Intelligent agriculture for monitoring and diagnosis of corn cultivation (Zea mays)

The demand for food for an increasing population raises challenges for governments in terms of sovereignty and food security; However, from the point of view of the producers, the challenges are related to the low competitiveness of the region, the high production costs and low productivity, as a consequence of an unreasonable management of crops and the limited incorporation of technologies in the production processes. New information and communication technologies (ICT) make processes possible, improving management and decision making. The most relevant ICTs for the study of agricultural systems are the techniques of remote sensing and Deep Learning used in the analysis of satellite images, drones and aerial photographs for the diagnosis of the state of the crops since they provide a rapid estimate of the nutritional status of the crops to a high spatial resolution in vast areas. This proposal proposes to carry out a descriptive scientific investigation and technological development of a web platform for the diagnosis of the state of corn cultivation by applying remote sensing techniques (TD) and Deep Learning (DL) in the provinces of Manabí, Guayas and Azuay for modern and sustainable agriculture, by executing three components: I. Characterization of agronomic and productive variable Production plots; II. Multi -spectral image classification models with TD and DL techniques; And III. Web platform with functionality to visualize, consult and download the geoinformation generated in the project.

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