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

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

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 di...

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Main Authors: 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, Thieres George Freire da Silva
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Agriculture
Subjects:
automated procedures
E<i>x</i>GR
forage cactus
RGBVI
Random Forest
visible vegetation indices
Online Access:https://www.mdpi.com/2077-0472/14/12/2166
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Summary: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<i>x</i>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<sup>−1</sup> 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.
ISSN:2077-0472

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