Caio Bruno Wetterich
University of São Paulo
13 Papers
14 Citations
Caio Bruno Wetterich is an academic researcher from University of São Paulo. The author has contributed to research in topics: Citrus canker & Computer science. The author has an hindex of 4, co-authored 10 publications.
Chat about Author
Papers
Detection of citrus canker and Huanglongbing using fluorescence imaging spectroscopy and support vector machine technique
TL;DR: This work combined fluorescence imaging spectroscopy and a machine learning technique to discriminate between citrus canker and Huanglongbing and shows that it is possible to accurately identify citrus diseases that present similar symptoms.
A Comparative Study on Application of Computer Vision and Fluorescence Imaging Spectroscopy for Detection of Citrus Huanglongbing Disease in USA and Brazil
Caio Bruno Wetterich,Ratnesh Kumar,Sindhuja Sankaran,José Belasque Junior,Reza Ehsani,Luís Gustavo Marcassa +5 more
TL;DR: Fluorescence imaging spectroscopy is used to investigate Huanglongbing disease in USA and Brazil and results show differences between leaves collected in Brazil and USA.
14
Multiclass classifier based on deep learning for detection of citrus disease using fluorescence imaging spectroscopy
TL;DR: In this paper , the authors combined fluorescence imaging spectroscopy (FIS) and supervised learning methods to identify and discriminate between citrus canker, Huanglongbing, and other leaf symptoms.
6
Predictive models of chlorophyll content in sugarcane seedlings using spectral images
Nelson Oliveros,Rodolpho Cesar dos Reis Tinini,Daniel dos Santos Costa,Rodrigo Pereira Ramos,Caio Bruno Wetterich,Bárbara Teruel +5 more
TL;DR: In this paper, the spectral image processing-based method can provide a chlorophyll measurement equivalent to SPAD, with the advantage of having a higher spatial coverage over the entire leaf area.
Fluorescence spectral study in grapes (Vitis Vinifera L.) Benitaka variety to different solid soluble values
Rodolpho Cesar dos Reis Tinini,Caio Bruno Wetterich,Luís Gustavo Marcassa,Danilo G. Figueiredo,Bárbara Teruel +4 more
TL;DR: In this article, the authors used principal component analysis (PCA) to distinguish Benitaka variety grapes with different concentrations of soluble solids using spectral image of fluorescence, which can contribute to technological advancement in the nondestructive evaluation field and real-time in high added value products such as derivatives Viticulture.
1