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PubMed Publications| Keyword search (4,163 papers available) |  |
"Ross A" Authored Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods | Ekwe M; Fernando H; James G; Adeluyi O; Verrelst J; Kross A; | 41682534 CONCORDIA |
| 2 | Urban sprawl in Canada: Values in all 33 Census Metropolitan Areas and corresponding 469 Census Subdivisions between 1991 and 2011 | Pourali M; Townsend C; Kross A; Guindon A; Jaeger JAG; | 35242923 LIBRARY |
| 3 | A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines | OmidYeganeh M; Khalili-Mahani N; Bermudez P; Ross A; Lepage C; Vincent RD; Jeon S; Lewis LB; Das S; Zijdenbos AP; Rioux P; Adalat R; Van Eede MC; Evans AC; | 34381348 PERFORM |
| Title: | Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods | ||||
| Authors: | Ekwe M, Fernando H, James G, Adeluyi O, Verrelst J, Kross A | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/41682534/ | ||||
| DOI: | 10.3390/s26031018 | ||||
| Publication: | Sensors (Basel, Switzerland) | ||||
| Keywords: | LAI; PlanetScope; machine learning models; spectral bands; vegetation indices; | ||||
| PMID: | 41682534 | Category: | Date Added: | 2026-02-13 | |
| Dept Affiliation: |
CONCORDIA
1 Department of Strategic Space Application, National Space Research and Development Agency, Airport Road, P.M.B. 437, Abuja 900101, Nigeria. 2 Department of Geography, Planning and Environment, Concordia University, Montréal, QC H3G 1M8, Canada. 3 Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada. 4 Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain. |
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Description: |
Leaf area index (LAI) is a key indicator of crop growth and development and is widely used in both agricultural research and precision farming applications. PlanetScope imagery is generally used for monitoring crop growth due to its high revisit frequency, broad spatial coverage, and cost-effective access to consistent high-resolution multispectral data. Therefore, we developed regression models to estimate peanut LAI, combining PlanetScope spectral bands and vegetation indices (VIs). Specifically, we compared the performance of random forest (RF), eXtreme Gradient Boosting (XGBoost), and Partial Least Squares Regression (PLSR) regression algorithms for peanut LAI estimation. Our results showed that most of the VIs exhibited strong relationships with LAI. Thirteen VIs were individually evaluated for estimating LAI using the aforementioned algorithms, and our results showed that the best single predictors of LAI are: TSAVI (RF: R2 = 0.87, RMSE = 0.83 m2/m2, RRMSE = 24.20%; XGBoost: R2 = 0.77, RMSE = 0.95 m2/m2, RRMSE = 27.96%); and RTVIcore (PLSR: R2 = 0.68, RMSE = 1.12 m2/m2, RRMSE = 32.88%). The top six ranked VIs were used to calibrate the RF, XGBoost, and PLSR algorithms. Model validation indicated that RF achieved the highest accuracy (R2 = 0.844, RMSE = 0.858 m2/m2, RRMSE = 25.17%), followed by XGBoost (R2 = 0.808, RMSE = 0.92 m2/m2, RRMSE = 26.99%), whereas PLSR showed comparatively lower performance (R2 = 0.76, RMSE = 0.983 m2/m2, RRMSE = 28.85%). Further results showed that PlanetScope VIs provided superior model accuracy in estimating peanut LAI compared to the use of spectral bands alone. Additionally, integrating spectral bands with VIs reduced LAI estimation accuracy, underscoring the importance of selecting predictor variables in ensuring optimal model performance. Overall, the presented results are significant for future crop monitoring using RF to reduce overreliance on multiple models for peanut LAI estimation. |
