Tian Weixin, Chen Chen, Xiaoyu Song, Dong Ren, and Jihua Wang
[1] C.J. Zhao, Advances of research and application in remotesensing for agriculture, Transactions of the Chinese Societyfor Agricultural Machinery, 12, 2014, 277–293. [2] F. Kuri, A. Murwir, K.S. Murwira, and M. Masochab, Sci-entific predicting maize yield in Zimbabwe using dry dekadsderived from remotely sensed Vegetation Condition Index, International Journal of Applied Earth Observation and Geoinformation, 33, 2014, 39–46. [3] U. Thomas, D. Philippe, B. Christian, R. Franz, et al.,Retrieving the bioenergy potential from maize crops usinghyperspectral remote sensing, Remote Sensing, 5, 2013, 254–273. [4] H.A. Jin, J.D. Wang, and Y.C. Bo, Estimation on regionalmaize yield based on assimilation of remote sensing data andcrop growth model, Transactions of the CSAE, 28(6), 2012,162–173. [5] S. Kenneth, J. Gab-Sue, L. Robert, and S.E. John, Long-term agroecosystem research in the Central Mississippi RiverBasin: Hyperspectral remote sensing of reservoir water quality,Journal of Environmental Quality, 44, 2015, 71–83. [6] J. Bellvert, J. Marsl, J. Girona, and P.J. Zarco-Tejada, Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermalimagery, Irrigation Science, 33, 2016, 81–93. [7] S. Hazratkulova, R.C. Sharma, S. Alikulov, S. Islomov, et al.,Analysis of genotypic variation for normalized difference vegetation index and its relationship with grain yield in winterwheat under terminal heat stress, Plant Breeding, 131(6), 2012,716–721. [8] B. Fatiha, A. Abdelkader, H. Latifa, and E. Mohamed, Spatiotemporal analysis of vegetation by vegetation indices frommulti-dates satellite images: Application to a semi arid areain ALGERIA, Energy Procedia, 36, 2013, 667–675. [9] H. Jin and L. Eklundh, A physically based vegetation indexfor improved monitoring of plant phenology. Remote Sensingof Environment, 152, 2014, 512–525. [10] Q.H. Liao, Chlorophyll content mapping of crops in HeiheRiver Basin based on hyper spectral vegetation indices, ChineseSociety of Agricultural Engineering, 31, 2015, 159–163. [11] Z.F. Zhai, Z. Xu, X.Q. Zhou, L.L. Wang, et al., Recognition ofhazard grade for cotton blind stinkbug based on Naive Bayesianclassifier, Chinese Society of Agricultural Engineering, 31,2015, 204–211. [12] P. Jaroonrut and P. Charnchai, Segmentation of white bloodcells and comparison of cell morphology by linear and na¨ıveBayes classifiers, BioMedical Engineering OnLine, 14(1), 2015,63–81. [13] O. Kisi, Pan evaporation modeling using least square supportvector machine, multivariate adaptive regression splines andM5 model tree, Journal of Hydrology, 528, 2015, 312–320. [14] Y.Y. Gu and L.X. Hu, The application of M5 model treeon optimal load distribution in thermal power plants, EnergyConservation Technology, 31, 2013, 125–131. [15] J. Zhang and W.J. Hou, Research and analysis of methodof ranking micro-blog search results based on binary logisticmodel, Lecture Notes in Computer Science, 7719, 2013, 830–842. [16] S.S. Jun, S.L. Won, and R. Ehsani, Postharvest citrus massand size estimation using a logistic classification model and awatershed algorithm, Biosystems Engineering, 113(1), 2012,42–53. [17] A. Martin and M. Reza Emami, Just-in-time cooperativesimultaneous localization and mapping: A robust particle filterapproach, International Journal of Robotics and Automation,29(2), 2014, 66–78. [18] A. H. Jabbari, O. Giuseppe, and B. Hossein, An adaptivescheme for image-based visual servoing of an underactuatedUAV, International Journal of Robotics and Automation, 29(1),2014, 92–104. [19] Y. Wang and I.H. Witten, Induction of model trees for predicting continuous classes, Poster Papers of the Ninth EuropeanConference on Machine Learning, Prague, Czech Republic,April 23–25, 1997, 128–137. [20] S. Sakhare and M.C. Deo, Derivation of wave spectrum usingdata driven methods, Marine Structures, 22(3), 2009, 594–609.
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