Real-Time Object Segmentation in Fruit Farming: Contributions to Agriculture 4.0
Abstract
Aiming at contributing with the development of Agriculture 4.0 solutions for Fruit Farming, this work addressed the task of apple instance segmentation from images, considering different ripening stages. The proposed solution consisted of a YOLOv11 Small convolutional neural network trained and tested on a public dataset containing modal masks of partially occluded fruits, reflecting realistic agricultural contexts. The model proved to be effective and efficient, achieving an experimental mAP@0.5 of 0.823 at 52 FPS, contributing to real-time intelligent solutions that assist in production quantification, automatic harvesting, and strategic decisionmaking in Digital Agriculture.References
Bishop, C. M. and Bishop, H. (2024). Deep Learning: Foundations and Concepts. Springer.
Da Silveira, F., Lermen, F. H., and Amaral, F. G. (2021). An Overview of Agriculture 4.0 Development: Systematic Review of Descriptions, Technologies, Barriers, Advantages, and Disadvantages. Computers and Electronics in Agriculture, 189:106405.
Fachinello, J. (2009). Fruticultura: Fundamentos e Pr’aticas. Embrapa Clima Temperado.
FAO (2009). Organização das Nações Unidas para a Alimentação e a Agricultura (FAO) – How to Feed the Worldin 2050. Disponível em: [link]. Acesso: 04/mar/2025.
FAO (2023). The State of Food and Agriculture 2023. Food and Agriculture Organization of the United Nations, Itália.
Ganesh, P., Volle, K., Burks, T., and Mehta, S. (2019). Deep orange: Mask r-cnn based orange detection and segmentation. Ifac-papersonline, 52(30):70–75.
Gené-Mola, J., Ferrer-Ferrer, M., Gregorio, E., Blok, P. M., Hemming, J., Morros, J.-R., Rosell-Polo, J. R., Vilaplana, V., and 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.
Gené-Mola, J., Ferrer-Ferrer, M., Hemming, J., van Dalfsen, P., de Hoog, D., Sanz-Cortiella, R., Rosell-Polo, J. R., Morros, J.-R., Vilaplana, V., Ruiz-Hidalgo, J., and Gregorio, E. (2024). 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.
Khanam, R. and Hussain, M. (2024). YOLOv11: An overview of the key architectural enhancements. Disponível em [link]. Acesso em 15/mar/2025.
Li, Z., Ye, W., Terven, J., Bennett, Z., Zheng, Y., Jiang, T., and Huang, T. (2023). Muva: A New Large-scale Benchmark for Multi-view Amodal Instance Segmentation in the Shopping Scenario. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23504–23513.
Marçal de Queiroz, D., M. Valente, D. S., de Assis de Carvalho Pinto, F., Borém, A., and Schueller, J. K., editors (2022). Digital Agriculture. Springer, Cham.
ONU (2015). Objetivos de Desenvolvimento Sustentável. Disponível em [link]. Acesso: 04/mar/2025.
Pechan, P. M., Bohle, H., and Obster, F. (2023). Reducing Vulnerability of Fruit Orchards to Climate Change. Agricultural Systems, 210:103713.
Redmon, J., Divvala, S., Girshick, R. B., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Rutledge, Z. and Mérel, P. (2022). Farm Labor Supply and Fruit and Vegetable Production. Am. J. Agric. Econ.
Tang, S., Zhu, Q., Zhou, X., Liu, S., and Wu, M. (2002). A Conception of Digital Agriculture. In IEEE International Geoscience and Remote Sensing Symposium, pages 3026–3028, Canada. IEEE.
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., and Liang, Z. (2019). Apple Detection during Different Growth Stages in Orchards using the Improved YOLO-V3 model. Computers and Electronics in Agriculture, 157:417–426.
Ultralytics (2024). Ultralytics yolov11. Disponível em: [link]. Acesso em 15/mar/2025.
Vijayakumar, A. and Vairavasundaram, S. (2024). YOLO-based object detection models: A review and its applications. Multimed. Tools Appl., 83:83535–83574.
Da Silveira, F., Lermen, F. H., and Amaral, F. G. (2021). An Overview of Agriculture 4.0 Development: Systematic Review of Descriptions, Technologies, Barriers, Advantages, and Disadvantages. Computers and Electronics in Agriculture, 189:106405.
Fachinello, J. (2009). Fruticultura: Fundamentos e Pr’aticas. Embrapa Clima Temperado.
FAO (2009). Organização das Nações Unidas para a Alimentação e a Agricultura (FAO) – How to Feed the Worldin 2050. Disponível em: [link]. Acesso: 04/mar/2025.
FAO (2023). The State of Food and Agriculture 2023. Food and Agriculture Organization of the United Nations, Itália.
Ganesh, P., Volle, K., Burks, T., and Mehta, S. (2019). Deep orange: Mask r-cnn based orange detection and segmentation. Ifac-papersonline, 52(30):70–75.
Gené-Mola, J., Ferrer-Ferrer, M., Gregorio, E., Blok, P. M., Hemming, J., Morros, J.-R., Rosell-Polo, J. R., Vilaplana, V., and 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.
Gené-Mola, J., Ferrer-Ferrer, M., Hemming, J., van Dalfsen, P., de Hoog, D., Sanz-Cortiella, R., Rosell-Polo, J. R., Morros, J.-R., Vilaplana, V., Ruiz-Hidalgo, J., and Gregorio, E. (2024). 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.
Khanam, R. and Hussain, M. (2024). YOLOv11: An overview of the key architectural enhancements. Disponível em [link]. Acesso em 15/mar/2025.
Li, Z., Ye, W., Terven, J., Bennett, Z., Zheng, Y., Jiang, T., and Huang, T. (2023). Muva: A New Large-scale Benchmark for Multi-view Amodal Instance Segmentation in the Shopping Scenario. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 23504–23513.
Marçal de Queiroz, D., M. Valente, D. S., de Assis de Carvalho Pinto, F., Borém, A., and Schueller, J. K., editors (2022). Digital Agriculture. Springer, Cham.
ONU (2015). Objetivos de Desenvolvimento Sustentável. Disponível em [link]. Acesso: 04/mar/2025.
Pechan, P. M., Bohle, H., and Obster, F. (2023). Reducing Vulnerability of Fruit Orchards to Climate Change. Agricultural Systems, 210:103713.
Redmon, J., Divvala, S., Girshick, R. B., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788.
Rutledge, Z. and Mérel, P. (2022). Farm Labor Supply and Fruit and Vegetable Production. Am. J. Agric. Econ.
Tang, S., Zhu, Q., Zhou, X., Liu, S., and Wu, M. (2002). A Conception of Digital Agriculture. In IEEE International Geoscience and Remote Sensing Symposium, pages 3026–3028, Canada. IEEE.
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., and Liang, Z. (2019). Apple Detection during Different Growth Stages in Orchards using the Improved YOLO-V3 model. Computers and Electronics in Agriculture, 157:417–426.
Ultralytics (2024). Ultralytics yolov11. Disponível em: [link]. Acesso em 15/mar/2025.
Vijayakumar, A. and Vairavasundaram, S. (2024). YOLO-based object detection models: A review and its applications. Multimed. Tools Appl., 83:83535–83574.
Published
2025-07-20
How to Cite
SANTOS, Valéria Ribeiro dos; GUEDES, Elloá B..
Real-Time Object Segmentation in Fruit Farming: Contributions to Agriculture 4.0. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 16. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 89-98.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2025.7970.
