02475naa a2200313 a 450000100080000000500110000800800410001902400520006010000180011224501480013026000090027852015040028765000200179165000200181165000130183165000130184465300260185765300270188365300330191065300210194365300210196465300260198565300160201165300250202770000160205270000190206870000160208777300580210321432842023-07-15       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1007/s12065-022-00736-92DOI1 aFRANCO, V. R.  aHybrid machine learning methods combined with computer vision approaches to estimate biophysical parameters of pastures.h[electronic resource]  c2023  aWith population growth, the search for technologies that enable improvements in production respecting the environment and people?s health has become an essential point for society. In this context, this paper presents a study based on computer vision techniques and Machine Learning (ML) to extract information from pastures Panicum maximum cv. BRS Zuri to assist in the management and research on pasture conditions, possibilitando a obtenção de informações da. Computer vision aproaches are used to extract biophysical parameters from images acquired orthogonally from the canopy of vegetation. The extracted information serves as input for Machine Learning (ML) methods to predict pasture height and biomass. The contribution of this paper is developing a possible new solution compared to traditional methods in the large-scale study of plant biophysical parameters, which can be laborious and costly and sometimes depend on destructive harvesting. For this, three techniques were used: Support Vector Regression, Multi-Layer Perceptron (MLP), and Least Absolute Shrinkage and Selection. In addition, the Diferential Evolution technique was used to select the best model. Thirty independent runs of the Diferential Evolution technique were performed to assess the approach?s performance. The cross-validation method results show the MLP obtained the best results reaching an average of Coefcient of Determination (R2) equal 0.496 to estimate biomass and 0.656 to estimate the pasture height.  aComputer vision  aNeural networks  aPastures  aPastagem  aDiferential evolution  aEvolução diferencial  aEvolutionary model selection  aMachine learning  aModelo evolutivo  aParâmetro biofísico  aRede neural  aVisão computacional1 aHOTT, M. C.1 aANDRADE, R. G.1 aGOLIATT, L.  tEvolutionary Intelligencegv. 16, p. 1271-1284, 2023.