technologies such as satellite and multispectral imagery, drones, and artificial intelligence. This consultancy seeks to contract a specialized firm for the design, development, implementation, and validation of predictive and estimation models for commercial production pilots, with a roadmap for escalation to other production systems and nation-wide implementation. It also includes a knowledge transfer and capacity development component for SAG-SISAGRO technical staff and academia for AI-supported modeling and reporting. II. General Objective and Scope To support capacity transfer to SAG-SISAGRO for the design, development, implementation, and field validation of AI-assisted crop identification, health monitoring, area and yield prediction, estimation, modeling, and reporting for maize and bean pilots in selected areas of Olancho and El Paraíso, Honduras during the 2026-2027 planting season. III. Specific Objectives · To assess current gaps (policy, architecture, hardware, software, human resources, etc.) in SAG-SISAGRO and Honduras for AI-assisted staple crop identification, health monitoring, area, yield modeling and reporting at the pilot level in selected areas of Olancho and El Paraiso. · To develop a methodological guide and user manual for SAG-SISAGRO and the Honduras Agriculture Academia for modeling and periodic reporting of staple crop production information using agrometeorological and soil data, satellite and drone imagery, artificial intelligence, field data and historical agricultural production data. · To provide the field data collection consulting firm with guidelines and specifications for pilot sampling design, agrometeorological, soil, satellite and drone imagery, and field surveying, for data cleanup, packaging and delivery for modeling and reporting. · To support the institutional strengthening of SAG-SISAGRO for piloting the use of AI in crop remote identification, monitor crop health, modeling production area and yield and report generation in selected areas of Olancho and El Paraiso for strategic public and private sector decision-making. · To provide SAG-SISAGRO with a technical 2027-2030 roadmap for integrating AI-assisted staple crop identification, health monitoring, area, yield modeling and reporting to the SISAGRO Platform Crop Monitoring Module to enable scalability from landscape pilot to regional and then to national coverage. Also, to transition from a yield prediction model (based on soil and agrometeorological data) to a yield estimation model (based on satellite, drone imagery and field validation) and expand crop monitoring to other crops. · To design protocols for SAG-SISAGRO systematic Quality Assurance, data and information integrity, cyber security and update of the models and reports, enabling the generation of reliable forecasts during and at the end of each agricultural cycle at the landscape pilot level with a roadmap for escalation. · To support SAG-SISAGRO in developing, performing and reporting 2026-2027 pilot model validation procedures by comparing its predictions against actual historical and field-based production data. · To design the SAG- SISAGRO modeling and reporting system architecture with scalability capabilities to progress from landscape pilot to national coverage and incorporate other strategic crops in Honduras. · To transfer knowledge and build the capacity of the SAG-SISAGRO technical team for the operation, maintenance, and continuous update of the model. · To develop comprehensive technical documentation for the SAG-SISAGRO, including user manuals and system administration manuals for modeling and reporting, including annual, seasonal and emergency report formats. 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