Remote sensing techniques using satellite images are widely applied in several fields of agricultural science. The ease of obtaining a large amount of information in real time combined with the resources offered by deep neural networks have enabled great advances in this field of study.
With a special focus on remote sensing applied to the identification of sugarcane varieties, discrimination
between varieties is extremely important as it allows monitoring the growth of the crop in terms of the
characteristics of each variety of sugarcane.
The methodology applied makes it possible to identify any ground cover.
The technique consists of an efficient and fast location of the image on the satellite, performing a
resampling, extraction of coverage indices and applying Artificial Intelligence algorithms for classification.
As an important part of the project, a platform was developed that uses artificial intelligence (AI).
Through this platform, researchers and partner institutions can carry out area analyses.
The platform performs the processing and provides a report of the desired region.
Click on an image to access the Platform
To disseminating the knowledge built in this project, the PhD Student Priscila Marques Kai taught an online mini-course on the techniques used. All material used in the course is available below.
Video Class
Video Class
Extra Files
Video Class Extra Files
Video Class Extra Files
Video Class Extra Files
