Smart agriculture for the monitoring and diagnosis of the corn crop (Zea mays)
General Objective: Diagnose the nutritional status of the corn crop (Zea mays) through an intelligent system based on remote sensing techniques and Deep Learning for modern and sustainable agriculture. Specific Objectives Participating Institutions: ESPOL, UTM, UC. Participants: Project Director María Fernanda Calderón Vega. Awarded budget: $38,241.50 Project status: In progress.
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