Acquisition of Agronomic Images with Sufficient Quality by Automatic Exposure Time Control and Histogram Matching
Agronomic images in Precision Agriculture are most times used for crop lines detection and weeds identification; both are a key issue because specific treatments or guidance require high accuracy. Agricultural images are captured in outdoor scenarios, always under uncontrolled illumination. CCD-based cameras, acquiring these images, need a specific control to acquire images of sufficient quality for greenness identification from which the crop lines and weeds are to be extracted. This paper proposes a procedure to achieve images with sufficient quality by controlling the exposure time based on image histogram analysis, completed with histogram matching. The performance of the proposed procedure is verified against testing images.
KeywordsUncontrolled illumination Automatic Exposure Time Histogram analysis Histogram matching Machine Vision Precision Agriculture
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- 2.Romeo, J., Pajares, G., Montalvo, M., Guerrero, J.M., Guijarro, M., Ribeiro, A.: Crop Row Detection in Maize Fields Inspired on the Human Visual Perception. The Scientific World Journal 2012, Article ID 484390, 10 pages (2012), doi:10.1100/2012/484390Google Scholar
- 4.Hague, T., Tillett, N., Wheeler, H.: Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture 1(1), 95–113 (2006)Google Scholar
- 11.Fontaine, V., Crowe, T.G.: Development of line-detection algorithms for local positioning in densely seeded crops. Canadian Biosystems Engineering 48, 7.19–7.29 (2006)Google Scholar
- 15.Kremens, R., Sampat, N., Venkataraman, S., Yeh, T.: System implications of implementing auto-exposure on consumer digital cameras. In: SPIE Electronic Imaging 1999 Conference, vol. 3650 (January 1999)Google Scholar
- 16.Jiang, T., Kuhnert, K.D., Nguyen, D., Kuhnert, L.: Multiple templates auto exposure control based on luminance histogram for onboard camera. In: Proc. IEEE Int. Conf. on Computer Science and Automation Engineering (CSAE), Shanghai, China, June 10-12, vol. 3, pp. 237–241 (2011)Google Scholar
- 17.Vuong, Q.K., Yun, S.H., Kim, S.: A New Auto Exposure and Auto White-Balance Algorithm to Detect High Dynamic Range Conditions Using CMOS Technology. In: Proc. of the World Congress on Engineering and Computer Science (WCECS) 2008, San Francisco, USA, October 22-24 (2008)Google Scholar
- 18.Nourani-Vatani, N., Roberts, J.: Automatic Camera Exposure Control. In: Proc. of the Australian Conf. Robotics and Automation, Brisbane, Australia, pp. 1–6 (December 2007)Google Scholar
- 19.SVS-VISTEK. The Focal Point of Machine Vision (2013), http://www.svsvistek.com/ (accessed on May 1, 2013)
- 20.National Instruments, http://spain.ni.com/ (accessed on May 1, 2013)
- 21.Kreuznach, S.: C-Mount Lenses compact series 1, http://wwwschneiderkreuznach.com/en/industrial-solutions/lenses-and-accessories/products/ (accessed on May 1, 2013)
- 22.Kreuznach, S.: Tips and Tricks (2013), http://www.schneiderkreuznach.com/en/photo-imaging/product-field/b-w-fotofilter/products/filtertypes/special-filters/486-uvir-cut/ (accessed on 1 May 1, 2013)