UAV-based spectral images using remote sensing and YOLOv8 in Eucalyptus saligna Sm. inventory

Authors

DOI:

https://doi.org/10.5902/1980509888522

Keywords:

Artificial intelligence, Precision forestry, Individual tree detection

Abstract

Accurate and low-cost tree inventories in forest plantations are essential for an effective production management. Stimulated by recent advancements in Unmanned Aerial Vehicle (UAV) imagery coupled with artificial intelligence, and by the interest in developing models capable of supporting decision-making on silvicultural and forest management, this study aimed to evaluate the performance of different vegetation indices in detecting Eucaliptus saligna individuals by using an improved deep learning model. The tree-individual detection model was created using the YOLOv8n algorithm using UAV RGB images and vegetation indices (VI) generated from the multispectral sensor onboard the UAV. Nine VIs were selected for training (65%) and testing (35%) the models. The proposed framework demonstrated that the MPRI, PSRI, and NDVI indices achieved an F1 score of 0.98 and a precision of 0.97 for detecting E. saligna individual trees six months after planting. Our study demonstrates the robustness of the proposed framework and recommends the application of the MPRI index in individual tree detection due to its efficient performance, cost-effectiveness, and simplicity, as it only utilizes regions of the visible spectrum.

Downloads

Download data is not yet available.

Author Biographies

Vinicius Richter, Federal University of Santa Maria

Graduado em Engenharia Florestal e atualmente cursando Mestrado em Engenharia Florestal com especialização em crescimento e produção florestal pela Universidade Federal de Santa Maria. Atua nas áreas de inteligência artificial, visão computacional, programação Python, inventário florestal, sensoriamento remoto e manejo florestal.

Max Vinicius Reis de Sousa, Federal University of Santa Maria

Forestry Engineer from the Federal University of Tocantins (UFT), Master's student in the Graduate Program in Forest Engineering at the Federal University of Santa Maria (UFSM). Has experience in Production Planning and Control, Forest Inventory and Measurement, and Programming in R Language.

Renato Souza Santos, Federal University of Santa Maria

Graduated in Forest Engineering from the Federal University of Santa Maria, campus Frederico Westphalen (UFSM). He is currently a Master's student in Forest Engineering with a focus on Forest Management in the Postgraduate Program in Forest Engineering (PPGEF) at the Federal University of Santa Maria (UFSM).

Matheus Morais Ziembowicz, Federal University of Santa Maria

Forest engineer graduated from the Federal University of Santa Maria, holds a master's degree in forest engineering from the State University of Santa Catarina, and is currently a doctoral student in forest engineering at the Federal University of Santa Maria. He has experience in Remote Sensing, Geoprocessing, Geographic Information Systems, Forest Management, and related areas.

Juliane Cardozo Rigão, Federal University of Santa Maria

Graduated in Forest Engineering and currently studying for a Master's degree in Forest Engineering in the area of ecological restoration at the Federal University of Santa Maria. She is a member of the UFSM Center for Studies and Research into the Recovery of Degraded Areas (NEPRADE).

Norton Borges Júnior, CMPC Celulose Riograndense

He holds a degree in Forestry Engineering from the Federal University of Santa Maria (2001). He has a master's degree in plant production from the Federal University of Rio Grande do Sul (UFRGS). He is currently a researcher at CMPC Celulose Riograndense LTDA. He has experience in the area of Vegetative Propagation of Plants, Water Resources and Forest Protection. Management of eucalyptus pests and diseases, research in Irrigation and Water Management, Environmental Sciences and Agricultural Plant Sciences.

Lúcio de Paula Amaral, Federal University of Santa Maria

Adjunct Professor at UFSM - Department of Rural Engineering - Center for Rural Sciences. He works as a Professor in the area of geomatics in undergraduate courses in Agronomy, Forestry Engineering, Architecture and Urbanism, and in the Professional Master's Course in Precision Agriculture (PPGAP-Colégio Politécnico da UFSM). He is a Forest Engineer, graduated from the Faculty of Agricultural Sciences-UNESP/Botucatu-SP, Specialist in Geomatics - PG-Geomática/UFSM, Master in Agronomy - Plant Production, Postgraduate Program in Agronomy at the State University of the Center-West - UNICENTRO ,Guarapuava-PR, Master in Precision Agriculture, Postgraduate Program in Precision Agriculture, Polytechnic College of UFSM, and PhD in Forestry Engineering from the Federal University of Santa Maria - UFSM, PPGEF. He has experience in the area of Forest Resources and Forest Engineering, with an emphasis on geoprocessing, GIS and geostatistics, working mainly on the following topics: use of geographic information systems, remote sensing products, thematic mapping, GNSS positioning, use of aromatic forest species to obtain essential oils as bioactive agents in pest control, production and planting of native and exotic forest species, technical reports and expertise, forest inventory, sewage sludge, biosolids, biodigester.

Sally Deborah Pereira da Silva, Federal University of Santa Maria

Graduated in Forestry Engineering from the Federal University of the State of Pará (UEPA). He has a master's degree in forestry engineering and is currently pursuing a doctorate in forestry engineering in the postgraduate program in forestry engineering (PPGEF) at the Federal University of Santa Maria (UFSM). He has experience in Remote Sensing, geoprocessing, artificial intelligence and precision agriculture.

Jorge Carneiro Amado, Federal University of Santa Maria

He has a degree in Agronomy from UFSM (1982), a master's degree and doctorate (1997) in Soil Science from the Federal University of Rio Grande do Sul (UFRGS) and from Auburn University, USA - sandwich doctorate (1997). Postdoctoral at Kansas State University (KSU), USA (2008). CNPq scholarship holder since 1988. He is currently a full professor at UFSM and an adjunct professor at KSU. He taught the Tropical Soil Management discipline at KSU every two years, in person and online. He has experience in the Soil Management and Conservation area, working on the following topics: direct planting, nitrogen, organic matter, cover crops, carbon balance and precision agriculture. He has been technical coordinator of the Aquarius Precision Agriculture (AP) Project since 2004

References

ABDOLLAHNEJAD, A.; PANAGIOTIDIS, D. Tree species classification and health status assessment for a mixed broadleaf-conifer forest with UAS multispectral imaging. Remote Sensing, v. 12, n. 22, p. 3722, 2020. DOI: https://doi.org/10.3390/rs12223722

ALVARES, C. A. et al. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, n. 6, p. 711–728, 2013. DOI: https://doi.org/10.1127/0941-2948/2013/0507

BARBOSA, B. et al. RGB vegetation indices applied to grass monitoring: a qualitative analysis. Agricultural Research, v. 17, n. 2, p. 349–357, 2019. DOI: https://doi.org/10.15159/ar.19.119

BARNES, E. M. et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In: Proceedings of the fifth international conference on precision agriculture, Bloomington, MN, USA. 2000.

BOCHKOVSKIY, A.; WANG, C.-Y.; LIAO, H.-Y.M. YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv, 2020. Available at: https://arxiv.org/abs/2004.10934

CHEN, Y. et al. An efficient approach to monitoring pine wilt disease severity based on random sampling plots and UAV imagery. Ecological Indicators, v. 156, p. 111215, 2023. DOI: https://doi.org/10.1016/j.ecolind.2023.111215

CHOI, K. et al. An automatic approach for tree species detection and profile estimation of urban street trees using deep learning and Google Street view images. ISPRS Journal of Photogrammetry and Remote Sensing, v. 190, p. 165-180, 2022. DOI: https://doi.org/10.1016/j.isprsjprs.2022.06.004

DUARTE, Marciel Lelis; RIBEIRO, Aristides. Influência do El Niño e La Niña na produtividade de plantios de Eucalipto em distintas regiões no Brasil. Ciência Florestal, v. 33, p. e61334, 2023. DOI: https://doi.org/10.5902/1980509861334

DUTTA, A.; ZISSERMAN, A. The VIA annotation software for images, audio and video. In: Proceedings of the 27th ACM international conference on multimedia. Anais... 2019. p. 2276-2279.

EUGENIO, F. C. et al. Estimation of soybean yield from machine learning techniques and multispectral rpas imagery. Remote Sensing Applications: Society and Environment, v. 20, p. 100397, nov. 2020. DOI: https://doi.org/10.1016/j.rsase.2020.100397

FAO. Global Forest Resources Assessment 2020. 2020. DOI: https://doi.org/10.4060/ca9825en

GITELSON, A. A. et al. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, v. 30, n. 5, 2003. DOI: https://doi.org/10.1029/2002gl016450

GITELSON, A. A.; GRITZ, Y.; MERZLYAK, M. N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, v. 160, n. 3, p. 271–282, 2003. DOI: https://doi.org/10.1078/0176-1617-00887

GONÇALVES, H.; EMBRAPA SOLOS. Sistema brasileiro de classificação de solos. Embrapa, 2018.

GUO, B. et al. Detection of the Grassland Weed Phlomoides umbrosa Using Multi-Source Imagery and an Improved YOLOv8 Network. Agronomy, v. 13, n. 12, p. 3001, 2023. DOI: https://doi.org/10.3390/agronomy13123001

HUANG, S. et al. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, v. 32, n. 1, p. 1–6, 2020. DOI: https://doi.org/10.1007/s11676-020-01155-1

INSTITUTO BRASILEIRO DE ÁRVORES. IBÁ. Relatório Anual. Available at: https://iba.org/datafiles/publicacoes/relatorios/relatorio-anual-iba2022-compactado.pdf. Accessed in: 2022.

IOST FILHO, F. H. et al. Drones: innovative technology for use in precision pest management. Journal of Economic Entomology, v. 113, n. 1, p. 1–25, 8 fev. 2020. DOI: https://doi.org/10.1093/jee/toz268

JEMAA, H. et al. UAV-based computer vision system for orchard apple tree detection and health assessment. Remote Sensing, v. 15, n. 14, p. 3558, 2023. DOI: https://doi.org/10.3390/rs151435587

JINTASUTTISAK, T.; EDIRISINGHE, E.; ELBATTAY, A. Deep neural network-based date palm tree detection in drone imagery. Computers and Electronics in Agriculture, v. 192, p. 106560, 2022. DOI: https://doi.org/10.1016/j.compag.2021.106560

JOSEPH, R. et al. You Only Look Once: Unified, Real-Time Object Detection. arXiv (Cornell University), 2016. DOI: https://doi.org/10.48550/arxiv.1506.02640

KATTENBORN, T. et al. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, v. 173, p. 24-49, 2021. DOI: https://doi.org/10.1016/j.isprsjprs.2020.12.010

KOPACKOVÁ-STRNADOVÁ, V. et al. Canopy top, height and photosynthetic pigment estimation using parrot sequoia multispectral imagery and the unmanned aerial vehicle (UAV). Remote Sensing, v. 13, p. 705, 2021. DOI: https://doi.org/10.3390/rs13040705

LAFETÁ, Bruno Oliveira et al. Eficiência de utilização de macronutrientes em eucalipto por método não destrutivo estimados por redes neurais artificiais. Ciência Florestal, v. 28, n. 2, p. 613-623, 2018. DOI: http://dx.doi.org/10.5902/1980509832049

LIN, T. Y. et al. Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V. Anais... Springer International Publishing, 2014. p. 740-755. DOI: https://doi.org/10.1007/978-3-319-10602-1_48

LING, Q.; ISA, N. A. M.; ASAARI, M. S. M. Precise Detection for Dense PCB Components Based on Modified YOLOv8. IEEE Access, v. 11, p. 116545-116560, 2023. DOI: 10.1109/ACCESS.2023.3325885.

MA, Y. K. et al. Object detection of individual mangrove based on improved YOLOv5. Laser Optoelectron. Prog, v. 59, p. 436-446, 2022. DOI: 10.3788/LOP202259.1828003.

MALABAD, A. M. et al. A combined approach utilizing UAV 3d imaging methods, in-situ measurements, and laboratory experiments to assess water evaporation and trace element uptake by tree species growing in a red gypsum landfill. Journal of Hazardous Materials, v. 425, p. 127977, 2022. DOI: 10.1016/j.jhazmat.2021.127977.

MCFEETERS, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, v. 17, n. 7, p. 1425–1432, 1996. DOI: https://doi.org/10.1080/01431169608948714

MERZLYAK, M. N. et al. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, v. 106, n. 1, p. 135–141, 199

MICA SENSE. MicaSense RedEdge™ 3 multispectral camera user manual. Seattle: MicaSense, 2015. 33 p. Rev 06, October 2015. Available at: https://support.micasense.com/hc/en-us/articles/215261448-RedEdge-User-Manual-PDF-Download. Accessed in: 28th Nov. 2019.

REDMON, J.; FARHADI, A. YOLO9000: Better, Faster, Stronger. arXiv, 2016, arXiv:1612.08242.

REDMON, Joseph; FARHADI, Ali. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.

ROUSE, R.; HAAS, J.; DEERING, D. Monitoring vegetation systems in the great plains with erts. Available at: https://ntrs.nasa.gov/api/citations/19740022614/downloads/19740022614.pdf

ȘANDRIC, I., et al. Tree's detection & health's assessment from ultra-high resolution UAV imagery and deep learning. Geocarto International, v. 37, n. 25, p. 10459-10479, 2022. https://doi.org/10.1080/10106049.2022.2036824

SIKATI, J.; NOUAZE, J. C. YOLO-NPK: A Deep Lightweight Network for Lettuce Nutrient Deficiency Classification Based on Improved YOLOv8 Nano. Engineering Proceedings, v. 58, n. 1, p. 31, 2023. DOI: https://doi.org/10.3390/ecsa-10-16256

TANNURE FARIA, Júlio Cézar et al. Manejo da densidade de plantas durante a produção de mudas em viveiro.Ciência Florestal, v. 29, n. 3, 2019. DOI: https://doi.org/10.5902/1980509830030

TZUTALIN. LabelImg. Git code, 2015. Available at: https://github.com/tzutalin/labelImg

VAHL DE PAULA, B.; SQUIZANI ARRUDA, W.; ETIENNE PARENT, L.; FRANK DE ARAUJO, E.; BRUNETTO, G. Nutrient diagnosis of Eucalyptus at the factor-specific level using machine learning and compositional methods. Plants, v. 9, n. 8, p. 1049, 2020. DOI: https://doi.org/10.3390/plants9081049

VINCINI, M.; FRAZZI, E.; D’ALESSIO, P. Comparison of narrow-band and broad-band vegetation indices for canopy chlorophyll density estimation in sugar beet. In: Precision agriculture'07. Wageningen Academic, 2007. p. 189-196.

WANG, F., et al. Rice yield estimation using parcel-level relative spectral variables from UAV-based hyperspectral imagery. Frontiers in Plant Science, v. 10, p. 453, 2019. DOI: https://doi.org/10.3389/fpls.2019.00453

XIANGSHU, X. I.; KAI, X. I. A.; YINHUI, Y. A. N. G.; XIAOCHEN, D. U.; HAILIN, F. E. N. G. Urban individual tree crown detection research using multispectral image dimensionality reduction with deep learning. National Remote Sensing Bulletin, v. 26, n. 4, p. 711-721, 2022. DOI: https://dx.doi.org/10.11834/jrs.20220163

XU, S.; WANG, R.; SHI, W.; WANG, X. Classification of tree species in transmission line corridors based on YOLO v7. Forests, v. 15, n. 1, p. 61, 2023. DOI: https://doi.org/10.3390/f15010061

YANG, Z.; WILLIS, P.; MUELLER, R. Impact of band-ratio enhanced AWIFS image to crop classification accuracy. Available at: https://www.asprs.org/a/publications/proceedings/pecora17/0041.pdf. Accessed in: 15th May 2024.

ZHANG, L.; ZHAO, C.; FENG, Y.; LI, D. Pests identification of ip102 by yolov5 embedded with the novel lightweight module. Agronomy, v. 13, n. 6, p. 1583, 2023. DOI: https://doi.org/10.3390/agronomy13061583

ZHENG, H., et al. Evaluation of rgb, color-infrared and multispectral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in rice. Remote Sensing, v. 10, n. 6, 2018. DOI: https://doi.org/10.3390/rs10060824

ZHUO, W.; WU, N.; SHI, R.; WANG, Z. UAV mapping of the chlorophyll content in a tidal flat wetland using a combination of spectral and frequency indices. Remote Sensing., v. 14, 827, 2022. DOI: 10.3390/rs14040827.

A capa traz as imagens de recuperação das margens do Rio Pardinho, RS.

Downloads

Published

2025-05-02

How to Cite

Richter, V., Sousa, M. V. R. de, Santos, R. S., Ziembowicz, M. M., Rigão, J. C., Borges Júnior, N., Amaral, L. de P., Silva, S. D. P. da, & Amado, J. C. (2025). UAV-based spectral images using remote sensing and YOLOv8 in Eucalyptus saligna Sm. inventory. Ciência Florestal, 35, e88522. https://doi.org/10.5902/1980509888522

Issue

v. 35 (2025): Publicação Contínua

Section

Articles

License

Copyright (c) 2025 Ciência Florestal

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

A revista CIÊNCIA FLORESTAL reserva-se o direito de realizar, nos originais, alterações de ordens normativas, ortográficas e gramaticais, com vistas a manter o padrão escolar da língua, mas respeitando o estilo dos autores. As provas finais podem ou não ser enviadas aos autores.

Most read articles by the same author(s)