Next Article in Journal
Anaerobic Digestate and Carbon Dot Biostimulants: Nutrient Uptake Efficiency and Residual Effects on Corn (Zea mays L.) Vegetative Growth in Sandy Soils
Previous Article in Journal
UAV-Based Deep Learning for Weed Detection in Sugar Beet: A Case Study from Beni Mellal (Morocco) and Implications for Site-Specific Spraying
Previous Article in Special Issue
Hyperspectral Mapping of Pasture Nitrogen Content and Metabolizable Energy in New Zealand Hill Country Grasslands
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
AgriEngineering
Volume 8
Issue 7
10.3390/agriengineering8070261
agriengineering-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Unmanned Aerial Vehicle Remote Sensing and Machine Learning to Predict Productive and Physiological Traits of Forage Cactus in Semi-Arid Forage Systems

by
Ricardo Macedo da Silva
1,
Mario Adriano Ávila Queiroz
2,
Thieres George Freire da Silva
3,
Juliana Caroline Santos Santana
4,
Stela Antas Urbano
4,
Juliana Cantalino dos Santos
1,
Wagner Martins dos Santos
3,
Antonio Leandro Chaves Gurgel
5,
Felipe Pontes Teixeira das Chagas
4,
Fábio dos Anjos Rezende
1 and
João Virgínio Emerenciano Neto
2,4,*
1
Federal Institute of Education, Science and Technology of the Sertão Pernambucano, Petrolina 56300-000, PE, Brazil
2
Agricultural Sciences Campus, Federal University of Vale São Francisco, Petrolina 56300-000, PE, Brazil
3
Federal Rural University of Pernambuco, Serra Talhada 56909-535, PE, Brazil
4
Academic Unit Specialized in Agrarian Sciences, Federal University of Rio Grande do Norte, Macaíba 59280-000, RN, Brazil
5
Campus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus 64900-000, PI, Brazil
*
Author to whom correspondence should be addressed.
AgriEngineering 2026, 8(7), 261; https://doi.org/10.3390/agriengineering8070261 (registering DOI)
Submission received: 17 April 2026 / Revised: 18 June 2026 / Accepted: 23 June 2026 / Published: 25 June 2026
(This article belongs to the Special Issue Advancements in Remote Sensing and AI-Driven Analytics for Sustainable Agriculture)
Versions Notes

Abstract

The use of nondestructive technologies combined with machine learning has emerged as a promising approach for estimating structural and productive traits in agricultural systems. This study evaluated the potential of Unmanned Aerial Vehicle (UAV) imagery integrated with the Random Forest algorithm to predict structural, physiological and productive variables of forage cactus cultivated under semi-arid conditions. The experiment was conducted over two years using four varieties: Orelha de Elefante Mexicana (OEM), Miúda, IPA Sertânia and IPA 20. RGB and red–green–near-infrared (RGNir) orthomosaics, along with a digital elevation model, were used to derive spectral and structural variables, which were related to field measurements. Model performance was assessed using the coefficient of determination (R2). The models showed high predictive performance for dry mass production, particularly for OEM, IPA Sertânia and IPA 20 (R2 = 0.85, 0.85 and 0.83). Physiological variables, such as chlorophyll A and B, also showed consistent fits (R2 = 0.70 and 0.83), while structural variables, including height and volume, exhibited lower stability. Differences among varieties affected model accuracy, especially for Miúda, due to its architectural characteristics. The integration of UAV imagery and machine learning provides a reliable approach for monitoring forage cactus, although model performance depends on plant structure.
Keywords: chlorophyll; digital elevation model; nopalea; opuntia; orthophotomosaic; remote sensing; yield chlorophyll; digital elevation model; nopalea; opuntia; orthophotomosaic; remote sensing; yield

Share and Cite

MDPI and ACS Style

Silva, R.M.d.; Queiroz, M.A.Á.; Silva, T.G.F.d.; Santana, J.C.S.; Urbano, S.A.; Santos, J.C.d.; Santos, W.M.d.; Gurgel, A.L.C.; Chagas, F.P.T.d.; Rezende, F.d.A.; et al. Unmanned Aerial Vehicle Remote Sensing and Machine Learning to Predict Productive and Physiological Traits of Forage Cactus in Semi-Arid Forage Systems. AgriEngineering 2026, 8, 261. https://doi.org/10.3390/agriengineering8070261

AMA Style

Silva RMd, Queiroz MAÁ, Silva TGFd, Santana JCS, Urbano SA, Santos JCd, Santos WMd, Gurgel ALC, Chagas FPTd, Rezende FdA, et al. Unmanned Aerial Vehicle Remote Sensing and Machine Learning to Predict Productive and Physiological Traits of Forage Cactus in Semi-Arid Forage Systems. AgriEngineering. 2026; 8(7):261. https://doi.org/10.3390/agriengineering8070261

Chicago/Turabian Style

Silva, Ricardo Macedo da, Mario Adriano Ávila Queiroz, Thieres George Freire da Silva, Juliana Caroline Santos Santana, Stela Antas Urbano, Juliana Cantalino dos Santos, Wagner Martins dos Santos, Antonio Leandro Chaves Gurgel, Felipe Pontes Teixeira das Chagas, Fábio dos Anjos Rezende, and et al. 2026. "Unmanned Aerial Vehicle Remote Sensing and Machine Learning to Predict Productive and Physiological Traits of Forage Cactus in Semi-Arid Forage Systems" AgriEngineering 8, no. 7: 261. https://doi.org/10.3390/agriengineering8070261

APA Style

Silva, R. M. d., Queiroz, M. A. Á., Silva, T. G. F. d., Santana, J. C. S., Urbano, S. A., Santos, J. C. d., Santos, W. M. d., Gurgel, A. L. C., Chagas, F. P. T. d., Rezende, F. d. A., & Emerenciano Neto, J. V. (2026). Unmanned Aerial Vehicle Remote Sensing and Machine Learning to Predict Productive and Physiological Traits of Forage Cactus in Semi-Arid Forage Systems. AgriEngineering, 8(7), 261. https://doi.org/10.3390/agriengineering8070261

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop