Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

Computers and Electronics in Agriculture

Volumn 151, Issue , 2018, Pages 61-69

  Chlingaryan, Anna ^a   Sukkarieh, Salah ^a   Whelan, Brett ^b  

^a UNIVERSITY OF SYDNEY   (Australia)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

Decision making; Features extraction; Information fusion; Predictive modelling; Vegetation indices

Indexed keywords

ARTIFICIAL INTELLIGENCE; COST EFFECTIVENESS; CROPS; DECISION MAKING; DECISION SUPPORT SYSTEMS; ENVIRONMENTAL IMPACT; HYBRID SYSTEMS; INFORMATION FUSION; NITROGEN; OPERATING COSTS; PRECISION AGRICULTURE; REMOTE SENSING; SIGNAL PROCESSING;

DECISION SUPPORT TOOLS; ESTIMATION AND DECISIONS; FEATURES EXTRACTION; MACHINE LEARNING APPROACHES; PREDICTIVE MODELLING; REMOTELY SENSED DATA; SIGNAL PROCESSING TECHNIQUE; VEGETATION INDEX;

LEARNING SYSTEMS;

EID: 85048488279     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2018.05.012     Document Type: Review

References (141)

1. Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., Notarnicola, C., Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data. Remote Sens., 7, 2015, 15841.
2. Andrews, M., Raven, J.A., Lea, P.J., Do plants need nitrate? The mechanisms by which nitrogen form affects plants. Ann. Appl. Biol. 163 (2013), 174–199.
3. Apostol, S., Viau, A., Tremblay, N., A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn. Can. J. Remote Sens. 33 (2007), 150–161.
4. Aqeel-ur-Rehman, Abbasi, A.Z., Islam, N., Shaikh, Z.A., A review of wireless sensors and networks’ applications in agriculture. Comp. Stand. Interf. 36 (2014), 263–270.
5. Barati, S., Rayegani, B., Saati, M., Sharifi, A., Nasri, M., Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. Egypt. J. Remote Sens. Space Sci. 14 (2011), 49–56.
6. Barnes, E.M., Sudduth, K.A., Hummel, J.W., Lesch, S.M., Corwin, D.L., Yang, C., Daughtry, C.S.T., Bausch, W.C., Remote- and ground-based sensor techniques to map soil properties. Photogramm. Eng. Remote Sens. 6 (2003), 619–630.
7. Behmann, J., Mahlein, A., Rumpf, T., Römer, C., Plümer, L., A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Prec. Agric. 16 (2015), 239–260.
8. Bellvert, J., Marsal, J., Girona, J., Gonzalez-Dugo, V., Fereres, E., Ustin, S., Zarco-Tejada, P., Airborne thermal imagery to detect the seasonal evolution of crop water status in peach, nectarine and saturn peach orchards. Remote Sens., 8, 2016, 39.
9. Birth, G.S., McVey, G.R., Measuring the color of growing turf with a reflectance spectrophotometer. Agron. J. 60 (1968), 640–643.
10. Bishop, C.M., Pattern Recognition and Machine Learning (Information Science and Statistics). 2006, Springer-Verlag, New York Inc.
11. Bolster, K.L., Martin, M.E., Aber, J.D., Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectances: a comparison of statistical methods. Can. J. For. Res. 26 (1996), 590–600.
12. Bolton, D.K., Friedl, M.A., Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric. For. Meteorol. 173 (2013), 74–84.
13. Bose, P., Kasabov, N.K., Bruzzone, L., Hartono, R.N., Spiking neural networks for crop yield estimation based on spatiotemporal analysis of image time series. IEEE Trans. Geosci. Remote Sens. 54 (2016), 6563–6573.
14. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
15. Campbell, J.B., Wynne, R.H., 2011. Introduction to Remote Sensing. fifth ed. Guildford Press.
16. Cao, Q., Miao, Y., Feng, G., Gao, X., Li, F., Liu, B., Yue, S., Cheng, S., Ustin, S.L., Khosla, R., Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Comput. Electr. Agric. 112 (2015), 54–67.
17. Cao, Q., Miao, Y., Li, F., Gao, X., Liu, B., Lu, D., Chen, X., Developing a new crop circle active canopy sensor-based precision nitrogen management strategy for winter wheat in North China Plain. Prec. Agric. 18 (2017), 2–18.
18. Castaldi, F., Castrignanò A., Casa, R., A data fusion and spatial data analysis approach for the estimation of wheat grain nitrogen uptake from satellite data. Int. J. Remote Sens. 37 (2016), 4317–4336.
19. Chen, P., Haboudane, D., Tremblay, N., Wang, J., Vigneault, P., Li, B., New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens. Environ. 114 (2010), 1987–1997.
20. Chen, D., Suter, H., Islam, A., Edis, R., Freney, J.R., Walker, C.N., Prospects of improving efficiency of fertiliser nitrogen in Australian agriculture: a review of enhanced efficiency fertilisers. Soil Res. 46 (2008), 289–301.
21. Cilia, C., Panigada, C., Rossini, M., Meroni, M., Busetto, L., Amaducci, S., Boschetti, M., Picchi, V., Colombo, R., Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sens., 6, 2014, 6549.
22. Clark, R.N., Roush, T.L., Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. J. Geophys. Res.: Solid Earth 89 (1984), 6329–6340.
23. Curran, P.J., Remote sensing in agriculture: an introductory review. J. Geogr. 86 (1987), 147–156.
24. Dai, X., Zhou, X., Jia, D., Xiao, L., Kong, H., He, M., Managing the seeding rate to improve nitrogen-use efficiency of winter wheat. Field Crops Res. 154 (2013), 100–109.
25. Dai, F., Zhou, Q., Lv, Z., Wang, X., Liu, G., Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecol. Indicat. 45 (2014), 184–194.
26. Dalponte, M., Bruzzone, L., Gianelle, D., Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens. Environ. 123 (2012), 258–270.
27. Diacono, M., Rubino, P., Montemurro, F., Precision nitrogen management of wheat A review. Agron. Sustain. Dev. 33 (2013), 219–241.
28. Drummond, S.T., Sudduth, K.A., Joshi, A., Birrel, S.J., Kitchen, N.R., Statistical and neural methods for site-specific yield prediction. Trans. Am. Soc. Agric. Eng. 46 (2003), 5–14.
29. Du, Z., Jeffrey, J.P.T., Advances in Machine Learning Applications in Software Engineering. 2007, IGI Global, Hershey, PA, USA.
30. Elfatih, M.A., Fethi, B.A., Riyad, I., Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. Int. J. Remote Sens. 34 (2013), 712–728.
31. El-Shikha, D.M., Waller, P., Hunsaker, D., Clarke, T., Barnes, E., Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agric. Water Manage. 92 (2007), 183–193.
32. Engman, E.T., Gurney, R.J., Remote sensing in hydrology. 1991, Chapman and Hall, London.
33. Farifteh, J., Van der Meer, F., Atzberger, C., Carranza, E.J.M., Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN). Remote Sens. Environ. 110 (2007), 59–78.
34. Ferguson, T.S., A Bayesian analysis of some nonparametric problems. Ann. Statist. 1 (1973), 209–230.
35. Fortin, J.G., Anctil, F., Parent, L., Bolinder, M.A., Site-specific early season potato yield forecast by neural network in Eastern Canada. Prec. Agric. 12 (2011), 905–923.
36. Frausto-Solis, J., Gonzalez-Sanchez, A., Larre, M., 2009. A New Method for Optimal Cropping Pattern, In: Aguirre, A.H., Borja, R.M., Garciá C.A.R. (Eds.), MICAI 2009: Advances in Artificial Intelligence: 8th Mexican International Conference on Artificial Intelligence, Guanajuato, México, November 9-13, 2009. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 566–577.
37. Gao, B., NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58 (1996), 257–266.
38. Goldstein, A., Fink, L., Meitin, A., Bohadana, S., Lutenberg, O., Ravid, G., Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist's tacit knowledge. Prec. Agric., 2017.
39. Gontia, N.K., Tiwari, K.N., Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry. Agric. Water Manage. 95 (2008), 1144–1152.
40. Gonzalez-Sanchez, A., Frausto-Solis, J., Ojeda-Bustamante, W., Predictive ability of machine learning methods for massive crop yield prediction. Spanish J. Agric. Res. 12 (2014), 313–328.
41. Goron, T., Nederend, J., Stewart, G., Deen, B., Raizada, M., Mid-season leaf glutamine predicts end-season maize grain yield and nitrogen content in response to nitrogen fertilization under field conditions. Agronomy, 7, 2017, 41.
42. Govender, M., Dye, P.J., Weiersbye, I.M., Witkowski, E.T.F., Ahmed, F., 2009. Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water SA 35.
43. Griffiths, T.L., Ghahramani, Z., The Indian buffet process: an introduction and review. J. Mach. Learn. Res. 12 (2011), 1185–1224.
44. Grossman, Y.L., Ustin, S.L., Jacquemoud, S., Sanderson, E.W., Schmuck, G., Verdebout, J., Critique of stepwise multiple linear regression for the extraction of leaf biochemistry information from leaf reflectance data. Remote Sens. Environ. 56 (1996), 182–193.
45. Guo, P., Li, M., Luo, W., Tang, Q., Liu, Z., Lin, Z., Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach. Geoderma 237 (2015), 49–59.
46. Gupta, R.P., Remote Sensing Geology. 2003, Springer-Verlag, Berlin Heidelberg, Germany.
47. Gutiérrez, S., Diago, M.P., Fernández-Novales, J., Tardaguila, J., Vineyard water status assessment using on-the-go thermal imaging and machine learning. PLOS One, 13, 2018, e0192037.
48. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B., Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90 (2004), 337–352.
49. Haboudane, D., Tremblay, N., Miller, J.R., Vigneault, P., Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Trans. Geosci. Remote Sens. 46 (2008), 423–437.
50. Hansen, P.M., Schjoerring, J.K., Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 86 (2003), 542–553.
51. Heremans, S., Dong, Q., Zhang, B., Bydekerke, L., Van Orshoven, J., Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data. APPRES, 9, 2015, 097095.
52. Holmgren, P., Thuresson, T., Satellite remote sensing for forestry planning—A review. Scandinavian J. For. Res. 13 (1998), 90–110.
53. Huang, Z., Turner, B.J., Dury, S.J., Wallis, I.R., Foley, W.J., Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sens. Environ. 93 (2004), 18–29.
54. Huete, A.R., A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25 (1988), 295–309.
55. Hultquist, C., Chen, G., Zhao, K., A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests. Remote Sens. Lett. 5 (2014), 723–732.
56. Jaafar, H.H., Ahmad, F.A., Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands. Int. J. Remote Sens. 36 (2015), 4570–4589.
57. Jin, X., Kumar, L., Li, Z., Xu, X., Yang, G., Wang, J., Estimation of winter wheat biomass and yield by combining the AquaCrop model and field hyperspectral data. Remote Sens., 8, 2016, 972.
58. Johnson, D.M., An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sens. Environ. 141 (2014), 116–128.
59. Jones, D.I.H., Moseley, G., 1993. Laboratory methods for estimating nutritive quality. In: Davies, A., Baker, R.D., Grant, S.A., Laidlaw, A.S. (Eds.), Sward Measurement Handbook, second ed., pp. 265–283.
60. Jordan, C.F., Derivation of leaf-area index from quality of light on the forest floor. Ecology 50 (1969), 663–666.
61. Kaul, M., Hill, R.L., Walthall, C., Artificial neural networks for corn and soybean yield prediction. Agric. Syst. 85 (2005), 1–18.
62. Knipling, E.B., Physical and physiological bases for the reference of visible and near infrared radiation from vegetation. Remote Sens. Environ. 1 (1970), 155–159.
63. Kokaly, R.F., Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sens. Environ. 75 (2001), 153–161.
64. Kuwata, K., Shibasaki, R., 2015. Estimating crop yields with deep learning and remotely sensed data, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 858–861.
65. Lamb, D.W., Brown, R.B., PA—Precision agriculture. J. Agric. Eng. Res. 78 (2001), 117–125.
66. Lamb, D.W., Steyn-Ross, M., Schaare, P., Hanna, M.M., Silvester, W., Steyn-Ross, A., Estimating leaf nitrogen concentration in ryegrass (Lolium spp.) pasture using the chlorophyll red-edge: Theoretical modelling and experimental observations. Int. J. Remote Sens. 23 (2002), 3619–3648.
67. Li, F., Miao, Y., Hennig, S.D., Gnyp, M.L., Chen, X., Jia, L., Bareth, G., Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Prec. Agric. 11 (2010), 335–357.
68. Li, Z., Wang, J., Tang, H., Huang, C., Yang, F., Chen, B., Wang, X., Xin, X., Ge, Y., Predicting grassland leaf area index in the meadow steppes of northern china: a comparative study of regression approaches and hybrid geostatistical methods. Remote Sens., 8, 2016, 632.
69. Liang, L., Di, L., Zhang, L., Deng, M., Qin, Z., Zhao, S., Lin, H., Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sens. Environ. 165 (2015), 123–134.
70. Liu, J., Goering, C.E., Tian, L., 2001. A neural network for setting target corn yields. 44, pp. 705–713.
71. Lukina, E.V., Freeman, K.W., Wynn, K.J., Thomason, W.E., Mullen, R.W., Stone, M.L., Solie, J.B., Klatt, A.R., Johnson, G.V., Elliott, R.L., Raun, W.R., Nitrogen fertilization optimization algorithm based on in-season estimates of yield and plant nitrogen uptake. J. Plant Nutr. 24 (2001), 885–898.
72. MacKerron, D.K.L., Young, M.W., Davies, H.V., A method to optimize N-application in relation to soil supply of N, and yield of potato. Fragoso, M.A.C., Van Beusichem, M.L., Houwers, A., (eds.) Optimization of Plant Nutrition: Refereed papers from the Eighth International Colloquium for the Optimization of Plant Nutrition, 31 August – 8 September 1992, Lisbon, Portugal, 1993, Springer, Netherlands, Dordrecht, 635–640.
73. Maes, W.H., Steppe, K., Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review. J. Exp. Bot. 63 (2012), 4671–4712.
74. Magney, T.S., Eitel, J.U.H., Vierling, L.A., Mapping wheat nitrogen uptake from RapidEye vegetation indices. Prec. Agric. 18 (2017), 429–451.
75. Maresma, Á., Ariza, M., Martínez, E., Lloveras, J., Martínez-Casasnovas, J., 2016. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service. Remote Sensing 8, 973.
76. Marinković B., Crnobarac, J., Brdar, S., Antić B., Jaćimović G., Crnojević V., Data mining approach for predictive modeling of agricultural yield data. 2009, Proc. First International Workshop on Sensing Technologies in Agriculture, Novi Sad, Serbia, 1–5.
77. Mehra, L.K., Cowger, C., Gross, K., Ojiambo, P.S., Predicting pre-planting risk of stagonospora nodorum blotch in winter wheat using machine learning models. Front. Plant Sci., 7, 2016, 390.
78. Morellos, A., Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R., Tziotzios, G., Wiebensohn, J., Bill, R., Mouazen, A.M., Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosyst. Eng. 152 (2016), 104–116.
79. Muñoz-Huerta, R., Guevara-Gonzalez, R., Contreras-Medina, L., Torres-Pacheco, I., Prado-Olivarez, J., Ocampo-Velazquez, R., A review of methods for sensing the nitrogen status in plants: advantages disadvantages and recent advances. Sensors, 13, 2013, 10823.
80. Nari, K., Yang-Won, L., Machine learning approaches to corn yield estimation using satellite images and climate data: a case of iowa state. J. Korean Soc. Surv., Geodesy, Photogramm. Cartogr. 34 (2016), 383–390.
81. Nawar, S., Corstanje, R., Halcro, G., Mulla, D., Mouazen, A.M., Chapter Four - Delineation of Soil Management Zones for Variable-Rate Fertilization: A Review. Sparks, D.L., (eds.) Advances in Agronomy, 2017, Academic Press, 175–245.
82. Panda, S.S., Ames, D.P., Panigrahi, S., Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens. 2 (2010), 673–696.
83. Pantazi, X.E., Moshou, D., Mouazen, A., Alexandridis, T., Kuang, B., 2015. Data Fusion of Proximal Soil Sensing and Remote Crop Sensing for the Delineation of Management Zones in Arable Crop Precision Farming. In: 7th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2015), Kavala - Greece.
84. Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L., Mouazen, A.M., Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electr. Agric. 121 (2016), 57–65.
85. Papageorgiou, E.I., Markinos, A.T., Gemtos, T.A., Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application. Appl. Soft Comput. 11 (2011), 3643–3657.
86. Papageorgiou, E.I., Aggelopoulou, K.D., Gemtos, T.A., Nanos, G.D., Yield prediction in apples using Fuzzy Cognitive Map learning approach. Comput. Electr. Agric. 91 (2013), 19–29.
87. Peña, J., Gutiérrez, P., Hervás-Martínez, C., Six, J., Plant, R., López-Granados, F., Object-based image classification of summer crops with machine learning methods. Remote Sens., 6, 2014, 5019.
88. Pohl, C., Van Genderen, J.L., Review article Multisensor image fusion in remote sensing: Concepts, methods and applications. Int. J. Remote Sens. 19 (1998), 823–854.
89. Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., Sorooshian, S., A modified soil adjusted vegetation index. Remote Sens. Environ. 48 (1994), 119–126.
90. Quemada, M., Gabriel, J., Zarco-Tejada, P., Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization. Remote Sens., 6, 2014, 2940.
91. Rasmussen, C.E., Williams, C.K.I., 2005. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press.
92. Raun, W.R., Solie, J.B., Stone, M.L., Martin, K.L., Freeman, K.W., Mullen, R.W., Zhang, H., Schepers, J.S., Johnson, G.V., Optical sensor-based algorithm for crop nitrogen fertilization. Commun. Soil Sci. Plant Anal. 36 (2005), 2759–2781.
93. Reyniers, M., Vrindts, E., Measuring wheat nitrogen status from space and ground-based platform. Int. J. Remote Sens. 27 (2006), 549–567.
94. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring Vegetation Systems in the Great plains with ERTS, NASA. Goddard Space Flight Center 3d ERTS-1 Symposium. United States, Washington, DC, pp. 309–317.
95. Ruß G., Kruse, R., Feature Selection for Wheat Yield Prediction. Bramer, M., Ellis, R., Petridis, M., (eds.) Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII, 2010, Springer, London, London, 465–478.
96. Ruß G., Kruse, R., Machine learning methods for spatial clustering on precision agriculture data. Kofod-Petersen, A., Heintz, F., Langseth, H., (eds.) Eleventh Scandinavian Conference on Artificial Intelligence, 2011, IOS Press, 40–49.
97. Ruß G., 2009. Data mining of agricultural yield data: a comparison of regression models. In: Perner, P. (Ed.), Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 24–37.
98. Safa, B., Khalili, A., Teshnehlab, M., Liaghat, A., 2004. Artificial neural networks application to predict wheat yield using climatic data. In: Proc. 20th Int. Conf. on IIPS, pp. 1–39.
99. Satir, O., Berberoglu, S., Crop yield prediction under soil salinity using satellite derived vegetation indices. Field Crops Res. 192 (2016), 134–143.
100. Scharf, P.C., Schmidt, J.P., Kitchen, N.R., Sudduth, K.A., Hong, S.Y., Lory, J.A., Davis, J.G., Remote sensing for nitrogen management. J. Soil Water Conserv. 57 (2002), 518–524.
101. Schepers, J.S., Raun, W.R., 2008. Nitrogen in Agricultural Systems. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI.
102. Schuster, E.W., Kumar, S., Sarma, S.E., Willers, J.L., Milliken, G.A., 2011. Infrastructure for data-driven agriculture: identifying management zones for cotton using statistical modeling and machine learning techniques. In: 2011 8th International Conference & Expo on Emerging Technologies for a Smarter World, p. 1.
103. Seelan, S.K., Laguette, S., Casady, G.M., Seielstad, G.A., Remote sensing applications for precision agriculture: A learning community approach. Remote Sens. Environ. 88 (2003), 157–169.
104. Shao, Y., Zhao, C., Bao, Y., He, Y., Quantification of nitrogen status in rice by least squares support vector machines and reflectance spectroscopy. Food Bioprocess Technol. 5 (2012), 100–107.
105. Sims, D.A., Gamon, J.A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81 (2002), 337–354.
106. Song, X., Yang, G., Yang, C., Wang, J., Cui, B., Spatial variability analysis of within-field winter wheat nitrogen and grain quality using canopy fluorescence sensor measurements. Remote Sens., 9, 2017, 237.
107. Stas, M., Orshoven, J.V., Dong, Q., Heremans, S., Zhang, B., 2016. A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT. In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5.
108. Subhadra, M., Debahuti, M., Gour Hari, S., 2016. Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper. Indian J. Sci. Technol. 9.
109. Sui, R., Thomasson, J.A., Hanks, J., Wooten, J., Ground-based sensing system for weed mapping in cotton. Comput. Electr. Agric. 60 (2008), 31–38.
110. Taghvaeian, S., Chávez, J., Hansen, N., Infrared thermometry to estimate crop water stress index and water use of irrigated maize in Northeastern Colorado. Remote Sens., 4, 2012, 3619.
111. Tellaeche, A., Burgos-Artizzu, X.P., Pajares, G., Ribeiro, A., A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recogn. 41 (2008), 521–530.
112. Tellaeche, A., BurgosArtizzu, X.P., Pajares, G., Ribeiro, A., 2007. A vision-based classifier in precision agriculture combining bayes and support vector machines. In: IEEE International Symposium on Intelligent Signal Processing, pp. 1–6.
113. Tian, Y.C., Yao, X., Yang, J., Cao, W.X., Hannaway, D.B., Zhu, Y., Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Res. 120 (2011), 299–310.
114. Tian, Y.C., Gu, K., Chu, X., Yao, X., Cao, W.X., Zhu, Y., Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant Soil 376 (2014), 193–209.
115. Tilling, A.K., O'Leary, G.J., Ferwerda, J.G., Jones, S.D., Fitzgerald, G.J., Rodriguez, D., Belford, R., Remote sensing of nitrogen and water stress in wheat. Field Crops Res. 104 (2006), 77–85.
116. Tremblay, N., Fallon, E., Ziadi, N., Sensing of crop nitrogen status: opportunities, tools, limitations, and supporting information requirements. HortTechnology 21 (2011), 274–281.
117. Tremblay, N., Wang, Z., Cerovic, Z.G., Sensing crop nitrogen status with fluorescence indicators. A review. Agron. Sustain. Dev. 32 (2012), 451–464.
118. Tucker, C.J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8 (1979), 127–150.
119. Ustuner, M., Sanli, F.B., Abdikan, S., Esetlili, M.T., Kurucu, Y., Crop Type Classification Using Vegetation Indices of RapidEye Imagery. 2014, ISPRS Technical Commission VII Symposium, Istanbul, Turkey.
120. Vigneau, N., Ecarnot, M., Rabatel, G., Roumet, P., Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat. Field Crops Res. 122 (2011), 25–31.
121. Viña, A., Gitelson, A.A., Nguy-Robertson, A.L., Peng, Y., Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens. Environ. 115 (2011), 3468–3478.
122. Vrindts, E., Mouazen, A.M., Reyniers, M., Maertens, K., Maleki, M.R., Ramon, H., De Baerdemaeker, J., Management zones based on correlation between soil compaction, yield and crop data. Biosyst. Eng. 92 (2005), 419–428.
123. Wahabzada, M., Mahlein, A., Bauckhage, C., Steiner, U., Oerke, E., Kersting, K., 2016. Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants. 6, 22482.
124. Wang, J., Shen, C., Liu, N., Jin, X., Fan, X., Dong, C., Xu, Y., Non-destructive evaluation of the leaf nitrogen concentration by in-field visible/near-infrared spectroscopy in pear orchards. Sensors, 17, 2017, 538.
125. Wang, Y., Witten, I., Inducing model trees for continuous classes, Proc. 9th Eur. Conf. Mach. Learn., 1997, 128–137.
126. Whelan, B.M., Taylor, J.A., 2013. Precision agriculture for grain production systems. CSIRO Publishing.
127. Wiegand, C.L., Richardson, A.J., Kanemasu, E.T., Leaf area index estimates for wheat from LANDSAT and their implications for evapotranspiration and crop modeling. Agron. J. 71 (1979), 336–342.
128. Wittenberghe, V.S., Verrelst, J., Rivera, J.P., Alonso, L., Moreno, J., Samson, R., Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset. J. Photochem. Photobiol. B: Biol. 134 (2014), 37–48.
129. Wu, Q., Jin, Y., Bao, Y., Hai, Q., Yan, R., Chen, B., Zhang, H., Zhang, B., Li, Z., Li, X., Xin, X., Comparison of two inversion methods for leaf area index using HJ-1 satellite data in a temperate meadow steppe. Int. J. Remote Sens. 36 (2015), 5192–5207.
130. Xue, J., Su, B., Significant remote sensing vegetation indices: a review of developments and applications. J. Sens., 2017, 2017, 17.
131. Yang, J., Gong, W., Shi, S., Du, L., Sun, J., Song, S., Laser-induced fluorescence characteristics of vegetation by a new excitation wavelength. Spectrosc. Lett. 49 (2016), 263–267.
132. Yao, X., Zhu, Y., Tian, Y.C., Feng, W., Cao, W., Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. Int. J. Appl. Earth Observ. Geoinform. 12 (2010), 89–100.
133. Yao, X., Huang, Y., Shang, G., Zhou, C., Cheng, T., Tian, Y.C., Cao, W., Zhu, Y., Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sens., 7, 2015, 14939.
134. You, J., Li, X., Low, M., Lobell, D., Ermon, S., 2017. Deep gaussian process for crop yield prediction based on remote sensing data. Association for the Advancement of Artificial Intelligence.
135. Yu, K., Li, F., Gnyp, M.L., Miao, Y., Bareth, G., Chen, X., Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain. ISPRS J. Photogramm. Remote Sens. 78 (2013), 102–115.
136. Yuan, Y., Zhu, G., Wang, Q., Hyperspectral band selection by multitask sparsity pursuit. IEEE Trans. Geosci. Remote Sens. 53 (2015), 631–644.
137. Zarco-Tejada, P.J., González-Dugo, V., Berni, J.A.J., Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 117 (2012), 322–337.
138. Zhang, L., Zhang, J., Kyei-Boahen, S., Zhang, M., Simulation and prediction of soybean growth and development under field conditions. Am.-Eurasian J. Agric. Environ. Sci. 7 (2010), 374–385.
139. Zhao, G., Miao, Y., Wang, H., Su, M., Fan, M., Zhang, F., Jiang, R., Zhang, Z., Liu, C., Liu, P., Ma, D., A preliminary precision rice management system for increasing both grain yield and nitrogen use efficiency. Field Crops Res. 154 (2013), 23–30.
140. Zhou, Y., Song, G., Wang, M., Wireless sensor network data fusion algorithm based on neural network in the area of agriculture. Sens. Transducers J. 16 (2012), 128–136.
141. Zhu, Y., Li, Y., Zhou, D., Tian, Y.C., Yao, X., Cao, W., Quantitative relationship between leaf nitrogen concentration and canopy reflectance spectra in rice and wheat. Acta Ecol. Sin. 26 (2006), 3463–3469.