Pattern Mining and Classification Techniques for Agriculture and Crop Simulation

• Autores: Rozas-Acurio, Javier, Zavaleta-Salazar, Sergio, Ugarte, Willy
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/669598
• Enlace del recurso: http://hdl.handle.net/10757/669598
• Nivel de acceso: acceso embargado
• Materia: Agriculture; Classification; Crop simulation; Pattern mining

Research shows that data analysis and artificial intelligence applied to agriculture in Peru can help manage crop production and mitigate monetary losses. This work presents SmartAgro, a system based on pattern mining and classification techniques that takes information from multiple sources related to the agricultural process to extract knowledge and produce recommendations about the crop growth process. The problem we seek to mitigate with our system is the economic losses generated in Peruvian agriculture caused by poor crop planning. Our results show a high accuracy in regards to type of crop recommendation, and a knowledge base useful for agricultural planning.