Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles

Gabriel Italo Novaes da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Wagner Martins dos Santos, Alan Cézar Bezerra, Elisiane Alba, Marcos Vinícius da Silva, Jhon Lennon Bezerra da Silva, Luciana Sandra Bastos de Souza, Gabriel Thales Barboza Marinho, Abelardo Antônio de Assunção Montenegro and Thieres George Freire da Silva ()
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Gabriel Italo Novaes da Silva: Department of Agricultural Engineering, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, s/n, Dois Irmãos, Recife 52171-900, PE, Brazil
Alexandre Maniçoba da Rosa Ferraz Jardim: Department of Biodiversity, Institute of Biosciences, São Paulo State University—UNESP, Avenue 24A, 1515, Rio Claro 13506-900, SP, Brazil
Wagner Martins dos Santos: Department of Agricultural Engineering, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, s/n, Dois Irmãos, Recife 52171-900, PE, Brazil
Alan Cézar Bezerra: Academic Unit of Serra Talhada, Federal Rural University of Pernambuco, Gregório Ferraz Nogueira Avenue, s/n, Serra Talhada 56909-535, PE, Brazil
Elisiane Alba: Academic Unit of Serra Talhada, Federal Rural University of Pernambuco, Gregório Ferraz Nogueira Avenue, s/n, Serra Talhada 56909-535, PE, Brazil
Marcos Vinícius da Silva: Department of Forest Engineering, Federal University of Campina Grande—UFCG, Patos 58708-110, PB, Brazil
Jhon Lennon Bezerra da Silva: Cerrado Irrigation Graduate Program, Goiano Federal Institute—Campus Ceres, GO-154, km 218–Zona Rural, Ceres 76300-000, GO, Brazil
Luciana Sandra Bastos de Souza: Academic Unit of Serra Talhada, Federal Rural University of Pernambuco, Gregório Ferraz Nogueira Avenue, s/n, Serra Talhada 56909-535, PE, Brazil
Gabriel Thales Barboza Marinho: Department of Agricultural Engineering, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, s/n, Dois Irmãos, Recife 52171-900, PE, Brazil
Abelardo Antônio de Assunção Montenegro: Department of Agricultural Engineering, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, s/n, Dois Irmãos, Recife 52171-900, PE, Brazil
Thieres George Freire da Silva: Department of Agricultural Engineering, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, s/n, Dois Irmãos, Recife 52171-900, PE, Brazil

Agriculture, 2024, vol. 14, issue 12, 1-16

Abstract: The objective of this study was to correlate the biophysical parameters of forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them with machine learning in different agricultural systems. Four experimental units were conducted. Units I and II had different plant spacings (0.10, 0.20, 0.30, 0.40, and 0.50 m) with East–West and North–South planting directions, respectively. Unit III had row spacings (1.00, 1.25, 1.50, and 1.75 m), and IV had cutting frequencies (6, 9, 12 + 6, and 18 months) with the clones “Orelha de Elefante Mexicana”, “Miúda”, and “IPA Sertânia”. Plant height and width, cladode area index, fresh and dry matter yield (FM and DM), dry matter content, and fifteen vegetation indices of the visible range were analyzed. The RGBVI and E x GR indices stood out for presenting greater correlations with FM and DM. The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha −1 in experimental units I and II, III, and IV, respectively. The results showed potential in the application of machine learning with RGB images for predictive analysis of the biophysical parameters of forage cactus.

Keywords: automated procedures; E x GR; forage cactus; RGBVI; Random Forest; visible vegetation indices (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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