A Cyber-Physical Architecture for Crop Recommendation: Integrating Climate Prediction (XGBoost), IoT Sensing, and Multi-Criteria Decision-Making - Melo, Vitor Hugo Barbosa; Alves, André Vinicius Neves; Oliveira, Saulo Mattheus Ribeiro de; Negreiros, Waldemiro José Assis Gomes; Silva, Filipe Corrêa da; Rodrigues, Nilton Rodolfo Nascimento Melo; Cardoso, Diego Lisboa; Seruffo, Marcos Cesar da Rocha (Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA); 2026: Anais do XVII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos NaturaisDO - 10.5753/wcama.2026.23738, 2026)
This work proposes a unified cyber-physical framework to evaluate the agronomic suitability of Açaí, Cocoa, and Soybean in the state of Pará, Brazil. The methodology integrates ERA5 climate data, XGBoost forecasting, and a conceptual IoT node for local microclimatic calibration using Edge Computing. Climate projections for 2026–2028 fed a modified SAW (Simple Additive Weighting) decision engine. For Soybean, the system identifies optimal planting windows (January–April), while for Cocoa and Açaíit supports year-round thermal and water stress monitoring. Simulations indicated high suitability during the rainy season and severe penalties in dry months (July–October), highlighting Cocoa’s vulnerability to heat stress and Soybean’s planting restrictions. Results validate the software layer and support future IoT field deployment.