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
- To characterize the agronomic and productive behavior of the corn crop in three experimental plots of 0.5 ha each and production plots of 150 ha distributed in the provinces of Manabí, Guayas and Azuay. As an assumption, the active collaboration of corn producers is expected to monitor the production plots.
- Generate multispectral image classification models using remote sensing techniques and Deep Learning. For which, two models of nutritional diagnosis of the corn crop will be generated with the aforementioned techniques, from the acquisition of images of the phenological stages of the crop.
- Create a web platform for the management of information in the efficient management of corn cultivation, where the geoinformation generated in the project can be viewed, consulted and downloaded.
Participating Institutions:
ESPOL, UTM, UC.
Participants:
Director of the project María Fernanda Calderón Vega.
- Henry Antonio Pacheco Gil
- Miguel Andres Realpe Robalino
- Jonathan Salvador Paillacho Corredores
- Rosa Lucia Lupercio Novillo
- Andres Eduardo Arciniegas Farez
- Victor Eduardo Tacuri Espinoza
Awarded budget: $38241,50
Project status: In progress.
