https://www.taaeufla.eeng.ufla.br/index.php/TAAE/issue/feed 2024-02-17T17:03:06-03:00 Marcelo de Carvalho Alves marcelocarvalhoalves@gmail.com Open Journal Systems <p>Theoretical and Applied Engineering (TAAE) covers all aspects and information on scientific and technical advances in engineering sciences and publishes contributions on all aspects of Geomatics, Mechanical Engineering, Control, Automation and Systems, Sanitary and Environmental Engineering, Water Resources, Environmental Earth Sciences, Materials Engineering, Chemical Engineering, Civil Engineering, Architecture, Computational Engineering, Physical Engineering, Meteorology and climatology, Agricultural Engineering, Agronomy, Forest Engineering, Food Engineering, Safety and Security Engineering, Rural Constructions and Environment, Animal Production.</p> https://www.taaeufla.eeng.ufla.br/index.php/TAAE/article/view/56 2024-01-18T10:08:57-03:00 Flávio Vanoni de Carvalho Júnior flavio.vanoni@gmail.com Marcelo de Carvalho Alves marcelo.alves@ufla.br Gabrielly Carvalho de Souza gcsouza003@gmail.com Fortunato Silva de Menezes fmenezes@ufla.br

<p>Imagery from sensors embedded in satellites enables low-cost crop analysis and has been the subject of correlation studies between vegetation indices and productivity. Vegetation indices obtained from orbital platforms and crop maps have been important tools in the context of popularizing precision agriculture. However, there are many factors that affect maize yields and the resulting harvest maps. As a result, correlations between vegetation indices and yields are not always obtained. This leaves a gap for methodologies to identify areas of non-correlation and investigate the possible causes in a targeted manner. The aim of this study was to use freely available satellite images, together with yield data from a maize harvester, to identify regions with and without a correlation between yields and vegetation indices. In areas with correlation, a linear model of yield as a function of NDVI was obtained. A map of discrepancies was calculated, in which most of the crop was correlated, with yields varying by around 2 Mg ha⁻¹ in relation to the model. Areas with discrepant yields were identified, both negatively and positively in relation to the model, enabling a localized investigation into the possible causes of the phenomenon and crop management.</p>

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