He holds a degree in Geography (2008) from São Paulo State University "Júlio de Mesquita Filho" (Unesp), as well as a Master's degree (2011) and a PhD (2017) in Geosciences and Environment from the same institution. He has worked on projects in the Oil and Gas sector funded by ANP and Petrobras. As a researcher, he coordinated two FAPESP PIPE projects focused on developing tools to optimize agricultural production through the use of satellite imagery and Machine Learning techniques. He is currently a data scientist at IDGeo, where he works on the development and improvement of the company's products. His main areas of interest include Python, Artificial Intelligence, Machine Learning, open-source tools, and Geographic Information Systems (GIS). (Source: Lattes Curriculum)
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There is a growing concern among sugarcane producers to improve the productivity and reduce losses due to infestations of pests, diseases, weeds and failures derived from mechanized operations, which directly affects the development of the crop. The current methods for identifying areas of low productivity are based mainly on images collected by UAV (Unmanned Aerial Vehicles). However, th…
The development of new products and techniques that reduce production costs is a permanent concern of sugarcane producers. Production losses related to delayed control of diseases or pests can cause decreases in the order of 85% of the productive potential of the plots. Therefore, the early diagnosis of the problem is fundamental to improve pest control procedures in the field, especially…
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