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dc.creatorSantos, Adão F.-
dc.creatorLacerda, Lorena N.-
dc.creatorRossi, Chiara-
dc.creatorMoreno, Leticia de A.-
dc.creatorOliveira, Mailson F.-
dc.creatorPilon, Cristiane-
dc.creatorSilva, Rouverson P.-
dc.creatorVellidis, George-
dc.date.accessioned2022-06-27T21:22:39Z-
dc.date.available2022-06-27T21:22:39Z-
dc.date.issued2021-12-
dc.identifier.citationSANTOS, A. F. et al. Using UAV and multispectral images to estimate peanut maturity variability on irrigated and rainfed fields applying linear models and artificial neural networks. Remote Sensing, Basel, v. 14, n. 1, 2022. DOI: https://doi.org/10.3390/rs14010093.pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/50368-
dc.description.abstractUsing UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.pt_BR
dc.languageenpt_BR
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)pt_BR
dc.rightsacesso abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceRemote Sensingpt_BR
dc.subjectRemote sensingpt_BR
dc.subjectVegetation indexpt_BR
dc.subjectArtificial intelligencept_BR
dc.subjectArachis hypogaea L.pt_BR
dc.subjectSensoriamento remotopt_BR
dc.subjectÍndice de vegetaçãopt_BR
dc.subjectInteligência artificialpt_BR
dc.subjectAmendoimpt_BR
dc.titleUsing UAV and multispectral images to estimate peanut maturity variability on irrigated and rainfed fields applying linear models and artificial neural networkspt_BR
dc.typeArtigopt_BR
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