Continuous and autonomous image-based fruit tree monitoring
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13809Keywords:
Pattern recognition and AI in agriculture, Software sensors in agriculture, Wireless sensor networks in agriculture, Grading systems and Quality assessment, Decision-making supportAbstract
In Precision Agriculture, exhaustive monitoring of crop status is essential to optimize decision-making regarding water use and harvest logistics. However, large-scale instrumentation and digitization in the agricultural sector depend on automated and low-cost solutions. This study addresses the continuous monitoring of tangerine fruit on trees using fixed visible-light image sensors. Computer vision techniques, powered by Artificial Intelligence, are employed for fruit segmentation and the subsequent analysis of their visual parameters. Based on the results from 24 sensors deployed in the field over a full growing season, the system’s ability to reflect trends in fruit growth and ripening is demonstrated. Additionally, a comparison with manual reference measurements is included, yielding mean R2 values of 0.816 and 0.953 for size and color, respectively.
References
Apolo-Apolo, O.E., Martínez-Guanter, J., Egea, G., Raja, P., Pérez-Ruiz, M., 2020. Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. Eur. J. Agron. 115, 126030. https://doi.org/10.1016/j.eja.2020.126030
Blanco, V., Torres-Sánchez, R., Blaya-Ros, P.J., Pérez-Pastor, A., Domingo, R., 2019. Vegetative and reproductive response of ‘Prime Giant’ sweet cherry trees to regulated deficit irrigation. Sci. Hortic. (Amsterdam). 249, 478–489. https://doi.org/10.1016/J.SCIENTA.2019.02.016
Carion, N., Gustafson, L., Hu, Y.-T., Debnath, S., Hu, R., Suris, D., Ryali, C., Vasudev Alwala, K., Khedr, H., Huang, A., Lei, J., Ma, T., Guo, B., Kalla, A., Marks, M., Greer, J., Wang, M., Sun, P., Rädle, R., Afouras, T., Mavroudi, E., Xu, K., Wu, T.-H., Zhou, Y., Momeni, L., Hazra, R., Ding, S., Vaze, S., Porcher, F., Li, F., Li, S., Kamath, A., Cheng, H.K., Dollár, P., Ravi, N., Saenko, K., Zhang, P., Feichtenhofer, C., 2025. SAM 3: Segment Anything with Concepts. arxiv.
Chen, M., Chen, Z., Luo, L., Tang, Y., Cheng, J., Wei, H., Wang, J., 2024. Dynamic visual servo control methods for continuous operation of a fruit harvesting robot working throughout an orchard. Comput. Electron. Agric. 219, 108774. https://doi.org/10.1016/J.COMPAG.2024.108774
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. Comput. Electron. Agric. 209. https://doi.org/10.1016/j.compag.2023.107854
Giménez-Gallego, J., Martínez-del-Rincon, J., Blaya-Ros, P.J., Navarro-Hellín, H., Navarro, P.J., Torres-Sánchez, R., 2024a. Fruit Monitoring and Harvest Date Prediction Using On-Tree Automatic Image Tracking. IEEE Trans. AgriFood Electron. 3, 56–68. https://doi.org/10.1109/TAFE.2024.3408912
Giménez-Gallego, J., Martinez-del-Rincon, J., González-Teruel, J.D., Navarro-Hellín, H., Navarro, P.J., Torres-Sánchez, R., 2024b. On-tree fruit image segmentation comparing Mask R-CNN and Vision Transformer models. Application in a novel algorithm for pixel-based fruit size estimation. Comput. Electron. Agric. 222, 109077. https://doi.org/10.1016/J.COMPAG.2024.109077
Jocher, G., Qiu, J., 2026. Ultralytics YOLO26 [WWW Document]. URL https://github.com/ultralytics/ultralytics
Liu, S., Ampatzidis, Y., Guan, H., Liu, W., Zhou, C., Choi, D., Lee, W.S., 2026. Agrosense v2: An AI-enabled system for precision orchard sensing and tree-level monitoring. Comput. Electron. Agric. 248, 111797. https://doi.org/10.1016/J.COMPAG.2026.111797
Miranda, J.C., Gené-Mola, J., Zude-Sasse, M., Tsoulias, N., Escolà, A., Arnó, J., Rosell-Polo, J.R., Sanz-Cortiella, R., Martínez-Casasnovas, J.A., Gregorio, E., 2023. Fruit sizing using AI: A review of methods and challenges. Postharvest Biol. Technol. 206, 112587. https://doi.org/10.1016/J.POSTHARVBIO.2023.112587
Piani, M., Scalisi, A., Bortolotti, G., Mengoli, D., Manfrini, L., 2025. Evaluating Orchard Fruit Quality from Monocular and Stereo RGB-D Imagery: A Comparative Study, in: 2025 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, pp. 51–56. https://doi.org/10.1109/METROAGRIFOR66923.2025.11512584
Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P., 2001. Color transfer between images. IEEE Comput. Graph. Appl. 21, 34–41. https://doi.org/10.1109/38.946629
Rizzo, M., Marcuzzo, M., Zangari, A., Gasparetto, A., Albarelli, A., 2023. Fruit ripeness classification: A survey. Artif. Intell. Agric. 7, 44–57. https://doi.org/10.1016/j.aiia.2023.02.004
Roboflow Annotate [WWW Document], n.d. URL https://roboflow.com/annotate (accessed 6.3.26).
Wang, D., Li, C., Song, H., Xiong, H., Liu, C., He, D., 2020. Deep Learning Approach for Apple Edge Detection to Remotely Monitor Apple Growth in Orchards. IEEE Access 8, 26911–26925. https://doi.org/10.1109/ACCESS.2020.2971524
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jaime Giménez-Gallego, Alejandro Gómez-López, Cristian Rodríguez-García, Honorio Navarro-Hellín, Roque Torres-Sánchez, Fulgencio Soto-Valles
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
