AUTHOR PROFILE
ACADEMIC AFFILIATION
Everton is associated with Universidade Católica Dom Bosco, where he contributes to cutting-edge research in computer vision and its applications in agriculture and urban studies.
PRECISION AGRICULTURE RESEARCH
His research in precision agriculture includes the integration of UAV technology and machine learning to optimize farming practices. By improving weed and pest detection methods, his work supports sustainable agriculture and food security.
COMPUTER VISION IN AGRICULTURE
Everton's expertise in computer vision extends to various agricultural applications, from crop monitoring to automated harvesting systems. His innovative solutions help in increasing agricultural productivity and efficiency.
REAL-TIME WEED DETECTION IN SOYBEAN USING UAV IMAGES
Everton Castelão Tetila specializes in the real-time detection of weeds in soybean fields through the innovative use of UAV (Unmanned Aerial Vehicle) images. His work significantly contributes to precision agriculture, enabling farmers to identify and manage weeds more efficiently.
YOLO PERFORMANCE ANALYSIS FOR SOYBEAN PEST DETECTION
Everton has conducted extensive performance analysis of the YOLO (You Only Look Once) algorithm for the real-time detection of soybean pests. His research enhances pest management practices, ensuring timely interventions and reducing crop damage.
URBAN AREA CLASSIFICATION AND MONITORING USING COMPUTER VISION
He applies advanced computer vision techniques for the classification and monitoring of urbanized areas. This work aids in urban planning and development, providing detailed and accurate assessments of urban growth and infrastructure.
EDUCATIONAL CONTRIBUTIONS
He is dedicated to advancing education in his field, sharing his knowledge and findings through publications and presentations. His contributions help train the next generation of researchers and professionals in computer vision and its agricultural applications.
NOTABLE PUBLICATION
YOLO performance analysis for real-time detection of soybean pests
Authors: E.C. Tetila, F.A.G. da Silveira, A.B. da Costa, H. Pistori, J.G.A. Barbedo
Year: 2024
Journal: Smart Agricultural Technology, 7, 100405
Title: Classification and monitoring of urbanized areas using computer vision techniques | Classificação e monitoramento de áreas urbanizadas usando técnicas de visão computacional
Authors: E.C. Tetila, P.M. de Moraes, M. Constantino, M.M.D.M. Greco, H. Pistori
Year: 2023
Journal: Desenvolvimento e Meio Ambiente, 61, pp. 32–42
Title: An approach for applying natural language processing to image classification problems
Authors: G. Astolfi, D.A. Sant'Ana, J.V.D.A. Porto, E.T. Matsubara, H. Pistori
Year: 2022
Journal: Neurocomputing, 513, pp. 372–382
Title: Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
Authors: F.A.G. Silveira, E.C. Tetila, G. Astolfi, A.B. Costa, W.P. Amorim
Year: 2021
Conference: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13074 LNAI, pp. 265–279
Title: Associative classification model for forecasting stock market trends
Authors: E.C. Tetila, B.B. MacHado, J.F. Rorigues, M. Constantino, H. Pistori
Year: 2021
Journal: International Journal of Business Intelligence and Data Mining, 19(1), pp. 97–112