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              <p>The leaf area index (LAI) is vital for assessing plant photosynthetic activity, crucial for optimising orchard management. This study presents a method to estimate leaf area density (LAD) variations and tree LAI using LiDAR data from unmanned ground vehicles (UGVs). Combining 3D tree reconstruction with neural network-based analysis of LiDAR penetration descriptors, the approach effectively estimates canopy parameters. The method was validated through simulation using diverse 3D canopy models, achieving performance metrics: RMSE: 0.2 m²/m³, R²: 0.95 for LAD and RMSE: 0.17 m²/m², R²: 0.84 for LAI. Results confirm the potential of LiDAR-based systems for precise orchard canopy monitoring.</p>
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              <p>El índice de área foliar (LAI) es fundamental para evaluar la actividad fotosintética de las plantas, un parámetro clave para la optimización de la gestión de huertos. Este estudio presenta un método para estimar las variaciones de la densidad de área foliar (LAD) y el LAI de los árboles a partir de datos LiDAR adquiridos por vehículos terrestres autónomos (UGV). Al combinar la reconstrucción 3D de los árboles con un análisis de descriptores de penetración LiDAR mediante redes neuronales, el enfoque permite una estimación eficaz de los parámetros del dosel. La metodología fue validada mediante simulaciones con diversos modelos 3D de copas, obteniendo métricas de rendimiento: RMSE: 0,2 m²/m³, R²: 0,95 para LAD y RMSE: 0,17 m²/m², R²: 0,84 para LAI. Los resultados confirman el potencial de los sistemas basados en LiDAR para el monitoreo preciso del dosel en huertos.</p>
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              <p>L'indice de surface foliaire (LAI) est essentiel pour évaluer l'activité photosynthétique des plantes, un paramètre clé pour l'optimisation de la gestion des vergers. Cette étude propose une méthode d'estimation des variations de la densité de surface foliaire (LAD) et du LAI des arbres à partir de données LiDAR acquises par des véhicules terrestres autonomes (UGV). En combinant la reconstruction 3D des arbres avec une analyse des descripteurs de pénétration LiDAR par réseaux neuronaux, l'approche permet une estimation efficace des paramètres du couvert végétal. La méthode a été validée par simulation sur divers modèles 3D de canopées, obtenant des performances de RMSE : 0,2 m²/m³, R² : 0,95 pour le LAD et RMSE : 0,17 m²/m², R² : 0,84 pour le LAI. Les résultats confirment le potentiel des systèmes basés sur le LiDAR pour un suivi précis du couvert végétal des vergers.</p>
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