References
- Anderson, H. B., H. Nilsen, H, Tommervik, S. R. Karlsen, S. Nagai, and E. J. Cooper, 2016: Using ordinary digital cameras in place of near-infrared sensors to derive vegetation indices for phenology studies of high arctic vegetation. Remote Sensing 8, 847pp. https://doi.org/10.3390/rs8100847
- Araus, J. L., S. C. Kefauver, M. Z. Allah, M. S. Olsen, and J. E. Cairns, 2018: Translating highthroughput phenotyping into genetic gain. Trends in Plant Science 23, 451-466. https://doi.org/10.1016/j.tplants.2018.02.001
- Bruin, J. L. D., and P. Pedersen, 2009: New and old soybean cultivar responses to plant density and intercepted light. Crop Science 49(6), 2225-2232. https://doi.org/10.2135/cropsci2009.02.0063
- Das, B., R. N. Sahoo, S. Pargal, G. Krishna, V. K. Gupta, R. Verma, and C. Viswanathan, 2016: Measuring leaf index from color digital image of wheat crop. Journal of Agrometeorology 18(1), 22-28.
- Easlon, H. M., and A. J. Bloom, 2014: Easy Leaf Area: Automated digital image analysis for rapid and accurate measurement. Applications in Plant Science. doi: 10.3732/apps.1400033
- Garcia, J. R., P. Almendros, and M. Quemada, 2012: Ground cover and leaf area index relationship in grass, legume and crucifer crop. Plant Soil Environment 50(8), 385-390.
- Gee, C. H., and J. Bossu, 2008: Crop/weed discrimination in perspective agronomic images. Computers and Electronic in Agriculture 60(1), 49-59. https://doi.org/10.1016/j.compag.2007.06.003
- Gitelsona, A. A., Y. J. Kaufmanb, R. Starkc, and D. Rundquista, 2002: Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment 80, 76-87. https://doi.org/10.1016/S0034-4257(01)00289-9
- Gonia, E. D., D. M. Oosterhuis, A. C. Bibi, and L. C. Purcell, 2012: Estimating light interception by cotton using a digital imaging technique. American Journal of Experimental Agriculture 2(1), 1-8. https://doi.org/10.9734/AJEA/2012/879
- Hamuda, E., M. Glavin, and E. Jones. 2016: A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronic in Agriculture 125, 184-199. https://doi.org/10.1016/j.compag.2016.04.024
- Jovanovic, N. Z., and G. Annandale, 1998: Measurement of radiant interception of crop canopies with the LAI-2000 plant canopy analyzer. South African Journal of Plant and Soil 15(1), 6-13. https://doi.org/10.1080/02571862.1998.10635107
- Kataoka, T., T. Kaneko, H. Okamoto, and S. Hata, 2003: Crop growth estimation system using machine vision. In Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).
- Li, L., Q, Zhabg, and D. Huang, 2014: A review of imaging techniques for plant phenotyping. Sensors 14, 20078-20111. https://doi.org/10.3390/s141120078
- Liu, J., and E. Pattey, 2010: Retrieval of leaf area index from top-of canopy digital photography over agricultural crops. Agricultural and Forest Meteorology 150(11), 1485-1490. https://doi.org/10.1016/j.agrformet.2010.08.002
- Louhaichi, M., M. M. Borman, and D. E. Johnson, 2001: Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International 16, 65-70. https://doi.org/10.1080/10106040108542184
- Mao, W., Y. Wang, and Y. Wang, 2003: Real-time detection of between-row weeds using machine vision. Written for presentation at the 2003 ASAE Annual International Meeting Sponsored by ASAE Riviera Hotel and Convention Center Las Vegas, Nevada, USA 27-30 July 2003 Paper Number 031004.
- Meyer, G. E., T. W. Hindman, and K. Lakshmi, 1999: Machine vison detection parameters for plant species identification. Meyer, G. E., J. A. De Shazer (Eds), Precision Agriculture and Biological Quality, Proceeding of SPIE Vol 3543, 327-335.
- Meyer, G. E., and J. C. Neto, 2008: Verification of color vegetation indices for automated crop imaging applications. Computers and Electronic in Agriculture 63(2), 282-293. https://doi.org/10.1016/j.compag.2008.03.009
- Nasirzadehdizaji, R., F. B. Sanli, S. Abdikan, Z. Cakir, A. Sekertekin, and M. Ustuner, 2019: Sensitivity analysis of multi-temporal sentinel-1 SAR parameters to crop height and canopy coverage. Applied Science 9, 655pp. https://doi.org/10.3390/app9040655
- Neeto, A. F. A., R. N. Martins, G. S. A. Souza, G. M. Araujo, S. L. H. Almeida, and V. A. Capelini, 2018: Segmentation of RGB images using different vegetation indices and thresholding methods. Nativa Sinop 6(4), 389-394. https://doi.org/10.31413/nativa.v6i4.5405
- Otus, N., 1979: A threshold selection method from gray-level histogram. IEEE transactions on Systems, Man, and Cybernetics 9, 62-66. https://doi.org/10.1109/TSMC.1979.4310076
- Park, H. K., W. Y. Choi, N. H. Back, S. S. Kim, B. K. Kim, and K. K. Kim, 2004: Estimation of leaf area index by plant canopy analyzer in rice. Korean Journal of Crop Science 49(6), 463-467.
- Patrignani, A., and T. E. Ochsner, 2015: Canopeo A powerful new tool for measuring fractional green canopy cover. Agronomy Journal 107(6), 2312-2320. https://doi.org/10.2134/agronj15.0150
- Perez, A. J., F. Lopez, J. V. Benlloch, and S. Christensen, 2000: Color and shape analysis techniques for weed detection in cereal fields. Computers and Electronic in Agriculture 25(3), 197-212. https://doi.org/10.1016/S0168-1699(99)00068-X
- Purcell, L. C., 2000: Soybean canopy coverage and light interception measurements using digital imagery. Crop Science 40(3), 834-837. https://doi.org/10.2135/cropsci2000.403834x
- Richter, G. L., A. J. Zanon, N. A. Streck, J. V. C. Guedes, B. Kraulich, T. S. M. D Rocha, J. E. M. Winck, and J. C. Cera, 2014: Estimating leaf area of modern soybean cultivars by a non-destructive method. Crop Production and Management 73(4), 416-425.
- Setiyono, T. D., A. Weiss, J. E. Specht, K. G. Cassman, and A. Dobermann, 2008: Leaf area index simulation in soybean grown udder nearoptimal conditions. Field Crops Research 108(1), 82-92. https://doi.org/10.1016/j.fcr.2008.03.005
- Shepherd, M. J., L. E. Lindey, and A. J. Lindsey, 2018: Soybean canopy cover measured with Canopeo compared with light interception. Agricultural & Environmental Letters 3(1), 1-3. https://doi.org/10.2134/ael2017.01.0001tr
- Shiraiw, T., Y. Kawasaki, and K. Homma, 2011: Estimation of crop radiation use efficiency. Japanese Journal of Crop Science 80(3), 360-364. https://doi.org/10.1626/jcs.80.360
- Stewart, A. M., 2007: Measuring canopy coverage with digital imaging. Communication in Soil Science and Plant Analysis 38, 895-902. https://doi.org/10.1080/00103620701277718
- Tagliapietra, E. L., N. A. Streck, T. S. M. Rocha, G. L. Richter, M. R. Silva, J. C. Cera, J. V. C. G. Guedes, and A. J. Zanon, 2018: Optimum leaf area index to reach soybean yield potential in subtropical environment. Agronomy Journal 1109(3), 932-938.
- Woebbecke, D., G. M. Meyer, K. Von, and D. Mortensen, 1993: Plant species identification, size, and enumeration using machine vision techniques on near-binary images, in SPIE Conference on Optics in Agriculture and Forestry, Boston, USA, 208-219.
- Woebbecke, D. M., G. M. Meyer, K. V. Bargen, and D. A. Mortensen, 1995: Color indices for weed identification under various soil, residue, and lighting conditions. Transaction of the American Society of Agricultural and Biological Engineers 38(1), 259-269. https://doi.org/10.13031/2013.27838
- Yang, W., S. Wang, X. Zhao, J. Zhang, and J. Feng, 2015: Greenness identification based on HSV decision tree. Information Processing in Agriculture 2, 149-160. https://doi.org/10.1016/j.inpa.2015.07.003