PAgPROTECT - Project results

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Scientific articles

  • Hajjaj, O.; de Bruin, S.; Martínez-Casasnovas, J.A.; Llorens, J.; Bosch-Serra, D.; Plata, J.M.; Torrent, X.; Arnó, J. 2026. Modelling of codling moth-canopy interactions as a new approach to variable-rate pesticide application in apple orchards. Biosystems Engineering, 269, 104529. DOI: https://doi.org/10.1016/j.biosystemseng.2026.104529

  • 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 

  • 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

 

Phd dissertations

  • Miranda, J. C. 2024. Open source software and benchmarking of computer vision algorithms for apple fruit detection, fruit sizing and yield prediction using RGB-D cameras. PhD Thesis, Universitat de Lleida. Advisors: Eduard Gregorio López and Jaime Arnó Satorra. Defense date: 03/06/2024. Repository link: https://hdl.handle.net/10803/690455. Open Access.

  • Sandonís Pozo, L. 2025. Methods to estimate growth and production in fruit plantations based on Precision Agriculture technologies. PhD Thesis, Universitat de Lleida. Advisors: José Antonio Martínez Casasnovas and Miquel Pascual Roca. Defense date: 01/17/2025. Repository link: https://hdl.handle.net/10803/693754. Open Access. Extraordinary Doctorate Award in Agricultural and Food Science and Technology. 

  • Lavaquiol Colell, B. 2025. Optimització de les metodologies per a la caracterització tridimensional de conreus arboris i arbustius amb sistemes òptics terrestres en Agricultura de Precisió. PhD Thesis, Universitat de Lleida. Advisors: Alexandre Escolà Agustí and Jordi Llorens Calveras. Defense date: 07/22/2025. Repository link: https://hdl.handle.net/10803/695318. Open Access.

  • Hajjaj El Imrani, O. (In progress). Spatio-temporal analysis of pests to improve alert systems, timing of treatments and variable and precise dosing of pesticides. PhD Thesis, Universitat de Lleida. Advisors: Jaime Arnó Satorra and José Antonio Martínez Casasnovas. Expected defense date: 05/2027.

 

Master degree thesis

 

  • Felip Pomés, M. 2023. AI-based Mobile Application for Real-Time Fruit Sizing. Final dissertation, master's degree 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/285205  Open Access

  • Vega Gallego, P. 2024. 3D Fruit Detection in RGB and LiDAR-based sensors using deep learning. Final dissertation, Master in Computer Vision, Autonomous University of Barcelona.

  • Arribas Revilla, L. 2025. Detection and measurement of fruits using multi-task neural networks. Final dissertation, Master in Computer Vision, Autonomous University of Barcelona.

  • Naranjo Salao, E.X. 2026. Evaluación de la deriva generada por pulverizadores hidroneumáticos y UAVs mediante tecnología LiDAR. Master in Agronomical Engineering, Universitat de Lleida. Finalizing drafting (Defense scheduled for September 2026). 

  • Ortega Boneta, N. 2026. Avaluació comparativa de sensors LiDAR de baix cost en la caracterització geomètrica de plantacions fructícoles per a l’agricultura de precisió. Master in Industrial Engineering, Universitat de Lleida. Finalizing drafting (Defense scheduled for September 2026). 

 

Final Degree Projects

 

  • Flores Junqué, A. 2023. Análisis de la contaminación aérea en tratamientos de pesticidas mediante el uso de un sistema LiDAR. Final dissertation, Bachelor's degree in Energy and Sustainability Engineering. Escola Politècnica Superior, Universitat de Lleida. [In Catalan] Link to the repository: https://repositori.udl.cat/handle/10459.1/463728. Open Access.  
    Enginy "Miquel Aixalà" Award for the best Final Degree's project in Industrial Engineering.

  • 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, Bachelor's degree Agricultural and Food Engineering. School of Agrifood and Forestry Engineering and Veterinary Medicine, Universitat de Lleida.

  •  Manuel 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, Bachelor's degree Agricultural and Food Engineering. School of Agrifood and Forestry Engineering and Veterinary Medicine, Universitat de Lleida. Link to the repository: https://hdl.handle.net/10459.1/468557.  Open Access. 
    Best Final Degree Project of the School of Agrifood and Forestry Enginyeering and Veterinary Medicine awarded  by the Consell Social of the Universitat de Lleida. Award for the best TFG (Bachelor's Thesis) in Precision Agriculture from the Timac Agro – UdL Chair.

  • 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, Bachelor's degree Agricultural and Food Engineering. School of Agrifood and Forestry Engineering and Veterinary Medicine, Universitat de Lleida. Link to the repository: https://hdl.handle.net/10459.1/468556. Open Access

  • 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, Bachelor's degree Agricultural and Food Engineering. School of Agrifood and Forestry Engineering and Veterinary Medicine, Universitat de Lleida.

  • Segura Paz, A. 2025. Reconstrucció 3D de plantacions d’arbres fruiters amb sensors fotònics de baix cost. Final dissertation, Bachelor's degree in Computer Engineering, Escola Politècnica Superior, Universitat de Lleida. Link to the repository:https://hdl.handle.net/10459.1/468385. Open Access

  • Benta. R.A. 2026.  Estudi dels sistemes d’aplicació variable de fitosanitaris en cultius arboris per a la implementació de l’Agricultura de Precisió.   Final dissertation, Bachelor's degree Agricultural and Food Engineering. School of Agrifood and Forestry Engineering and Veterinary Medicine, Universitat de Lleida. (Expected defense September 2026).

  • Chueca Bosch, M. 2026.   Estudi comparatiu entre una aplicació de productes fitosanitaris convencional i una de dosi variable.  Final dissertation, Bachelor's degree Agricultural and Food Engineering. School of Agrifood and Forestry Engineering and Veterinary Medicine, Universitat de Lleida. (Expected defense September 2026).

 

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.

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

Results Dissemination Workshop

On May 28th, 2026, we organized a workshop to present the results of the PAgPROTECT project. The presentations used are as follows.

 

 

   Última modificación:
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

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