Abstract
The progress in computer vision has allowed the development of a diversity of precision agriculture systems, improving the efficiency and yield of several processes of farming. Among the different processes, crop monitoring has been extensively studied to decrease the resources consumed and increase the yield, where a myriad of computer vision strategies has been proposed for fruit analysis (e.g., fruit counting) or plant health estimation. Nevertheless, the problem of fruit ripeness estimation has received little attention, particularly when the fruits are still on the tree. As such, this paper introduces a strategy to estimate the maturation stage of fruits based on images acquired from handheld devices while the fruit is still on the tree. Our approach relies on an image segmentation strategy to crop and align fruit images, which a CNN subsequently processes to extract a compact visual descriptor of the fruit. A non-linear regression model is then used for learning a mapping between descriptors to a set of physicochemical parameters, acting as a proxy of the fruit maturation stage. The proposed method is robust to the variations in position, lighting, and complex backgrounds, being ideal for working in the wild with minimal image acquisition constraints. Source code is available at https://github.com/Diogo365/WildFruiP.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Appe, S.R.N., Arulselvi, G., Balaji, G.N.: Tomato ripeness detection and classification using VGG based CNN models. Int. J. Intell. Syst. Appl. Eng. 11(1), 296–302 (2023)
Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), vol. 29. Curran Associates, Inc. (2016)
Fitzgibbon, A., Pilu, M., Fisher, R.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 21(5), 476–480 (1999)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the International Conference on Neural Networks (IJCNN), vol. 4, pp. 1942–1948 (1995)
Li, B., Lecourt, J., Bishop, G.: Advances in non-destructive early assessment of fruit ripeness towards defining optimal time of harvest and yield prediction - a review. Plants 7(1), 3 (2018)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Ni, X., Li, C., Jiang, H., Takeda, F.: Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield. Horticult. Res. 7, 110 (2020)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Rizzo, M., Marcuzzo, M., Zangari, A., Gasparetto, A., Albarelli, A.: Fruit ripeness classification: a survey. Artif. Intell. Agric. 7, 44–57 (2023)
Sabzi, S., Javadikia, H., Arribas, J.I.: A three-variety automatic and non-intrusive computer vision system for the estimation of orange fruit pH value. Measurement 152, 107–298 (2020)
Siricharoen, P., Yomsatieankul, W., Bunsri, T.: Recognizing the sweet and sour taste of pineapple fruits using residual networks and green-relative color transformation attached with mask R-CNN. Postharvest Biol. Technol. 196, 112–174 (2023)
Acknowledgements
This work was funded by the InovFarmer.MED project, which is part of the PRIMA Programme. Also, it was supported by NOVA LINCS (UIDB/04516/2020) with the financial support of FCT.IP.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Springer Nature Switzerland AG
About this paper
Cite this paper
Paulo, D.J., Neves, C.M.B., Wessel, D.F., Neves, J.C. (2024). WildFruiP: Estimating Fruit Physicochemical Parameters from Images Captured in the Wild. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-49018-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49017-0
Online ISBN: 978-3-031-49018-7
eBook Packages: Computer ScienceComputer Science (R0)
