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            <title xml:lang="es">De la Simulación al Campo: Un Enfoque sin Verdad Terreno para el Monitoreo 3D de Huertos</title>
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                <title xml:lang="fr">De la Simulation au Terrain : Une Approche Sans Vérité Terrain pour le Suivi 3D des Vergers</title>
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              <p>Lack of annotated data as well as model transfer challenges limit accurate structural analysis of 3D orchards in field conditions by LiDAR and machine learning techniques. This study explores a scalable framework that trains deep learning models on synthetic labelled data. It then applies them to unlabelled real point clouds acquired with an unmanned ground vehicle in an apple orchard. Integrating classification, skeletonisation, and contrastive learning the framework segments trees, generates structural maps, and detects anomalies without ground-truth annotations. These results suggest potential contributions to flexible orchard monitoring by supporting variability analysis based on descriptors derived from the structural representations of trees.</p>
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              <p>La falta de datos anotados, así como los desafíos de transferencia de modelos, limitan el análisis estructural preciso de huertos 3D en condiciones de campo mediante LiDAR y técnicas de aprendizaje automático. Este estudio explora un marco escalable que entrena modelos de aprendizaje profundo en datos sintéticos etiquetados. A continuación, los aplica a nubes de puntos reales sin etiquetar adquiridas con un vehículo terrestre no tripulado en un huerto de manzanos. Integrando clasificación, esqueletización y aprendizaje contrastivo, el marco segmenta árboles, genera mapas estructurales y detecta anomalías sin anotaciones reales. Estos resultados sugieren posibles contribuciones a la supervisión flexible de huertos mediante el apoyo al análisis de variabilidad basado en descriptores derivados de las representaciones estructurales de los árboles.</p>
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              <p>Le manque de données annotées ainsi que les défis liés au transfert de modèles limitent l'analyse structurelle précise des vergers 3D sur le terrain par LiDAR et les techniques d'apprentissage automatique. Cette étude explore un cadre évolutif qui entraîne des modèles d'apprentissage profond sur des données synthétiques étiquetées. Elle les applique ensuite à des nuages de points réels non étiquetés acquis à l'aide d'un véhicule terrestre sans pilote dans un verger de pommiers. Intégrant la classification, la squelettisation et l'apprentissage contrastif, le cadre segmente les arbres, génère des cartes structurelles et détecte les anomalies sans annotations de vérité au sol. Ces résultats suggèrent des contributions potentielles à la surveillance flexible des vergers en soutenant l'analyse de la variabilité basée sur des descripteurs dérivés des représentations structurelles des arbres.</p>
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