Comunicacions a congressos (Grup de Recerca en AgròTICa i Agricultura de Precisió)
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- ItemOpen AccessEvaluating NDVI as a proxy for LiDAR-based canopy characterisation in large almond orchards(Wageningen Academic Publishers, 2025) Escolà i Agustí, Alexandre; Torrent Martí, Xavier; Llorens Calveras, Jordi; Arnó Satorra, Jaume; Martínez Casasnovas, José Antonio; Sandonís Pozo, Leire; Othmane Hajjaj; Plata, José MaríaThe aim of the study was to validate the convenience of remote sensing in orchard canopy monitoring with respect to the canopy characterisation by means of LiDAR, a total of 4 almond subplots of 1 ha each were scanned. Subsequently, ordinary punctual kriging was applied to the cross-sectional area of tree rows estimated every 0.1 m along the rows derived from the MTLS data. In turn, Sentinel-2 satellite images of the same date as the LiDAR scans were downloaded and the normalized difference vegetation index (NDVI) was correlated to LiDAR-derived cross-sectional area. The results show strong correlations between the two variables in 3 of the 4 almond subplots (R2=0.77, 0.61 and 0.77, respectively).
- ItemOpen AccessAnalysis of drought impact on apple trees using the leafiness- LiDAR index(2025) Sandonís Pozo, Leire; Martínez Casasnovas, José Antonio; Pascual Roca, MiquelApple trees (Malus×domestica Borkh.) are widely cultivated in Mediterranean regions, where drought poses a major challenge to growth and productivity. This study assessed the Leafiness-LiDAR Index (LLI) as a proxy for the Leaf Area Index (LAI) and its relationship with yield parameters, including fruit count (num/tree), size, weight, and yield (kg/ha), under two irrigation regimes – full irrigation (FI) and deficit irrigation (DI) – and planting densities (0.5 m and 1 m). Results showed a positive correlation between LLI, LAI, and yield, with reduced vegetative growth under drought (DI) conditions. Wider planting frames favoured larger fruit size and higher fruit count, while denser frames increased yield per hectare. LLI proved effective for monitoring canopy dynamics, offering potential for improving productivity and drought resilience.
- ItemOpen AccessTowards Analysis of Drought Impact on Apple Trees Using the Leafiness-LiDAR Index(2025) Sandonís Pozo, Leire; Martínez Casasnovas, José Antonio; Pascual Roca, MiquelApple trees (Malus×domestica Borkh.) are widely cultivated in Mediterranean regions, where drought poses a major challenge to growth and productivity. This study assessed the Leafiness-LiDAR Index (LLI) as a proxy for the Leaf Area Index (LAI) and its relationship with yield parameters, including fruit count (num/tree), size, weight, and yield (kg/ha), under two irrigation regimes – full irrigation (FI) and deficit irrigation (DI) – and planting densities (0.5 m and 1 m). Results showed a positive correlation between LLI, LAI, and yield, with reduced vegetative growth under drought (DI) conditions. Wider planting frames favoured larger fruit size and higher fruit count, while denser frames increased yield per hectare. LLI proved effective for monitoring canopy dynamics, offering potential for improving productivity and drought resilience.
- ItemOpen AccessYield prediction using mobile terrestrial laser scanning(2019) Gené Mola, Jordi; Gregorio López, Eduard; Sanz Cortiella, Ricardo; Escolà i Agustí, Alexandre; Rosell Polo, Joan RamonYield prediction provides valuable information to plan the harvest campaign, fruit storage and sales. Traditionally, yield estimation has been carried out by manual counting of randomly selected samples, without addressing spatial variability within the orchard. To obtain a precise estimation it is necessary to sample a relatively large number of trees, which is unfeasible with manual counting. To solve this issue, this work proposes the use of a Mobile Terrestrial Laser Scanner (MTLS) for fruit detection and yield prediction. Experimental test were carried out in a commercial Fuji apple orchard. The row of threes was scanned from the two sides (east and west). The measurement equipment consisted of an MTLS comprised of a LiDAR sensor, and a real-time kinematics global navigation satellite system (RTK-GNSS) connected to a rugged laptop. The LiDAR sensor used was a Puck VLP-16 (Velodyne LIDAR Inc., San José, CA, USA), which provides a 3D point cloud with calibrated reflectance values of the measured scene. The fruit detection algorithm implemented in this work is divided into four steps: (1) Reflectance thresholding, which delete those points presenting a reflectance lower than 60%; (2) Connected Points Clustering using DBSCAN; (3) Fruit separation, which uses a support vector machine (SVM) to predict the number of fruits that contains each cluster; (4) False positive removal, also based on a trained SVM. From detections obtained with this algorithm, the yield was predicted using a linear model (obtained with training data) that relates the number of detections and the actual number of fruits Three different trials were evaluated: east (E) side scanning, west (W) side scanning and merging data from both scanned sides (E+W). As it was expected, fruit detection results showed lower detection rates when only scanning from one tree side, presenting detection rates of 38.3% and 48.5% for east and west sides, respectively. However, the detection rate increased up to 75.8% when using E+W data. Similarly, yield prediction results showed higher errors when using data from only one tree side, obtaining a RMSE of 15.2% and 15.3% (east and west, respectively). The prediction improved significantly when using data from both tree sides (E+W), presenting a RMSE of 5.4%. From these results it is concluded that MTLS has potential in yield prediction in fruit orchards. Although fruit detection rates are moderately successful, the system was able to predict the actual number of fruits with low estimation errors. Only using data from one tree side increases the prediction error, but it has de advantage of reducing a 50% the scanning time, which may be interesting depending on the application and the interest of the farmer. Future works will extend this study to other fruit varieties.
- ItemOpen AccessLow-cost terrestrial photogrammetry for orchard sidewards 3D reconstruction(2023) Martínez Casasnovas, José Antonio; Rosell Tarragó, Miquel; Rosell Polo, Joan Ramon; Sanz Cortiella, Ricardo; Gregorio López, Eduard; Gené Mola, Jordi; Arnó Satorra, Jaume; Plata Moreno, José Manuel; Escolà i Agustí, Alexandre