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Modified U-Net for Leaf Segmentation of Eucalyptus pellita Seedlings in Open Natural Environments
Corresponding Author(s) : Yeni Herdiyeni
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 10, No. 4, November 2025
Abstract
This study addressed leaf segmentation in open nursery environments for Eucalyptus pellita seedlings, where fluctuating illumination, cluttered backgrounds, and overlapping foliage had hindered reliable monitoring at operational scale. We proposed a Modified U-Net that integrated a ResNet-50 encoder for high-resolution feature extraction, L2 regularization in the decoder to improve generalization, and a composite binary cross-entropy plus Dice loss to balance pixel-level accuracy with shape conformity. We assembled 2,424 RGB images from an operational nursery and evaluated three architectures (Modified U-Net as the primary model, SegNet, and DeepLabv3+) under cloudy, sunny, and scorching illumination. We conducted inference at native resolution and summarized per-image metrics using medians with interquartile ranges, followed by nonparametric significance testing. The Modified U-Net consistently outperformed the baselines across all scenarios, achieving median Dice coefficients of 0.872 (cloudy), 0.841 (sunny), and 0.854 (scorching), with corresponding Intersection over Union values of 0.773, 0.725, and 0.745. A Kruskal-Wallis test on per-image Dice and Intersection over Union yielded no significant differences across lighting conditions (H = 4.012, p = 0.1345), indicating stable performance under natural illumination variability. Qualitative overlays revealed localized errors, including glare-induced false positives in sunny scenes and shadow-related artifacts under scorching light, which did not materially shift global overlap distributions. We concluded that the proposed architecture delivered robust, high-fidelity segmentation in realistic nursery conditions and provided a practical basis for field deployment, with further gains expected from glare- and shadow-aware augmentation and lightweight optimization for near real-time inference on edge devices.
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References
K. von Rintelen, E. Arida, and C. Häuser, “A review of biodiversity-related issues and challenges in megadiverse Indonesia and other Southeast Asian countries,” Res. Ideas Outcomes, vol. 3, 2017. https://doi.org/10.3897/rio.3.e20860
BPS, “Statistik Produksi Kehutanan 2023,” Badan Pus. Stat., p. 32, 2023.
Peraturan Presiden No.66 Tahun 2017 tentang Koordinasi Startegis Lintas sektoral Penyelengaraan Pelayanan Kepemudaan, “Lembaran Negara Republik,” Rencana Umum Energi Nas., no. 73, pp. 1–6, 2017.
M. A. Inail, E. B. Hardiyanto, and D. S. Mendham, “Growth responses of Eucalyptus pellita F. Muell plantations in south sumatra to macronutrient fertilisers following several rotations of Acacia mangium willd,” 2019. https://doi.org/10.3390/F10121054
S. C. Grossnickle, “Seedling establishment on a forest restoration site-An ecophysiological perspective,” Reforesta, vol. 6, pp. 110–139, 2018. https://doi.org/10.21750/REFOR.6.09.62
S. Jayathunga, G. D. Pearse, and M. S. Watt, “Unsupervised Methodology for Large-Scale Tree Seedling Mapping in Diverse Forestry Settings Using UAV-Based RGB Imagery,” 2023. https://doi.org/10.3390/rs15225276
G. Papadopoulos, S. Arduini, H. Uyar, V. Psiroukis, A. Kasimati, and S. Fountas, “Economic and environmental benefits of digital agricultural technologies in crop production: A review,” Smart Agric. Technol., vol. 8, p. 100441, 2024. https://doi.org/10.1016/j.atech.2024.100441
Y. Diez, S. Kentsch, M. Fukuda, M. L. L. Caceres, K. Moritake, and M. Cabezas, “Deep learning in forestry using uav-acquired rgb data: A practical review,” 2021. https://doi.org/10.3390/rs13142837
Z. Luo, W. Yang, Y. Yuan, R. Gou, and X. Li, “Semantic segmentation of agricultural images: A survey,” Inf. Process. Agric., vol. 11, no. 2, pp. 172–186, 2024. https://doi.org/10.1016/j.inpa.2023.02.001
K. Li, L. Zhang, B. Li, S. Li, and J. Ma, “Attention-optimized DeepLab V3 + for automatic estimation of cucumber disease severity,” Plant Methods, vol. 18, no. 1, p. 109, 2022. https://doi.org/10.1186/s13007-022-00941-8
R. Ma et al., “Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds,” Front. Plant Sci., vol. Volume 14, 2023. https://doi.org/10.3389/fpls.2023.1211075
K. Yang, W. Zhong, and F. Li, “Leaf Segmentation and Classification with a Complicated Background Using Deep Learning,” Agronomy, vol. 10, p. 1721, Nov. 2020. https://doi.org/10.3390/agronomy10111721
A. Silwal, T. Parhar, F. Yandun, H. Baweja, and G. Kantor, “A Robust Illumination-Invariant Camera System for Agricultural Applications,” IEEE Int. Conf. Intell. Robot. Syst., pp. 3292–3298, 2021. https://doi.org/10.1109/IROS51168.2021.9636542
J. Mitchel, T. Gao, E. Cole, V. Petukhov, and P. V. Kharchenko, “Impact of Segmentation Errors in Analysis of Spatial Transcriptomics Data.,” bioRxiv, p. 2025.01.02.631135, Jan. 2025. https://doi.org/10.1101/2025.01.02.631135
F. J. Hutapea, C. J. Weston, D. Mendham, and L. Volkova, “Sustainable management of Eucalyptus pellita plantations: A review,” For. Ecol. Manage., vol. 537, p. 120941, 2023. https://doi.org/10.1016/j.foreco.2023.120941
M. N. Megat Mohamed Nazir, R. Terhem, A. R. Norhisham, S. Mohd Razali, and R. Meder, “Early monitoring of health status of plantation-grown eucalyptus pellita at large spatial scale via visible spectrum imaging of canopy foliage using unmanned aerial vehicles,” 2021. doi: 10.3390/f12101393. https://doi.org/10.3390/f12101393
Y. Wang, Z. Yang, K. Gert, and H. A. Khan, “The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery,” Plant Methods, vol. 19, no. 1, p. 51, 2023. https://doi.org/10.1186/s13007-023-01028-8
J. R. Landis and G. G. Koch, “The Measurement of Observer Agreement for Categorical Data,” Biometrics, vol. 33, no. 1, p. 159, Sep. 1977. https://doi.org/10.2307/2529310
X. Song et al., “Agricultural Image Processing: Challenges, Advances, and Future Trends,” 2025. https://doi.org/10.3390/app15169206
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, p. 60, 2019. https://doi.org/10.1186/s40537-019-0197-0
Samsuzzaman et al., “Automated Seedling Contour Determination and Segmentation Using Support Vector Machine and Image Features,” 2024. https://doi.org/10.3390/agronomy14122940
W. Zhou, A. Zyner, S. Worrall, and E. Nebot, “Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective,” IEEE Robot. Autom. Lett., vol. 4, no. 2, pp. 461–468, 2019. https://doi.org/10.1109/lra.2019.2891027
R. Zenkl et al., “Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset,” Front. Plant Sci., vol. 12, 2022. https://doi.org/10.3389/fpls.2021.774068
K. Alomar, H. I. Aysel, and X. Cai, “Data Augmentation in Classification and Segmentation: A Survey and New Strategies,” 2023. https://doi.org/10.3390/jimaging9020046
L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., Cham: Springer International Publishing, 2018. https://doi.org/10.1007/978-3-030-01234-2_49
F. Milletari, N. Navab, and S. A. Ahmadi, “V-Net: Fully convolutional neural networks for volumetric medical image segmentation,” in Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, 2016. https://doi.org/10.1109/3DV.2016.79
P. Jaccard, “the Distribution of the Flora in the Alpine Zone.,” New Phytol., vol. 11, no. 2, pp. 37–50, Feb. 1912. https://doi.org/10.1111/j.1469-8137.1912.tb05611.x
C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. Cambridge: Cambridge University Press, 2008. https://doi.org/10.1017/cbo9780511809071
S. Sturges and M. Brown, “Polypsychopharmacy,” Bull. Menninger Clin., vol. 39, no. 3, pp. 274–279, 1975. https://doi.org/10.1097/01.jcp.0000177847.77791.99
M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, 2009. https://doi.org/10.1016/j.ipm.2009.03.002
A. A. Taha and A. Hanbury, “Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool,” BMC Med. Imaging, vol. 15, no. 1, p. 29, 2015. https://doi.org/10.1186/s12880-015-0068-x
W. H. Kruskal and W. A. Wallis, “Use of Ranks in One-Criterion Variance Analysis,” J. Am. Stat. Assoc., vol. 47, no. 260, pp. 583–621, Dec. 1952. https://doi.org/10.1080/01621459.1952.10483441
Tomczak M and Tomczak E, “The need to report effect size estimates revisited. An overview of some recommended measures of effect size,” Trends Sport Sci., vol. 21, no. 1, pp. 19–25, Jan. 2014.
A. Uryasheva, A. Kalashnikova, D. Shadrin, K. Evteeva, E. Moskovtsev, and N. Rodichenko, “Computer vision-based platform for apple leaves segmentation in field conditions to support digital phenotyping,” Comput. Electron. Agric., vol. 201, no. September 2021. https://doi.org/10.1016/j.compag.2022.107269
J. Fu, Y. Zhao, and G. Wu, “Potato Leaf Disease Segmentation Method Based on Improved UNet,” Appl. Sci., vol. 13, no. 20, 2023. https://doi.org/10.3390/app132011179