PAgPROTECT - Project results

 Download Pdf

Scientific articles

  • Lavaquiol-Colell, B.; Llorens-Calveras, J.; Sanz, R.; et al. 2025. Methodology for the assessment of leaf area in fruit tree orchards using a terrestrial LiDAR-based system. Precision Agriculture 26, 96. DOI: https://doi.org/10.1007/s11119-025-10296-4

  • Lavaquiol-Colell, B.; Escolà, A.; Arnó, J.; Grau, J.; Ninot, J.; Gómez, D.; Llorens-Calveras, J. 2025. Parameter configuration to maximize accuracy in point clouds acquired with lidar-based terrestrial mobile laser scanners in fruit tree orchards. Smart Agricultural Technology 12, 101573. DOI: https://doi.org/10.1016/j.atech.2025.101573

  • Lavaquiol-Colell, B., Escolà, A., Sanz-Cortiella, R., Arnó, J., Gené-Mola, J., Gregorio, E., Rosell-Polo, J. R., Ninot, J., & Llorens-Calveras, J. 2025. A methodology for the realistic assessment of 3D point clouds of fruit trees in full 3D context. Computers and Electronics in Agriculture 232, 110082. DOI: https://doi.org/10.1016/j.compag.2025.110082

  • Sandonís-Pozo, L., Rufat, J., Pascual, M., Villar, J. M., Arnó, J., Escola, A., Rosell-Polo, J. R., & Martinez-Casasnovas, J. A. 2024. LiDAR-derived indices and their relationship with productivity and oil quality attributes in high-density olive orchards. Smart Agricultural Technology 12 (2025), 101213. DOI: https://doi.org/10.1016/j.atech.2025.101213

  • Sandonís-Pozo, L., Oger, B., Tisseyre, B., Llorens, J., Escolà, A., Pascual, M., Martínez-Casasnovas, J.A. 2024. Leafiness-LiDAR index and NDVI for identification of temporal patterns in super-intensive almond orchards as response to different management strategies. European Journal of Agronomy 159, 127278. DOI: https://doi.org/10.1016/j.eja.2024.127278

  • Gené-Mola J, Ferrer-Ferrer M, Jochen H, van Dalfsen P, de Hoog D, Sanz-Cortiella R, Rosell- Polo JR, Morros JR, Vilaplana V, Ruiz-Hidalgo J, Gregorio E. 2023. AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation. Data in Brief, 52, 110000. DOI: https://doi.org/10.1016/j.dib.2023.110000

  • Miranda JC, Arnó J, Gené-Mola J, Lordan J, Asín L, Gregorio E. 2023. Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples. Computers and Electronics in Agriculture, 214, 108302. DOI: https://doi.org/10.1016/j.compag.2023.108302 

  • Miranda JC, Arnó J, Gené-Mola J, Fountas S, Gregorio E. 2023. AKFruitYield: Modular benchmarking and video analysis software for Azure Kinect cameras for fruit size and fruit yield estimation in apple orchards. SoftwareX, 24, 101548. DOI: https://doi.org/10.1016/j.softx.2023.101548

  • Miranda JC, Gené-Mola J, Zude-Sasse M, Tsoulias N, Escolà A, Arnó J, Rosell-Polo JR, Sanz-Cortiella R, Martínez-Casasnovas JA, Gregorio E. 2023. Fruit sizing using AI: A review of methods and challenges. Postharvest Biology and Technology 206 (2023) 112587. DOI: https://doi.org/10.1016/j.postharvbio.2023.112587 

  • Miranda, J.C., Arnó, J., Gené-Mola, J., Fountas, S., Gregorio, E. 2023. AKFruitYield: Modular benchmarking and video analysis software for Azure Kinect cameras for fruit size and fruit yield estimation in apple orchards. SoftwareX, 24, 101548, https://doi.org/10.1016/j.softx.2023.101548
  • Ferrer-Ferrer, M., Ruiz-Hidalgo, J., Gregorio, E., Vilaplana, V., Morros, J.R., Gené-Mola, J. 2023. Simultaneous fruit detection and size estimation using multitask deep neural networks. Biosystems Engineering, 233, 63-75, https://doi.org/10.1016/j.biosystemseng.2023.07.010
  • Gené-Mola, J., Ferrer-Ferrer, M., Gregorio, E., Blok, P.M., Hemming, J., Morros, J.R., Rosell-Polo, J.R., Vilaplana, V., Ruiz-Hidalgo, J. 2023. Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation. Computers and Electronics in Agriculture, 209, 107854. https://doi.org/10.1016/j.compag.2023.107854
  • Escolà, A., Peña, J.M., López-Granados, F., Rosell-Polo, J.R., de Castro, A., Gregorio, E., Jiménez-Brenes, F.M., Sanz, R., Sebé, F., Llorens, J., Torres-Sánchez, J. 2023. Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 1: Methodology and comparison in vineyards. Computers and Electronics in Agriculture, 212, 108109. https://doi.org/10.1016/j.compag.2023.108109
  • Torres-Sánchez, J., Escolà, A., de Castro, A., López-Granados, F., Rosell-Polo, J.R., Sebé, F., Jiménez-Brenes, F.M., Sanz, R., Gregorio, E., Peña, J.M. 2023. Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 2: Comparison for different crops and training systems. Computers and Electronics in Agriculture, 212, 108083. https://doi.org/10.1016/j.compag.2023.108083

Congresses and conferences

  • Sandonís-Pozo, L., Martínez-Casasnovas, J.A., Escolà, A., Rosell-Polo, J.R., Rufat, J., Pascual, M., 2023. A new leafiness-LiDAR index to estimate light interception in intensive olive orchards. In: Staford, J (Ed.), Precision Agriculture'23, 14th European Conference on Precision Agriculture, Wageningen Academic Publishers, Wageningen (The Netherlands), pp 189-195. DOI: https://doi.org/10.3920/978-90-8686-947-3_22, https://repositori.udl.cat/handle/10459.1/463686.
  • Gené-Mola J., Ferrer-Ferrer M, Gregorio E., Blok P.M., Hemming J., Morros JR., Rosell-Polo JR., Vilaplana V., Ruiz-Hidalgo J. Amodal segmentation for on-tree apple fruit size estimation with RGB-D images. 2023. Anual Catalan Meeting on Computer Vision (ACMCV2023). Poster.
  • Lavaquiol, B., Llorens, J., Sanz, R., Arnó, J., Escolà, A. 2023. Uncertainty analysis of a LiDAR-based MTLS point cloud using a high-resolution ground-truth. In: Canavari, M., Vitali, G., Mattetti, M. (Eds.), Book of Abstracts (Posters), 14th European Conference on Precision Agriculture, 2-6 July 2023, Bologna, Italy. pp 37. https://repositori.udl.cat/handle/10459.1/463872
  • Llorens, J., Román, C., Escolà, A., Gené-Mola, J, Arnó, J., Martínez-Casasnovas, J.A. 2023. How can precision agriculture contribute to the 50 % pesticide reduction goal of the farm-to-fork strategy? In: Staford, J (Ed.), Precision Agriculture'23, 14th European Conference on Precision Agriculture, Wageningen Academic Publsxhers, Wageningen (The Netherlands), pp 285-292. DOI: 10.3920/978-90-8686-947-3.
  • Martínez-Casasnovas, J.A., Rosell-Tarragó, M., Rosell-Polo, J.R., Sanz, R., Gregorio, E., Gené-Mola, J., Llorens, J., Arnó, J., Escolà, A. 2023. Low-cost terrestrial photogrammetry for orchard sidewards 3d reconstruction. In: Canavari, M., Vitali, G., Mattetti, M. (Eds.), Book of Abstracts (Posters), 14th European Conference on Precision Agriculture, 2-6 July 2023, Bologna, Italy. pp 113. https://repositori.udl.cat/handle/10459.1/464591

 

Technical papers

  • Escolà, A. 2025. Tecnologías emergentes para la fruticultura de precisión
    Plataforma Tierra (online).
  • Sandonís-Pozo, L., Pascual, M., Martínez-Casasnovas, J. A. 2025.Agricultura de Precisión en almendro superintensivo: avances y retos 
    Interempresas. Grandes cultivos.

  • Gil, E., García, J., Llorens, J., Escolà, A. 2024. Casos de éxito en Agricultura de Precisión: Aplicación variable de productos fitosanitarios en cultivos frutales y viña
    Agricultura. Ed. Grupo Interempresas.

Final Degree Projects, Master Thesis and Doctoral Thesis

  • Flores Junqué, A. 2023. Analysis of air pollution in pesticide treatments using a LiDAR system. Final dissertation, Degree in Energy Engineering and Sustainability, Polytechnic School, Universitat de Lleida. [In Catalan]. Enllaç a repositori: https://repositori.udl.cat/handle/10459.1/463728
    Enginy "Miquel Aixalà" Award for the best Final Degree's project in Industrial Engineering.
  • Felip Pomes, M. 2023. AI-based Mobile Application for Real-Time Fruit Sizing. Final dissertation, Master in Informatics Engineering, Polytechnic School, Universitat de Lleida.
    AETI Award for the best Final Master's project in Computer Engineering.
  • Arpaci, B. 2023. 3D Apple Detection from Large Point Clouds Using Deep Learning. Final dissertation, Master in Coputer Vision, Autonomous University of Barcelona. https://ddd.uab.cat/record/28520 
  • Vega, P. 2024. 3D Fruit Detection in RGB and LiDAR-based sensors using deep learning. Final dissertation, Master in Computer Vision, Autonomous University of Barcelona.
  • Camí Sòria, P. 2024. Millora i implementació d’un polvoritzador hidropneumàtic de cabal variable adaptat a plantacions fructícoles en formació en vas mitjançant tècniques d’agricultura de precisió. Final dissertation, Degree in Food and Agricultural Engineering, School of Agrifood and Forestry Engineering and Veterinary Medicine, Universitat de Lleida. [In Catalan].

  • Clèries, M. 2025. Efecte de variables climàtiques sobre la dinàmica espaitemporal de la carpocapsa (Cydia pomonella) a escala regional. Aplicació de mètodes geoestadístics i machine learning. Final dissertation, Degree in Agricultural and Food Engineering, Universitat de Lleida.

  • Solsona Codina, S. 2025. Anàlisi espacio-temporal de la carpocapsa (Cydia pomonella) a escala de parcel·la per a la millora del maneig integrat de plagues. Final dissertation, Degree in Agricultural and Food Engineering, Universitat de Lleida.

  • Pelay Felis, J. 2025. Efecte de la fragmentació del paisatge agrícola sobre la dinàmica espacial de la carpocapsa (Cydia pomonella) a escala regional. Aplicació de mètodes de classificació (machine learning). Final dissertation, Degree in Agricultural and Food Engineering, Universitat de Lleida.

Datasets & Software

  • Gené-Mola J, Ferrer-Ferrer M, Jochen H, van Dalfsen P, de Hoog D, Sanz-Cortiella R, Rosell- Polo JR, Morros JR, Vilaplana V, Ruiz-Hidalgo J, Gregorio E. 2023. AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation. Data in Brief, 52, 110000. DOI: https://doi.org/10.1016/j.dib.2023.110000

  • Software: Miranda, J.C., Arnó, J., Gené-Mola, J. Fountas, S., Gregorio, E. 2023. AKFruitYield: AK_SW_BENCHMARKER - Azure Kinect Size Estimation & Weight Prediction Benchmarker. https://github.com/GRAP-UdL-AT/ak_sw_benchmarker
   Last modification:
Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 2: Comparison for different crops and training systems

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 2: Comparison for different crops and training systems

Simultaneous fruit detection and size estimation using multitask deep neural networks

Simultaneous fruit detection and size estimation using multitask deep neural networks

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 1: Methodology and comparison in vineyards

Mobile terrestrial laser scanner vs. UAV photogrammetry to estimate woody crop canopy parameters – Part 1: Methodology and comparison in vineyards

Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation

Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation

Previous slide
Next slide