Abstract
Internet of Things (IoT) is an emerging technology where standalone equipments and autonomous devices are connected to each other and users via Internet. When IoT concept meets agriculture, the future of farming is pushed to the next level, giving birth to what is called “Smart Agriculture” or “Precision Agriculture”. The most important benefit from IoT is that a user can daily monitor his crop online in a seamless fashion. High quality data gathered from various sensors and transferred wirelessly to farm database will increase farmers understanding to their landuse leading to increasing income and product quality. One of the monitoring process is weeds detection and crop yield estimation using camera sensors. The acquired images help farmers to build map of weeds distribution or yield quantity all over the field, these maps can be used either for real-time processing or to predetermine weeds regions based on field maps history of the previous seasons. This process is referred to as segmentation problem. Several algorithms have been proposed for that purpose, however, these algorithms were run only on high performance computers. In this paper, we evaluate performance and the robustness of the most used legacy algorithms under local conditions. We focused on implementing these schemes within real-time application constraint. For instance, these algorithms were implemented and run in a low-cost embedded system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abouzahir, M., Elouardi, A., Bouaziz, S., Latif, R., Abdelouahed, T.: An improved Rao-Blackwellized particle filter based-SLAM running on an OMAP embedded architecture. In: 2014 Second World Conference on Complex Systems (WCCS), pp. 716–721, November 2014
Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., ALRahamneh, Z.: Fast and accurate detection and classification of plant diseases. Mach. Learn. 14(5) (2011)
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the Fuzzy C-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Cui, Y., Qiao, Z., Zou, X., Wang, B.: Study on the method of visual navigation baseline identification and extraction of agricultural machinery. In: 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 766–769. IEEE (2015)
De Rainville, F.M., Durand, A., Fortin, F.A., Tanguy, K., Maldague, X., Panneton, B., Simard, M.J.: Bayesian classification and unsupervised learning for isolating weeds in row crops. Pattern Anal. Appl. 17(2), 401–414 (2014)
Guijarro, M., Pajares, G., Riomoros, I., Herrera, P., Burgos-Artizzu, X., Ribeiro, A.: Automatic segmentation of relevant textures in agricultural images. Comput. Electron. Agric. 75(1), 75–83 (2011)
Hague, T., Tillett, N., Wheeler, H.: Automated crop and weed monitoring in widely spaced cereals. Precis. Agric. 7(1), 21–32 (2006)
Hamuda, E., Glavin, M., Jones, E.: A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 125, 184–199 (2016)
Kataoka, T., Kaneko, T., Okamoto, H., Hata, S.: Crop growth estimation system using machine vision. In: 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2003, Proceedings, vol. 2, pp. b1079–b1083. IEEE (2003)
Lavania, S., Matey, P.S.: Novel method for weed classification in maize field using Otsu and PCA implementation. In: 2015 IEEE International Conference on Computational Intelligence & Communication Technology (CICT), pp. 534–537. IEEE (2015)
Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Meyer, G.E., Hindman, T.W., Laksmi, K.: Machine vision detection parameters for plant species identification. In: Photonics East (ISAM, VVDC, IEMB), pp. 327–335. International Society for Optics and Photonics (1999)
Meyer, G.E., Neto, J.C., Jones, D.D., Hindman, T.W.: Intensified Fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Comput. Electron. Agric. 42(3), 161–180 (2004)
Ruiz-Ruiz, G., Gómez-Gil, J., Navas-Gracia, L.: Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Comput. Electron. Agric. 68(1), 88–96 (2009)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Technical report, DTIC Document (1985)
Sekulska-Nalewajko, J., Goclawski, J.: A semi-automatic method for the discrimination of diseased regions in detached leaf images using Fuzzy C-means clustering. In: 2011 Proceedings of VIIth International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), pp. 172–175. IEEE (2011)
Slaughter, D., Giles, D., Downey, D.: Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61(1), 63–78 (2008)
Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A.: Plant species identification, size, and enumeration using machine vision techniques on near-binary images. In: Applications in Optical Science and Engineering, pp. 208–219. International Society for Optics and Photonics (1993)
Woebbecke, D., Meyer, G., Von Bargen, K., Mortensen, D., et al.: Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE-Am. Soc. Agric. Eng. 38(1), 259–270 (1995)
Zheng, L., Zhang, J., Wang, Q.: Mean-shift-based color segmentation of images containing green vegetation. Comput. Electron. Agric. 65(1), 93–98 (2009)
Zhou, Z., Zang, Y., Li, Y., Zhang, Y., Wang, P., Luo, X.: Rice plant-hopper infestation detection and classification algorithms based on fractal dimension values and Fuzzy C-means. Math. Comput. Modell. 58(3), 701–709 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Abouzahir, S., Sadik, M., Sabir, E. (2017). IoT-Empowered Smart Agriculture: A Real-Time Light-Weight Embedded Segmentation System. In: Sabir, E., García Armada, A., Ghogho, M., Debbah, M. (eds) Ubiquitous Networking. UNet 2017. Lecture Notes in Computer Science(), vol 10542. Springer, Cham. https://doi.org/10.1007/978-3-319-68179-5_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-68179-5_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68178-8
Online ISBN: 978-3-319-68179-5
eBook Packages: Computer ScienceComputer Science (R0)