Abstract
According to recent studies, the world’s population has doubled since 1960. Furthermore, some projections indicate that the world’s population could reach more than ten billion in the next half of this century. As the world is getting increasingly crowded, the ever-growing need for resources is rising. It appears that depletion of natural resources will be three times more than current rates by the mid-century. People would not only consume more resources but also will need more agricultural produce for their everyday life. Hence, in order to meet the ever-increasing demand for farming products, yield should be maximized using top-end technologies. Precision agriculture is the application of technologies and methods to obtain data driven crop management of the farmland. In the middle of the 1980s, precision farming techniques initially were used for soil analysis using sensors and evolved to advanced applications that makes use of satellites, handheld devices and aerial vehicles. Drones commonly referred as unmanned aerial vehicles (UAVs) and have been extensively adopted in precision farming. Consequently, in the last two decades, 80 to 90% of the precision farming operations employed UAVs. Accordingly, this paper proposes a prototype UAV based solution, which can be used to hover over tomato fields, collect visual data and process them to establish meaningful information that can used by the farmers to maximize their crop. Furthermore, the findings of the proposed system showed that this was viable solution and identified the defected tomatoes with the success rate of 90%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexandratos, N., Bruinsma, J.: World agriculture towards 2030/2050: the 2012 revision. FAO: ESA Working paper No. 12-03 (2012)
Roser, M., Ritchie, H.: Yields and land use in agriculture. Our World in Data (2018). https://ourworldindata.org/yields-and-land-use-in-agriculture#breakdown-of-global-land-area-today. Accessed 01 Apr 2019
Smith, V.L.: Hunting and Gathering Economies: The World of Economics, pp. 330–338. Palgrave Macmillan, London (1991)
Pringle, H.: The Slow Birth of Agriculture. Science 282, 1446 (1998)
Matson, P.A., et al.: Agricultural intensification and ecosystem properties. Science 277(5325), 504–509 (1997)
Tran, D.V., Nguyen, N.V.: The concept and implementation of precision farming and rice integrated crop management systems for sustainable production in the twenty-first century. Int. Rice Comm. Newsl. 55, 91–113 (2006)
Wang, X., et al.: Development of visualization system for agricultural UAV crop growth information collection. IFAC-PapersOnLine 51(17), 631–636 (2018)
Ivushkin, K., et al.: UAV based soil salinity assessment of cropland. Geoderma 338, 502–512 (2019)
Senthilnath, J., et al.: Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV. Biosyst. Eng. 146, 16–32 (2016)
Kerkech, M., Hafiane, A., Canals, R.: Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Comput. Electron. Agric. 155, 237–243 (2018)
Rieke, M., et al.: High-precision positioning and real-time data processing of UAV systems. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 38(1/C22), 119–124 (2011)
Berkner: Academic Press Library in Signal Processing 4, 79–94 (2014)
Zhao, Z., Liu, H.: Spectral feature selection for supervised and unsupervised learning. In: Proceedings of the 24th International Conference on Machine Learning, pp. 1151–1157. ACM (2007)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of the International Conference on Image Processing, vol. 1, p. I. IEEE, Rochester (2002)
Deepthi, R.S., Sankaraiah, S.: Implementation of mobile platform using Qt and OpenCV for image processing applications. In: IEEE Conference on Open Systems, pp 284–289. IEEE, Langkawi (2011)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Altınbaş, M.D., Serif, T. (2019). Detecting Defected Crops: Precision Agriculture Using Haar Classifiers and UAV. In: Awan, I., Younas, M., Ünal, P., Aleksy, M. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2019. Lecture Notes in Computer Science(), vol 11673. Springer, Cham. https://doi.org/10.1007/978-3-030-27192-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-27192-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27191-6
Online ISBN: 978-3-030-27192-3
eBook Packages: Computer ScienceComputer Science (R0)