Alberdan José da Silva Teodoro
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Alberdan José da Silva Teodoro has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Enrico Palchetti
Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine 18, 50144 Firenze, Italy
1 shared publication - Gabriel Araújo e Silva Ferraz
Agricultural Engineering Department, Federal University of Lavras, Lavras 37200-000, Brazil
1 shared publication - Giuseppe Rossi
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
1 shared publication - Lucas Santos Santana
Institute of Agricultural Sciences (ICA), Federal University from Jequitinhonha and Mucuri’s Valleys (UFVJM), Av. Universitária, n°1000, Unaí 38610-000, MG, Brazil
1 shared publication - Mozarte Santos Santana
Institute of Natural Sciences, Federal University of Lavras, P.O. Box 3037, Lavras 37200-900, MG, Brazil
1 shared publication
Latest Publications
- Open Access
Advances in Precision Coffee Growing Research: A Bibliometric Review
Precision coffee-growing technologies contribute to increased yield, operational efficiency, and final product quality. In addition, they strengthen coffee growing in the global agricultural scenario, which makes this activity increasingly competitive. Scientific research is essential for technological development and offering security regarding its application. For relevant research identification, bibliometric revision methods expose the best studies and their relationships with countries and authors, providing a complete map of research directions. This study identified the main contributions and contributors to academic research generation about precision coffee growing from 2000 to 2021. Bibliometric analysis was performed in VOSViewer software from the referential bases Scopus and Web of Science that identified 150 articles. Based on the number of citations, publications about precision coffee-growing showed Brazilian institutions at the top of the list, and Brazil’s close relationships with North American and South African institutions. Geostatistical analysis, remote sensing and spatial variability mapping of cultivation areas were used in most experimental research. A trend in research exploring machine learning technologies and autonomous systems was evident. The identification of the main agents of scientific development in precision coffee growing contributes to objective advances in the development and application of new management systems. Overall, this analysis represents wide precision coffee growing research providing valuable information for farmers, policymakers, and researchers.
Open AccessAdvances in Precision Coffee Growing Research: A Bibliometric Review
Precision coffee-growing technologies contribute to increased yield, operational efficiency, and final product quality. In addition, they strengthen coffee growing in the global agricultural scenario, which makes this activity increasingly competitive. Scientific research is essential for technological development and offering security regarding its application. For relevant research identification, bibliometric revision methods expose the best studies and their relationships with countries and authors, providing a complete map of research directions. This study identified the main contributions and contributors to academic research generation about precision coffee growing from 2000 to 2021. Bibliometric analysis was performed in VOSViewer software from the referential bases Scopus and Web of Science that identified 150 articles. Based on the number of citations, publications about precision coffee-growing showed Brazilian institutions at the top of the list, and Brazil’s close relationships with North American and South African institutions. Geostatistical analysis, remote sensing and spatial variability mapping of cultivation areas were used in most experimental research. A trend in research exploring machine learning technologies and autonomous systems was evident. The identification of the main agents of scientific development in precision coffee growing contributes to objective advances in the development and application of new management systems. Overall, this analysis represents wide precision coffee growing research providing valuable information for farmers, policymakers, and researchers.