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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

Author

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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
    Academic Unit of Serra Talhada, Federal Rural University of Pernambuco, Gregório Ferraz Nogueira Avenue, s/n, Serra Talhada 56909-535, PE, Brazil)

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.

Suggested Citation

  • 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 & Lu, 2024. "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," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:12:p:2166-:d:1531595
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    References listed on IDEAS

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    1. Héctor García-Martínez & Héctor Flores-Magdaleno & Roberto Ascencio-Hernández & Abdul Khalil-Gardezi & Leonardo Tijerina-Chávez & Oscar R. Mancilla-Villa & Mario A. Vázquez-Peña, 2020. "Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-24, July.
    2. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
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