Precision Agriculture: technological monitoring based on patent analysis




Smart agriculture; Technological mapping; Patentometric analysis; Indicators.


Precision Farming systems adopt various high-tech equipment in hardware, software and communication to collect data from different sources in order to measure and evaluate all aspects of agricultural production. This management strategy allows the rationalization in the use of agricultural resources, increasing production efficiency while reducing the negative impacts of the activity in the field. This study aimed to monitor the technologies associated with Precision Farming, from the analysis of information from patent documents, in order to investigate the status and trends, as well as to identify the main providers and their collaboration networks. Research questions were formulated to guide the study, and this study is both quantitative and descriptive, and qualitative and exploratory in nature. The study also provides a methodological flow supported by free software tools for data collection, processing and visualization. Among the main results, the following stand out: 312 patent families filed by 303 applicants and 968 inventors were identified; 86% of the patent publications occurred in the last decade with an increasing trend; technologies were filed in 23 countries, with China and the United States standing out; about 86% of the holders and inventors own only one patent; planting, data processing and soil property analysis are the most developed technological sectors; highly cited patents; main inventors participate in a more cooperative way than the holders, being bridges between different groups of inventors.


Abbas, A., Zhang, L., & Khan, S. U. (2014). A literature review on the state-of-the-art in patent analysis. World Patent Information, 37, 3–13.

Adamchuk, V. ., Hummel, J. ., Morgan, M. ., & Upadhyaya, S. . (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44(1), 71–91.

Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 361–362.

Batagelj, V., & Mrvar, A. (1998). Pajek-program for large network analysis. Connections, 21, 47–57. /Networks/doc/pajek.pdf

Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80(4), 571–583.

Carrot2. (2021). Carrot2 clustering engine.

Chang, P.-L., Wu, C.-C., & Leu, H.-J. (2010). Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display. Scientometrics, 82(1), 5–19.

Cisternas, I., Velásquez, I., Caro, A., & Rodríguez, A. (2020). Systematic literature review of implementations of precision agriculture. Computers and Electronics in Agriculture, 176(July), 105626.

Demsar, J., Curk, T., Erjavec, A., Gorup, C., Hocevar, T., Milutinovic, M., Mozina, M., Polajnar, M., Toplak, M., Staric, A., Stajdohar, M., Umek, L., Zagar, L., Zbontar, J., Zitnik, M., & Zupan, B. (2013). Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research, 14(Aug), 2349−2353.

Ham, K. (2013). OpenRefine (version 2.5). Free, open-source tool for cleaning and transforming data. Journal of the Medical Library Association : JMLA, 101(3), 233–234.

Kent Shannon, D., Clay, D. E., & Sudduth, K. A. (2018). An Introduction to Precision Agriculture (p. 1–12).

Lotka, A. J. (1926). The freq distrib of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.

Martínez, C. (2011). Patent families: When do different definitions really matter? Scientometrics, 86(1), 39–63.

Masiakowski, P., & Wang, S. (2013). Integration of software tools in patent analysis. World Patent Information, 35(2), 97–104.

Osiński, S., Stefanowski, J., & Weiss, D. (2004). Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition. In Intelligent Information Processing and Web Mining (Vol. 20, Número 3, p. 359–368). Springer Berlin Heidelberg.

Pereira, J. M. (2016). Manual de Metodologia da Pesquisa Científica (4o ed). Atlas.

Precision Agriculture in the 21st Century. (1997). Precision Agriculture in the 21st Century. National Academies Press.

Silva, W. de V. R. da, Adelino, M. A., Silva, M. V. da, Silva, F. C. da, & Silva-Mann, R. (2020). Análise da produção científica e tecnológica acerca da Ciência Forense Digital. Research, Society and Development, 9(11), e45391110224.

Silva, W. de V. R. da, & Silva-Mann, R. (2020). Agricultura de Precisão no Brasil: conjuntura atual, desafios e perspectivas. Research, Society and Development, 9(11), e1979119603.

Silva, W. de V. R. da, & Silva-Mann, R. (2021). Precision Agriculture under a bibliometric view. International Journal for Innovation Education and Research, 9(11), 422–442.

Sternitzke, C., Bartkowski, A., & Schramm, R. (2008). Visualizing patent statistics by means of social network analysis tools. World Patent Information, 30(2), 115–131.

Tableau. (2021). Tableau Public. SAGE Publications, Ltd.

The Lens. (2021). About The Lens.

WIPO. (2016). The WIPO Manual on Open Source Patent Analytics.

Yang, Z. K., Lin, D. M., & Xu, M. Z. (2014). The Re-applicability Explore of Lotka’s Law in Patent Documents. Collnet Journal of Scientometrics and Information Management, 8(1), 183–191.



How to Cite

SILVA, W. de V. R. da; SILVA-MANN, R. Precision Agriculture: technological monitoring based on patent analysis. Research, Society and Development, [S. l.], v. 11, n. 3, p. e42611326852, 2022. DOI: 10.33448/rsd-v11i3.26852. Disponível em: Acesso em: 24 sep. 2023.



Agrarian and Biological Sciences