Bahrami, Hazhir 
ORCID: https://orcid.org/0000-0003-1371-7337; Chokmani, Karem ORCID: https://orcid.org/0000-0003-0018-0761; Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356; Adamchuk, Viacheslav I. ORCID: https://orcid.org/0000-0001-7279-3597; Saifuzzaman, Md; Albasha, Rami ORCID: https://orcid.org/0000-0001-5709-3760 et Leduc, Maxime ORCID: https://orcid.org/0000-0001-7097-1636
(2025).
Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine.
International Journal of Applied Earth Observation and Geoinformation
, vol. 142
.
p. 104729.
DOI: 10.1016/j.jag.2025.104729.
Résumé
Alfalfa (Medicago sativa L.), a perennial legume forage crop, is valued for its high yield and quality. However, its survival during winter can be affected by several factors, and its mortality significantly impacts alfalfa production, necessitating timely and spatially detailed monitoring. This study aims to propose a framework for estimating alfalfa stem density using satellite imagery and machine learning (ML) algorithms, which can lead to winter mortality detection early in the spring and provide a better understanding of potential total dry matter. Three ML models—support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB)—were applied to Harmonized Landsat Sentinel (Landsat only, which is HLSL30) and Sentinel-2 datasets, accessed via the Google Earth Engine (GEE) Python API. Two scenarios were evaluated: 1) single-date data, capturing satellite images within a 3-day time window to the date of field sample measurement, and 2) time-series data, in which three satellite images were collected for the measurements during the first growing cycle. Both classification and regression models were used in both scenarios to estimate and classify alfalfa stem density. ML Classification models categorized stem density into four groups (bare, low-density, medium-density, and high-density), achieving an accuracy of up to 85 % using Sentinel-2 data and 84 % using HLSL30 data. The results also indicated that alfalfa stem density can be estimated with an error of ∼ ±6-9 stems/foot² (1 foot = 30.48 cm) using ML regression models. RF outperformed XGB and SVM in classification and regression tasks, showing superior accuracy in classifying density and lower root mean square error (RMSE) in estimating stem density. Our proposed framework model can offer valuable information to growers and decision-makers, enabling them to make timely and informed decisions.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | remote sensing; alfalfa stem count; machine learning; winter mortality detection; satellite data |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 26 août 2025 19:05 |
| Dernière modification: | 26 août 2025 19:05 |
| URI: | https://espace.inrs.ca/id/eprint/16577 |
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