prikaz prve stranice dokumenta Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat
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Scientific paper - Original scientific paper
Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat
Remote Sensing, 5 (2025), ¨17!; 1-24. https://doi.org/10.3390/rs17050929
Skendžić, Sandra; Novak, Hrvoje; Zovko, Monika; Pajač Živković, Ivana; Lešić, Vinko; Maričević, Marko; Lemić, Darija

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Skendžić, S., Novak, H., Zovko, M., Pajač Živković, I., Lešić, V., Maričević, M. & Lemić, D. (2025). Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat. Remote Sensing, 5. (¨17!), 1-24. doi: 10.3390/rs17050929

Skendžić, Sandra, et al. "Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat." Remote Sensing, vol. 5, no. ¨17!, 2025, pp. 1-24. https://doi.org/10.3390/rs17050929

Skendžić, Sandra, Hrvoje Novak, Monika Zovko, Ivana Pajač Živković, Vinko Lešić, Marko Maričević and Darija Lemić. "Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat." Remote Sensing 5, no. ¨17! (2025): 1-24. https://doi.org/10.3390/rs17050929

Skendžić, S., et al. (2025) 'Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat', Remote Sensing, 5(¨17!), pp. 1-24. doi: 10.3390/rs17050929

Skendžić S, Novak H, Zovko M, Pajač Živković I, Lešić V, Maričević M, and sur.. Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat. Remote Sensing [Internet]. 2025 March 05 [cited 2025 March 17];5(¨17!):1-24. doi: 10.3390/rs17050929

S. Skendžić, et al., "Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat", Remote Sensing, vol. 5, no. ¨17!, pp. 1-24, March 2025. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:168:993023. [Accessed: 17 March 2025]

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Metadata
Title (english)Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat
AuthorSandra Skendžić
AuthorHrvoje Novak
AuthorMonika Zovko
AuthorIvana Pajač Živković
AuthorVinko Lešić
AuthorMarko Maričević
AuthorDarija Lemić
Author's institutionUniversity of Zagreb
Faculty of Agriculture
(Agricultural Zoology)
Author's institutionUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Control and Computer Engineering)
Scientific / art field,
discipline and subdiscipline		BIOTECHNICAL SCIENCES
Agronomy
Phytomedicine
Abstract (english)
Aphids are significant pests of winter wheat, causing damage by feeding on plant sap and reducing crop yield and quality. This study evaluates the potential of hyperspectral remote sensing (350–2500 nm) and machine learning (ML) models for classifying healthy and aphid-infested wheat canopies. Field-based hyperspectral measurements were conducted at three growth stages - T1 (stem elongation–heading), T2 (flowering), and T3 (milky grain development) - with infestation levels categorized... More according to established economic thresholds (ET) for each growth stage. Spectral data were analyzed using Uniform Manifold Approximation and Projection (UMAP); vegetation indices; and ML classification models, including Logistic Regression (LR), k-Nearest Neighbors (KNNs), Support vector machines (SVMs), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). The classification models achieved high performance, with F1-scores ranging from 0.88 to 0.99, and SVM and RF consistently outperforming other models across all input datasets. The best classification results were obtained at T2 with an F1-score of 0.98, while models trained on the full spectrum dataset showed the highest overall accuracy. Among vegetation indices, the Modified Triangular Vegetation Index, MTVI (rpb = −0.77 to −0.82), and Triangular Vegetation Index, TVI (rpb = −0.66 to −0.75), demonstrated the strongest correlations with canopy condition. These findings underscore the utility of canopy spectra and vegetation indices for detecting aphid infestations above ET levels, allowing for a clear classification of wheat fields into “treatment required” and “no treatment required” categories. This approach provides a precise and timely decision making tool for insecticide application, contributing to sustainable pest management by enabling targeted interventions, reducing unnecessary pesticide use, and supporting effective crop protection practices. Less
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusAccepted
Peer reviewPeer review - international
Publication versionAccepted version
Journal titleRemote Sensing
Numberingvol. 5, no. ¨17!, pp. 1-24, 929
BPC0 EUR
e-ISSN2072-4292
DOIhttps://doi.org/10.3390/rs17050929
URN:NBNurn:nbn:hr:168:993023
Online first2025-03-05
ProjectNumber: KK.05.1.1.02.0031
Title (croatian): Napredna i prediktivna poljoprivreda za otpornost klimatskim promjenama
Title (english): Advanced and predictive agriculture for resilience to climate change
Acronym: AgroSPARC
Leader: Vinko Lešić
Jurisdiction: eu
Funding stream: European Regional Development Fund
Document URLhttps://www.mdpi.com/2072-4292/17/5/929
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2025-03-10 09:24:23
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