Early sensing of peanut leaf spot using spectroscopy and thermal imaging

References

• Aquino VM, Shokes FM, Berger RD, Gorbet DW, Kucharek TA. 1992. Relationship among late leaf spot, healthy leaf area duration, canopy reflectance and pod yield of peanut. Phytopath. 82:546–552.
   Web of Science ®Google Scholar
• Berk A, Anderson GP, Acharya PK, Bernstein LS, Muratov L, Lee J, Fox MJ, Adler-Golden SM, Chetwynd JH, Hoke ML, et al. 2005. MODTRAN5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options. Proc SPIE Int Soc Opt Eng. 5655:88.
   Google Scholar
• Bruce L, Mathur A, Byrd J. 2006. Denosing and wavelet based feature extraction of MODIS multi temporal vegetation signatures. GI Sci Remote Sens. 43:67–77.
   Web of Science ®Google Scholar
• Butler DR, Wadia KDR, Reddy RK, Das ND, Johnson B, Kumar M, Murthy K, Srinivas B, Srivastava NN. 2000. A weather based scheme to advise on limited chemical control of groundnut leafspot disease in India. Exp Agr. 36:469–478.
   Web of Science ®Google Scholar
• Chen B, Wang K, Li S, Wang J, Bai J, Xiao C, Lai J. 2008. Spectrum characteristics of cotton canopy infected with Verticillium wilt and inversion of severity level. Proc Comput Comput Technologies Agric. 2:1169–1180.
   Google Scholar
• Choi YH, Tapias EC, Kim HK, Lefeber AWM, Erkelens C, Verhoeven JTJ, Brzin J, Zel J, Verpoorte R. 2004. Metabolic discrimination of Catharanthus roseus leaves infected by phytoplasma using 1H-NMR spectroscopy and multivariate data analysis. Plant Physiol. 135:2398–2410.
   PubMed Web of Science ®Google Scholar
• EBTP. 2015. European biofuels technology platform. Oil Crops for Production of Advanced Biofuels; [cited 2015 Sep 28]. Available from: http://wwwbiofuelstpeu/oil_cropshtml
   Google Scholar
• Fahlgren N, Gehan M, Baxter I. 2015. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin in Plant Biol. 24:93–99.
   PubMed Web of Science ®Google Scholar
• Falcone JA, Gomez R. 2005. Mapping impervious surface type and sub-pixel abundanceusing Hyperion hyperspectral imagery. Geocarto Int. 20:3–10.
   Google Scholar
• Goggin F, Lorence A, Topp C. 2015. Applying high-throughput phenotyping to plant–insect interactions: picturing more resistant crops. Curr Opin in Insect Sci. 9:69–76.
   Web of Science ®Google Scholar
• Hahn F. 2009. Actual pathogen detection: sensors and algorithms—a review. Algorithms. 2:301–338.
   Google Scholar
• Jackson RD. 1986. Remote sensing of biotic and abiotic plant stress. Annu Rev Phytopathol. 24:265–287.
   Web of Science ®Google Scholar
• Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH. 1993. The spectral image processing system (SIPS). Interactive visualisation and analysis of imaging spectrometer data. Remote Sens Environ. 44:145–163.
   Web of Science ®Google Scholar
• Lantao L, Jianwei L, Shanqin W, Yi M, Quanquan W, Xiaokun L, Rihuan C, Tao R. 2016. Methods for estimating leaf nitrogen concentration of winter oilseed rape (Brassica napus L.) using in situ leaf spectroscopy. Ind Crops Prod. 91:194–204.
   Web of Science ®Google Scholar
• Lins EC, Junior JB, Marcassa LG. 2009. Detection of citrus canker in citrus plants using laser induced fluorescence spectroscopy. Precis Agric. 10:319–330.
   Web of Science ®Google Scholar
• Lucas JA. 1998. Plant pathology and plant pathogens. 3rd ed. Oxford: Blackwell Science.
   Google Scholar
• MacLeod A, Head J, Gaunt A. 2004. An assessmen t of the potential economic impact of Thrips palmi on horticulture in England and the significance of a successful eradication campaign. Crop Protection. 23:601–610.
   Web of Science ®Google Scholar
• Mahlein A. 2016. Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and palnt phenotyping. Plant Dis. 100:241–251.
   PubMed Web of Science ®Google Scholar
• Mutka A, Bart R. 2015. Image-based phenotyping of plant disease symptoms. Front Plant Sci. 5:734.
   PubMed Web of Science ®Google Scholar
• Nilsson HE. 1995. Remote sensing and image analysis in plant pathology. Can J Plant Path. 17:154–166.
   Web of Science ®Google Scholar
• Omran ESE. 2016. Simplified imaging spectroscopy for rapid spectral discrimination of soil minerals. Eurasian Soil Sci. Forthcoming.
   Google Scholar
• Omran ESE. 2015. Remote estimation of vegetation parameters using narrowband sensor for precision agriculture in arid environment. Egypt J Soil Sci. 55. Forthcoming.
   Google Scholar
• Pu R, Gong P, Biging GS, Larrieu MR. 2003. Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index. IEEE Trans Geosci Remote Sens. 41:916–921.
   Web of Science ®Google Scholar
• Purcell DE, O‘Shea MG, Johnson RA, Kokot S. 2009. Near-infrared spectroscopy for the prediction of disease rating for Fiji leaf gall in sugarcane clones. Appl Spectrosc. 63:450–457.
   PubMed Web of Science ®Google Scholar
• Qin J, Burks F, Ritenour MA, Bonn WG. 2009. Detection of citrus canker using hyper-spectral reflectance imaging with spectral information divergence. J Food Eng. 93:183–191.
   Web of Science ®Google Scholar
• Sankaran S, Ashish M, Reza E, Cristina D. 2010. A review of advanced techniques for detecting plant disease. Comput Electron Agric. doi:10.1016/j.compag.2010.02.007
   Web of Science ®Google Scholar
• Schlerf M. 2011. Hyperspectral remote sensing applications for vegetation studies. In: Navalgund RR, Ray SS, editors. Hyperspectral data analysis techniques and applications. 1st ed. Dehra Dun: p. 79–99.
   Google Scholar
• Shafri HZM, Hamdan N. 2009. Hyperspectral imagery for mapping disease infection in oil palm plantation using vegetation indices and red edge techniques. Am J Appl Sci. 6:1031–1035.
   Google Scholar
• Stafford JV. 2000. Implementing precision agriculture in the 21th century. J Agric Eng Res. 76:267–275.
   Web of Science ®Google Scholar
• Thenkabail PS, Enclona EA, Ashton MS, Meer BVD. 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ. 91:354–376.
   Web of Science ®Google Scholar
• Thenkabail PS, Smith RB, De Pauw E 1999. Hyperspectral vegetation indices for determining agricultuml crop characteristics. CEO Research Publication Series #I, ISBN: 0-9671303-0-1. New Haven, CT: Center for Earth Observation, Yale University; p. 47.
   Google Scholar
• Wahabzada M, Mahlein A, Bauckhage C, Steiner M, Oerke E, Kersting K. 2015. Metro maps of plant disease dynamics-automated mining of differences using hyperspectral images. PloS One. 10:e0116902.
   PubMed Web of Science ®Google Scholar
• Walsh SJ, Amy LM, Carlos FM, Yang S, Julie PT, Augusto G, Rachel A. 2008. QuickBird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: implications for control and land use management. Remote Sens Environ. 112:1927–1941.
   Web of Science ®Google Scholar
• Xu H, Zhu S, Ying Y, Jiang H. 2006. Early detection of plant disease using infrared thermal imaging. In: Chen Y-R, Meyer GE, Tu S-I, editors. Proceedings of SPIE 6381, Optics for Natural Resources, Agriculture, and Foods. 638110. doi:10.1117/12.685534
   Google Scholar
• Yao X, Jia W, Si H, Guo Z, Tian Y. 2014. Exploring novel bands and key index for evaluating leaf equivalent water thickness in wheat using hyperspectra influenced by nitrogen. Plos One. 9:e96352.
   PubMed Web of Science ®Google Scholar