Assessment of data fusion oriented on data mining approaches to enhance precision agriculture practices aimed at increase of Durum Wheat (Triticum turgidum L. var. durum) yield
DOI:
https://doi.org/10.21839/jfna.2018.v1i1.229Keywords:
Data science, Crop Cultivation, Predictive ModelsAbstract
In 2050, world population will reach a total of 9 billion inhabitants and their food demand have to be satisfied. Durum wheat (Triticum turgidum L. var. durum) is one of the most important food crop and its consumption is increasing worldwide. Productivity growth in agriculture and profitable returns are strongly influenced by investment in research and development, where Precision Agriculture (PA) represents an innovative way to manage farms by introducing the Information and Communication Technology (ICT) into the production process. It is known that farms activities produce large amounts of data. Today ICT allows, with electronic and software systems, to collect and transfer automatically these data, thus increasing yields and profits. In this direction significant data are processed from agricultural production, and retrieved to extract useful information important to increase the knowledge base. Data from multiple data sources can be processed by a Data Fusion (DF) approach able to combine multiple data sources into an unique database system. Raw data are transformed into useful information, thus DF improves pattern recognition, analysis of growth factors, and relationship between crops and environments. Data Fusion is synonym of Data Integration, Sensor Fusion, and Image Fusion. By means of Data Mining (DM) it is possible to extract useful information from data of the production processes thus providing new outputs concerning product quality and product “health status”. The following literature take into account the DF and DM techniques applied to Precision Agriculture (PA) and to cultivation inputs (water, nitrogen, etc.) management. We report also last advances of DF and DM in modern agriculture and in precision durum wheat production.
Downloads
Download data is not yet available.
References
Adamchuk, V., Hummel, J., Morgan, M., and Upadhyaya, S. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44, 71–91.
Alpaydin, E. (2014). Introduction to Machine Learning, third edition. MIT Press, 640.
Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L., and Royo, C. (2000). Spectral Vegetation Indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92, 83.
Basso, B., Fiorentino, C., Cammarano, D., and Schulthess, U. (2016). Variable rate nitrogen fertilizer response in wheat using remote sensing. Precision Agriculture, 17, 168–82.
De Benedetto, D., Castrignano, A., Diacono, M., Rinaldi, M., Ruggieri, S., and Tamborrino, R. (2013). Field partition by proximal and remote sensing data fusion. Biosystems Engineering, 114(4), 372-383.
Bobryk, C.W., Yost, M.A., and Kitchen, N.R. (2018). Field variability and vulnerability index to identify regional precision agriculture opportunity. Precision Agriculture, 19, 589–605.
Bruce, L.M., and Reynolds, D. (2016). Game theory based data fusion for precision agriculture applications. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 3563-3566.
Buttafuoco, G., Castrignanò, A., Cucci, G., Lacolla, G., and Lucà, F. (2017). Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field. Precision Agriculture,18, 37–58.
Castrignanò, A., Landrum, C., and De Benedetto, D. (2015). Delineation of management zones in precision agriculture by integration of proximal sensing with multivariate geostatistics. Examples of sensor data fusion. Agriculturae Conspectus Scientificus, 80, 1, 39–45.
Castrignanò, A., Buttafuoco, G., Quarto, R., Vitti, C., Langella, G., Terribile, F., and Venezia, A. (2017). A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors, 17, 12, 2794.
Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R.A., Terribile, F., Langella, G., and Venezia, A. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture. Catena, 167, 293–304.
Cavallo, G., De Benedetto, D., Castrignanò, A., Quarto, R., Vonella, A.V., and Buttafuoco, G. (2016). Use of geophysical data for assessing 3D soil variation in a durum wheat field and their association with crop yield. Biosystems Engineering, 152, 28–40.
Chaudhari, K.., Tripathy, R., and Patel, N. (2010). Spatial wheat yield prediction using crop simulation model, GIS, remote sensing and ground observed data. Journal of Agrometeorology, 12,174–80.
Cho, Y., Sudduth, K.A., and Chung, S.O. (2016). Soil physical property estimation from soil strength and apparent electrical conductivity sensor data. Biosystems Engineering, 152, 68–78.
Chouhan, S. (2016). A survey and analysis of various agricultural crops classification techniques. International Journal of Computer Applications, 136, 11, 25–30.
Conijn, J.G., Bindraban, P.S., Schröder, J.J., and Jongschaap, R.E.E. (2018). Can our global food system meet food demand within planetary boundaries?Agriculture, Ecosystems and Environment, 251, 244–256.
Cossani, C.M., and Sadras, V.O. (2018). Water–Nitrogen Colimitation in Grain Crops, 1st Edn. Elsevier Inc., 150, 231-274.
D’Amico, A., Di Natale, C., and Sarro, P. M. (2015). Ingredients for sensors science. Sensors and Actuators B: Chemical, 207, 1060-1068.
Davatgar, N., Neishabouri, M.R., and Sepaskhah, A.R. (2012). Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma,111(8), 173–174.
Elias, E.M. (1995). Durum wheat products. Durum Wheat Improvement in the Mediterranean Region: New Challenges, Serie A: Séminaires Méditerranéennes, 40, 23-31.
FAO. (2009). How to Feed the World in 2050. Insights From An Expert Meet. FAO 2050, 1–35.
Fraisse, C.W., Sudduth, K.A., and Kitchen, N.R. (2001). Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Transactions of the ASAE, 44, 1, 155–166.
George, T., Bagazonzya, H., Ballantyne, P., Belden, C., Birner, R.,Del Castello, R., del; Castren, T., Choudhary, V., Dixie, G., Donovan, K., Edge, P., Hani, M., Harrod, J., Pekka, J., Jantunen, T., Jayaraman, N., Maru, A., Majumdar, S., Manfre, C., McLaren, R., McNamara, K., Morras, E., Nichterlein, K., Pehu, E., Pillai, M., Porcari, R., Luz D., Rudgard, S., Safdar, Z., Sen, S., Slavova, M., Srivastava, L., Stanley, V., and Treinen, S. (2011). ICT in Agriculture: Connecting smallholders to knowledge, networks, and institutions. World Bank, 64605, 1.
Georgi, C., Spengler, D., Itzerott, S., and Kleinschmit, B. (2017). Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data. Precision Agriculture, 19(4), 684-707.
Gevaert, C.M., Suomalainen, J., Tang, J., and Kooistra, L. (2015). Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 3140-3146.
Ghassemian, H. (2016). A review of remote sensing image fusion methods. Information Fusion, 32, 75-89.
Grafton, R.Q., Daugbjerg, C., and Qureshi, M.E. (2015). Towards food security by 2050. Food Security, 7(2), 179-183.
Guastaferro, F., Castrignanò, A., De Benedetto, D., Sollitto, D., Troccoli, A., Cafarelli, B. (2010). A comparison of different algorithms for the delineation of management zones. Precision Agriculture, 11(6), 600-620.
Guo, J., Li, X., Li, Z., Hu, L., Yang, G., Zhao, C., Fairbairn, D., Watson, D., and Ge, M. (2018). Multi-GNSS precise point positioning for precision agriculture. Precision Agriculture,1–17.
Gustafsson, F. (2010). Statistical sensor fusion. Studentlitteratur, 543.
Hunter, M.C., Smith, R.G., Schipanski, M.E., Atwood, L.W., and Mortensen, D.A. (2017). Agriculture in 2050: Recalibrating targets for sustainable intensification. Bioscience, 67, 4, 386–391.
Inurreta-Aguirre, H.D., Lauri, P.É., Dupraz, C., and Gosme, M. (2018). Yield components and phenology of durum wheat in a Mediterranean alley-cropping system. Agroforestry Systems, 1-14.
Jambekar, S., and Saquib, Z. (2018). Application of Data Mining Techniques for Prediction of Crop Production in India. Perception (MLP), 7, 66–69.
Ji, W., Adamchuk, V., Chen, S., Biswas, A., Leclerc, M., and Rossel, R.V. (2017). The use of proximal soil sensor data fusion and digital soil mapping for precision agriculture. Pedometrics, 298.
Jia, Y., Su, Z., Shen, W., Yuan, J., and Xu, Z. (2016). UAV remote sensing image mosaic and its application in agriculture. Image, 10(5), 159–70.
Jiménez, D., Dorado, H., Cock, J., Prager, S.D., Delerce, S., Grillon, A., Bejarano, M.A., Benavides, H., and Jarvis, A. (2016). From observation to information: Data-driven understanding of on farm yield variation. PLoS One, 11, 3, 1–20.
Kaur, K., and Singh, M. (2014). Knowledge discovery and data mining to identify agricultural patterns. International Journal of Engineering Sciences and Research Technology. 3(3), 1337-1345.
Khosla, R., Fleming, K., Delgado, J.A., Shaver, T.M., and Westfall, D.G. (2002). Use of site-specific management zones to improve nitrogen management for precision agriculture. Journal of Soil and Water Conservation, 57(6), 513-518.
Klein, L.A. (2004). Sensor and Data Fusion: A Tool for Information Assessment and Decision Making. Bellingham, SPIE PRESS BOOK.
Knauer, K., Gessner, U., Fensholt, R., and Kuenzer, C. (2016). An ESTARFM fusion framework for the generation of large-scale time series in cloud-prone and heterogeneous landscapes. Remote Sensing, 8(5), 1-21.
Koch, B., Khosla, R., Frasier, W.M., Westfall, D.G., and Inman, D. (2004). Economic feasibility of variable-rate nitrogen application utilizing site-specific management zones. Agronomy Journal, 96(6), 1572-1580.
Kodeeshwari, R.S., and Ilakkiya, K.T. (2017). Different types of data mining techniques used in agriculture-Asurvey. International Journal of Advanced Engineering Research and Science, 4(6), 17-23.
Kumar, S.A., and Ilango, P. (2018). The impact of wireless sensor network in the field of precision agriculture: A review. Wireless Personal Communications, 98(1), 685-698.
Kussul, N., Shelestov, A., Basarab, R., Skakun, S., Kussul, O., and Lavreniuk, M. (2015). Geospatial intelligence and data fusion techniques for sustainable development problems. Conference: ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer At: Lviv Polytechnic National University, Lviv, Ukraine, 1356, 196-203.
Lamb, D.W., and Brown, R.B. (2001). PA—precision agriculture: Remote-sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research, 78(2), 117-125.
Lamba, V., and Dhaka, V.S. (2014). Wheat yield prediction using artificial neural network and crop prediction techniques. International Journal for Research in Applied Science and Engineering Technology, 2(4), 330-341.
Li, Y., Shi, Z., Li, F., and Li, H-Y. (2007). Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Computers and Electronics in Agriculture, 56(2), 174-186.
Luschei, C. (2001). An assessment of the use of site-specific weed control for improving prediction-based management decisions and automating on-farm research. Doctoral dissertation, Montana State University-Bozeman, College of Agriculture.
Mahmood, H.S., Hoogmoed, W.B., and van Henten, E.J. (2012). Sensor data fusion to predict multiple soil properties. Precision Agriculture, 13(6), 628-645.
Mahmood, H.S. (2013). Proximal soil sensors and data fusion for precision agriculture. PhD thesis, Wageningen University, Wageningen, Netherlands.
Malviya, S., Mittal, D., and Birle, A. (2015). Agriculture multi sensor data fusion and analysis system. Measurements, 4(5), 1334–1337.
Manjula, A., and Narsimha, G. (2016). Crop Yield prediction with aid of optimal neural network in spatial data mining: New approaches. International Journal of Information and Computation Technology, (6)1 25-33.
Milovic, B., and Radojevic, V. (2015). Application of data mining in agriculture. Bulgarian Journal of Agricultural Science, 21(1), 26-34.
Mistry, A., Shah, V., and Vidyanagar, V. (2016). Brief survey of data mining techniques applied to applications of agriculture. International Journal of Advanced Research in Computer and Communication Engineering, 5(2), 301-304.
Mohd Kassim, M.R., Mat, I., and Harun, A.N. (2014). Wireless sensor network in precision agriculture application. Computer, Information and Telecommunication Systems (CITS), International Conference on IEEE, 1-5.
Moral, F.J., Terrón, J.M., and Da Silva, J.R.M. (2010). Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil and Tillage Research,106(2), 335-343.
Morari, F., Loddo,S., Berzaghi, P., Ferlito, J., Berti, A., Sartori, L., Visioli, G., Marmiroli, N., Piragnolo, D., and Mosca, G. (2013). Understanding the effects of site-specific fertilization on yield and protein content in durum wheat. Precision Agriculture, 13, 321-327.
Morari, F., Zanella, V., Sartori, L., Visioli, G., Berzaghi, P., and Mosca, G. (2018). Optimising durum wheat cultivation in North Italy: understanding the effects of site-specific fertilization on yield and protein content. Precision Agriculture, 19(2), 257-277.
Mouazen, A.M., Shi, Z., and Van Meirvenne, M. (2016). Sensing soil condition and functions. Biosystems Engineering, 152, 1–2.
Mucherino, A., Papajorgji, P.J., and Pardalos, P.M. (2009). Data Mining in Agriculture. Springer Science & Business Media, Switzerland.
Mulla, D.J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371.
Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F., and MacDonald, L. (2005). Spatial variability of measured soil properties across site-specific management zones. Soil Science Society of America Journal, 69(5), 1572-1579.
Nabrzyski, J., Liu, C., Vardeman, C., Gesing, S., and Budhatoki, M. (2014). Agriculture data for all - Integrated tools for agriculture data integration, analytics, and sharing. InBig Data (Big Data Congress), IEEE International Congress,774-775.
Nawar, S., Corstanje, R., Halcro, G., Mulla, D., and Mouazen, A.M. (2017). Delineation of soil management zones for variable-rate fertilization: A review. Advances in Agronomy, 143, 175-245.
Novelli, A., Tarantino, E., Fratino, U., Iacobellis, V., Romano, G., and Gentile, F. (2016). A data fusion algorithm based on the Kalman filter to estimate leaf area index evolution in durum wheat by using field measurements and MODIS surface reflectance data. Remote Sensing Letters, 7(5), 476-484.
Oliver, M.A., Bishop, T.F.A., and Marchant, B.P. (2013). Precision Agriculture for Sustainability and Environmental Protection. Routledge, UK.
Pantazi, X.E., Moshou, D., Mouazen, A.M., Alexandridis, T., and Kuang, B. (2015). Data fusion of proximal soil sensing and remote crop sensing for the delineation of management zones in arable crop precision farming. Proceedings of the 7th International Conference on Information and Communication Technologies in Agriculture, Food and Environment, Kavala, Greece, Pp. 765-776.
Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L., and Mouazen, A.M. (2016). Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture, 121, 57-65.
Patel, H., and Patel, D. (2014). A brief survey of data mining techniques applied to agricultural data. International Journal of Computer Applications, 95, 9.
Pedersen, S.M., and Lind, K.M. (2017). Precision Agriculture – From Mapping to Site-Specific Application. In: Pedersen S., Lind K. (Eds) Precision Agriculture: Technology and Economic Perspectives. Progress in Precision Agriculture. Springer, Cham, Switzerland.
Pedroso, M., Taylor, J., Tisseyre, B., Charnomordic, B., and Guillaume, S. (2010). A segmentation algorithm for the delineation of agricultural management zones. Computers and Electronics in Agriculture, 70(1), 199-208.
Pohl, C., and van Genderen, J. (2014). Remote sensing image fusion: an update in the context of Digital Earth. International Journal of Digital Earth, 7(2), 158-172.
Pohl, C., and van Genderen, J. (2015). Structuring contemporary remote sensing image fusion. International Journal of Image and Data Fusion, 6(1), 3-21.
Proffitt, A. P. B., Bramley, R., Lamb, D., and Winter, E. (2006). Precision viticulture: a new era in vineyard management and wine production. Ashford, Winetitles, 90.
Ramesh, D., and Vardhan, B.V. (2013). Data mining techniques and applications to agricultural yield data. International journal of Advanced Research in Computer and Communication Engineering, 2(9), 3477-3480.
Riezzo, E. E., Zippitelli, M., Impedovo, D., Todorovic, M., Cantore, V., and Buono, V. (2013). Hydro?Tech: an integrated decision support system for sustainable irrigation management (II): software and hardware architecture. CIGR Proceedings, 1(1) 443-486.
Rodrigues, F.A., Bramley, R.G.V., and Gobbett, D.L. (2015). Proximal soil sensing for precision agriculture: Simultaneous use of electromagnetic induction and gamma radiometrics in contrasting soils. Geoderma 243, 183–195.
Rodrigues, A.S., Marçal, A.R.S., and Cunha, M. (2009). Evaluation of data fusion methods for agricultural monitoring based on synthetic images. Remote Sensing for a Changing Europe: Proceedings of the 28th Symposium of the European Association of Remote Sensing Laboratories, 125-133.
Rosero-Vlasova, O.A., Pérez-Cabello, F., Montorio Llovería, R., and Vlassova, L. (2016). Assessment of laboratory VIS-NIR-SWIR setups with different spectroscopy accessories for characterisation of soils from wildfire burns. Biosystems Engineering, 152, 51-67.
Salampasis, M., and Theodoridis, A. (2013). Information and communication technology in agricultural development. Procedia Technology, 8, 1-3.
Schemberger, E.E., Fontana, F.S., Johann, J.A., and De Souza, E.G. (2017). Data mining for the assessment of management areas in precision agriculture. Engenharia Agrícola, 37(1), 185-193.
Schenatto, K., De Souza, E.G, Bazzi, C.L., Gavioli, A., Betzek, N.M., and Beneduzzi, H.M. (2017). Normalization of data for delineating management zones. Computers and Electronics in Agriculture, 143, 238-248.
Servadio, P., Bergonzoli, S., and Verotti, M. (2017). Delineation of management zones based on soil mechanical-chemical properties to apply variable rates of inputs throughout a field (VRA). Engineering in Agriculture, Environment and Food, 10(1), 20-30.
Shaddad, S.M., Madrau, S., Castrignanò, A., and Mouazen, A.M. (2016). Data fusion techniques for delineation of site-specific management zones in a field in UK. Precision agriculture, 17(2), 200-217.
Shannon, D.K., Clay, D.E., Sudduth, K.A., Shannon, D.K., Clay, D.E., and Kitchen, N.R. (2018). An introduction to precision agriculture. In: D. Kent Shannon, David E. Clay, and Newell R. Kitchen (Ed.), Precision Agriculture Basics. (Pp. 1–12.), American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.
Sissons, M., Abecassis, J., Marchylo, B., and Carcea, M. (2016). Durum Wheat: Chemistry and Technology (2nd Ed). AACC International, Elsevier Inc.
Stafford, J.V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76(3), 267-275.
Stewart, C.M., McBratney, A.B., and Skerritt, J.H. (2002). Site-specific durum wheat quality and its relationship to soil properties in a single field in northern New South Wales. Precision Agriculture, 3(2), 155-168.
Tagarakis, A., Liakos, V., Fountas, S., Koundouras, S., and Gemtos, T.A. (2013). Management zones delineation using fuzzy clustering techniques in grapevines. Precision Agriculture, 14(1), 18-39.
Tedone, L., Ali, S.A., and De Mastro, G. (2018). Optimization of nitrogen in durum wheat in the Mediterranean climate: the agronomical aspect and greenhouse gas (GHG) emissions.In Nitrogen in Agriculture-Updates. IntechOpen, DOI: 10.5772/intechopen.70195
Thorp, K.R., Wang, G., Bronson, K.F., Badaruddin, M., and Mon, J. (2017). Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield. Computers and Electronics in Agriculture, 136, 1-12.
Tilman, D., Balzer, C., Hill, J., and Befort, B.L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260-20264.
Todorovic, M., Cantore, V., Riezzo, E. E., Zippitelli, M., Galiano, A., and Buono, V. (2013). Hydrotech: an integrated decision support system for sustainable irrigation management (I): main algorithms and field testing. CIGR Proceedings, 1(1), 425-41.
Troccoli, A., Borrelli, G.M., De Vita, P., Fares, C., and Di Fonzo, N. (2000). Durum wheat quality: A multidisciplinary concept. Journal of Cereal Science, 32, 99–113.
Verma, A., Jatain, A., and Bajaj, S. (2018). Crop Yield Prediction of Wheat Using Fuzzy C Means Clustering and Neural Network. International Journal of Applied Engineering Research, 13(11), 9816-9821.
Veum, K.S., Sudduth, K.A., Kremer, R.J., and Kitchen, N.R. (2017). Sensor data fusion for soil health assessment. Geoderma, 305, 53-61.
Vidanapathirana, N.P. (2012). Agricultural information systems and their applications for development of agriculture and rural community, a review study. The 35th Information Systems Research Seminar in Scandinavia–IRIS, 1, 1-14.
Wagner, N.C. (2004). Wheat yield prediction modeling for localized optimization of fertilizer and herbicide application. Doctoral dissertation, Montana State University-Bozeman, College of Agriculture, 251.
Whelan, B., and Taylor, J. (2013). Precision agriculture for grain production systems. CSIRO Publishing, Clayton, Australia.
Xie, W., Xue, Y., Zhai, L., and Sang, H. (2013). Data fusion technology of multi-platform earth observation on agriculture. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1(1), 189-192.
Zhang, Q. (2017). Automation in Tree Fruit Production: Principles and Practice. (Pp. 312 ). CABI, UK.
Zhang, L., Guo, C.L., Zhao, L.Y., Zhu, Y., Cao, W.X., Tian, Y.C., Cheng, T., and Wang, X. (2016). Estimating wheat yield by integrating the WheatGrow and PROSAIL models. Field Crops Research, 192, 55-66.
Zheng, Y., Zhang, M., Zhang, X., Zeng, H., and Wu, B. (2016). Mapping winter wheat biomass and yield using time series data blended from -PROBA-V 100 and 300-m S1 products. Remote Sensing, 8(10), 824.
Alpaydin, E. (2014). Introduction to Machine Learning, third edition. MIT Press, 640.
Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L., and Royo, C. (2000). Spectral Vegetation Indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92, 83.
Basso, B., Fiorentino, C., Cammarano, D., and Schulthess, U. (2016). Variable rate nitrogen fertilizer response in wheat using remote sensing. Precision Agriculture, 17, 168–82.
De Benedetto, D., Castrignano, A., Diacono, M., Rinaldi, M., Ruggieri, S., and Tamborrino, R. (2013). Field partition by proximal and remote sensing data fusion. Biosystems Engineering, 114(4), 372-383.
Bobryk, C.W., Yost, M.A., and Kitchen, N.R. (2018). Field variability and vulnerability index to identify regional precision agriculture opportunity. Precision Agriculture, 19, 589–605.
Bruce, L.M., and Reynolds, D. (2016). Game theory based data fusion for precision agriculture applications. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 3563-3566.
Buttafuoco, G., Castrignanò, A., Cucci, G., Lacolla, G., and Lucà, F. (2017). Geostatistical modelling of within-field soil and yield variability for management zones delineation: a case study in a durum wheat field. Precision Agriculture,18, 37–58.
Castrignanò, A., Landrum, C., and De Benedetto, D. (2015). Delineation of management zones in precision agriculture by integration of proximal sensing with multivariate geostatistics. Examples of sensor data fusion. Agriculturae Conspectus Scientificus, 80, 1, 39–45.
Castrignanò, A., Buttafuoco, G., Quarto, R., Vitti, C., Langella, G., Terribile, F., and Venezia, A. (2017). A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors, 17, 12, 2794.
Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R.A., Terribile, F., Langella, G., and Venezia, A. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in Precision Agriculture. Catena, 167, 293–304.
Cavallo, G., De Benedetto, D., Castrignanò, A., Quarto, R., Vonella, A.V., and Buttafuoco, G. (2016). Use of geophysical data for assessing 3D soil variation in a durum wheat field and their association with crop yield. Biosystems Engineering, 152, 28–40.
Chaudhari, K.., Tripathy, R., and Patel, N. (2010). Spatial wheat yield prediction using crop simulation model, GIS, remote sensing and ground observed data. Journal of Agrometeorology, 12,174–80.
Cho, Y., Sudduth, K.A., and Chung, S.O. (2016). Soil physical property estimation from soil strength and apparent electrical conductivity sensor data. Biosystems Engineering, 152, 68–78.
Chouhan, S. (2016). A survey and analysis of various agricultural crops classification techniques. International Journal of Computer Applications, 136, 11, 25–30.
Conijn, J.G., Bindraban, P.S., Schröder, J.J., and Jongschaap, R.E.E. (2018). Can our global food system meet food demand within planetary boundaries?Agriculture, Ecosystems and Environment, 251, 244–256.
Cossani, C.M., and Sadras, V.O. (2018). Water–Nitrogen Colimitation in Grain Crops, 1st Edn. Elsevier Inc., 150, 231-274.
D’Amico, A., Di Natale, C., and Sarro, P. M. (2015). Ingredients for sensors science. Sensors and Actuators B: Chemical, 207, 1060-1068.
Davatgar, N., Neishabouri, M.R., and Sepaskhah, A.R. (2012). Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma,111(8), 173–174.
Elias, E.M. (1995). Durum wheat products. Durum Wheat Improvement in the Mediterranean Region: New Challenges, Serie A: Séminaires Méditerranéennes, 40, 23-31.
FAO. (2009). How to Feed the World in 2050. Insights From An Expert Meet. FAO 2050, 1–35.
Fraisse, C.W., Sudduth, K.A., and Kitchen, N.R. (2001). Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Transactions of the ASAE, 44, 1, 155–166.
George, T., Bagazonzya, H., Ballantyne, P., Belden, C., Birner, R.,Del Castello, R., del; Castren, T., Choudhary, V., Dixie, G., Donovan, K., Edge, P., Hani, M., Harrod, J., Pekka, J., Jantunen, T., Jayaraman, N., Maru, A., Majumdar, S., Manfre, C., McLaren, R., McNamara, K., Morras, E., Nichterlein, K., Pehu, E., Pillai, M., Porcari, R., Luz D., Rudgard, S., Safdar, Z., Sen, S., Slavova, M., Srivastava, L., Stanley, V., and Treinen, S. (2011). ICT in Agriculture: Connecting smallholders to knowledge, networks, and institutions. World Bank, 64605, 1.
Georgi, C., Spengler, D., Itzerott, S., and Kleinschmit, B. (2017). Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data. Precision Agriculture, 19(4), 684-707.
Gevaert, C.M., Suomalainen, J., Tang, J., and Kooistra, L. (2015). Generation of spectral–temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 3140-3146.
Ghassemian, H. (2016). A review of remote sensing image fusion methods. Information Fusion, 32, 75-89.
Grafton, R.Q., Daugbjerg, C., and Qureshi, M.E. (2015). Towards food security by 2050. Food Security, 7(2), 179-183.
Guastaferro, F., Castrignanò, A., De Benedetto, D., Sollitto, D., Troccoli, A., Cafarelli, B. (2010). A comparison of different algorithms for the delineation of management zones. Precision Agriculture, 11(6), 600-620.
Guo, J., Li, X., Li, Z., Hu, L., Yang, G., Zhao, C., Fairbairn, D., Watson, D., and Ge, M. (2018). Multi-GNSS precise point positioning for precision agriculture. Precision Agriculture,1–17.
Gustafsson, F. (2010). Statistical sensor fusion. Studentlitteratur, 543.
Hunter, M.C., Smith, R.G., Schipanski, M.E., Atwood, L.W., and Mortensen, D.A. (2017). Agriculture in 2050: Recalibrating targets for sustainable intensification. Bioscience, 67, 4, 386–391.
Inurreta-Aguirre, H.D., Lauri, P.É., Dupraz, C., and Gosme, M. (2018). Yield components and phenology of durum wheat in a Mediterranean alley-cropping system. Agroforestry Systems, 1-14.
Jambekar, S., and Saquib, Z. (2018). Application of Data Mining Techniques for Prediction of Crop Production in India. Perception (MLP), 7, 66–69.
Ji, W., Adamchuk, V., Chen, S., Biswas, A., Leclerc, M., and Rossel, R.V. (2017). The use of proximal soil sensor data fusion and digital soil mapping for precision agriculture. Pedometrics, 298.
Jia, Y., Su, Z., Shen, W., Yuan, J., and Xu, Z. (2016). UAV remote sensing image mosaic and its application in agriculture. Image, 10(5), 159–70.
Jiménez, D., Dorado, H., Cock, J., Prager, S.D., Delerce, S., Grillon, A., Bejarano, M.A., Benavides, H., and Jarvis, A. (2016). From observation to information: Data-driven understanding of on farm yield variation. PLoS One, 11, 3, 1–20.
Kaur, K., and Singh, M. (2014). Knowledge discovery and data mining to identify agricultural patterns. International Journal of Engineering Sciences and Research Technology. 3(3), 1337-1345.
Khosla, R., Fleming, K., Delgado, J.A., Shaver, T.M., and Westfall, D.G. (2002). Use of site-specific management zones to improve nitrogen management for precision agriculture. Journal of Soil and Water Conservation, 57(6), 513-518.
Klein, L.A. (2004). Sensor and Data Fusion: A Tool for Information Assessment and Decision Making. Bellingham, SPIE PRESS BOOK.
Knauer, K., Gessner, U., Fensholt, R., and Kuenzer, C. (2016). An ESTARFM fusion framework for the generation of large-scale time series in cloud-prone and heterogeneous landscapes. Remote Sensing, 8(5), 1-21.
Koch, B., Khosla, R., Frasier, W.M., Westfall, D.G., and Inman, D. (2004). Economic feasibility of variable-rate nitrogen application utilizing site-specific management zones. Agronomy Journal, 96(6), 1572-1580.
Kodeeshwari, R.S., and Ilakkiya, K.T. (2017). Different types of data mining techniques used in agriculture-Asurvey. International Journal of Advanced Engineering Research and Science, 4(6), 17-23.
Kumar, S.A., and Ilango, P. (2018). The impact of wireless sensor network in the field of precision agriculture: A review. Wireless Personal Communications, 98(1), 685-698.
Kussul, N., Shelestov, A., Basarab, R., Skakun, S., Kussul, O., and Lavreniuk, M. (2015). Geospatial intelligence and data fusion techniques for sustainable development problems. Conference: ICT in Education, Research and Industrial Applications: Integration, Harmonization and Knowledge Transfer At: Lviv Polytechnic National University, Lviv, Ukraine, 1356, 196-203.
Lamb, D.W., and Brown, R.B. (2001). PA—precision agriculture: Remote-sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research, 78(2), 117-125.
Lamba, V., and Dhaka, V.S. (2014). Wheat yield prediction using artificial neural network and crop prediction techniques. International Journal for Research in Applied Science and Engineering Technology, 2(4), 330-341.
Li, Y., Shi, Z., Li, F., and Li, H-Y. (2007). Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Computers and Electronics in Agriculture, 56(2), 174-186.
Luschei, C. (2001). An assessment of the use of site-specific weed control for improving prediction-based management decisions and automating on-farm research. Doctoral dissertation, Montana State University-Bozeman, College of Agriculture.
Mahmood, H.S., Hoogmoed, W.B., and van Henten, E.J. (2012). Sensor data fusion to predict multiple soil properties. Precision Agriculture, 13(6), 628-645.
Mahmood, H.S. (2013). Proximal soil sensors and data fusion for precision agriculture. PhD thesis, Wageningen University, Wageningen, Netherlands.
Malviya, S., Mittal, D., and Birle, A. (2015). Agriculture multi sensor data fusion and analysis system. Measurements, 4(5), 1334–1337.
Manjula, A., and Narsimha, G. (2016). Crop Yield prediction with aid of optimal neural network in spatial data mining: New approaches. International Journal of Information and Computation Technology, (6)1 25-33.
Milovic, B., and Radojevic, V. (2015). Application of data mining in agriculture. Bulgarian Journal of Agricultural Science, 21(1), 26-34.
Mistry, A., Shah, V., and Vidyanagar, V. (2016). Brief survey of data mining techniques applied to applications of agriculture. International Journal of Advanced Research in Computer and Communication Engineering, 5(2), 301-304.
Mohd Kassim, M.R., Mat, I., and Harun, A.N. (2014). Wireless sensor network in precision agriculture application. Computer, Information and Telecommunication Systems (CITS), International Conference on IEEE, 1-5.
Moral, F.J., Terrón, J.M., and Da Silva, J.R.M. (2010). Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil and Tillage Research,106(2), 335-343.
Morari, F., Loddo,S., Berzaghi, P., Ferlito, J., Berti, A., Sartori, L., Visioli, G., Marmiroli, N., Piragnolo, D., and Mosca, G. (2013). Understanding the effects of site-specific fertilization on yield and protein content in durum wheat. Precision Agriculture, 13, 321-327.
Morari, F., Zanella, V., Sartori, L., Visioli, G., Berzaghi, P., and Mosca, G. (2018). Optimising durum wheat cultivation in North Italy: understanding the effects of site-specific fertilization on yield and protein content. Precision Agriculture, 19(2), 257-277.
Mouazen, A.M., Shi, Z., and Van Meirvenne, M. (2016). Sensing soil condition and functions. Biosystems Engineering, 152, 1–2.
Mucherino, A., Papajorgji, P.J., and Pardalos, P.M. (2009). Data Mining in Agriculture. Springer Science & Business Media, Switzerland.
Mulla, D.J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371.
Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F., and MacDonald, L. (2005). Spatial variability of measured soil properties across site-specific management zones. Soil Science Society of America Journal, 69(5), 1572-1579.
Nabrzyski, J., Liu, C., Vardeman, C., Gesing, S., and Budhatoki, M. (2014). Agriculture data for all - Integrated tools for agriculture data integration, analytics, and sharing. InBig Data (Big Data Congress), IEEE International Congress,774-775.
Nawar, S., Corstanje, R., Halcro, G., Mulla, D., and Mouazen, A.M. (2017). Delineation of soil management zones for variable-rate fertilization: A review. Advances in Agronomy, 143, 175-245.
Novelli, A., Tarantino, E., Fratino, U., Iacobellis, V., Romano, G., and Gentile, F. (2016). A data fusion algorithm based on the Kalman filter to estimate leaf area index evolution in durum wheat by using field measurements and MODIS surface reflectance data. Remote Sensing Letters, 7(5), 476-484.
Oliver, M.A., Bishop, T.F.A., and Marchant, B.P. (2013). Precision Agriculture for Sustainability and Environmental Protection. Routledge, UK.
Pantazi, X.E., Moshou, D., Mouazen, A.M., Alexandridis, T., and Kuang, B. (2015). Data fusion of proximal soil sensing and remote crop sensing for the delineation of management zones in arable crop precision farming. Proceedings of the 7th International Conference on Information and Communication Technologies in Agriculture, Food and Environment, Kavala, Greece, Pp. 765-776.
Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L., and Mouazen, A.M. (2016). Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture, 121, 57-65.
Patel, H., and Patel, D. (2014). A brief survey of data mining techniques applied to agricultural data. International Journal of Computer Applications, 95, 9.
Pedersen, S.M., and Lind, K.M. (2017). Precision Agriculture – From Mapping to Site-Specific Application. In: Pedersen S., Lind K. (Eds) Precision Agriculture: Technology and Economic Perspectives. Progress in Precision Agriculture. Springer, Cham, Switzerland.
Pedroso, M., Taylor, J., Tisseyre, B., Charnomordic, B., and Guillaume, S. (2010). A segmentation algorithm for the delineation of agricultural management zones. Computers and Electronics in Agriculture, 70(1), 199-208.
Pohl, C., and van Genderen, J. (2014). Remote sensing image fusion: an update in the context of Digital Earth. International Journal of Digital Earth, 7(2), 158-172.
Pohl, C., and van Genderen, J. (2015). Structuring contemporary remote sensing image fusion. International Journal of Image and Data Fusion, 6(1), 3-21.
Proffitt, A. P. B., Bramley, R., Lamb, D., and Winter, E. (2006). Precision viticulture: a new era in vineyard management and wine production. Ashford, Winetitles, 90.
Ramesh, D., and Vardhan, B.V. (2013). Data mining techniques and applications to agricultural yield data. International journal of Advanced Research in Computer and Communication Engineering, 2(9), 3477-3480.
Riezzo, E. E., Zippitelli, M., Impedovo, D., Todorovic, M., Cantore, V., and Buono, V. (2013). Hydro?Tech: an integrated decision support system for sustainable irrigation management (II): software and hardware architecture. CIGR Proceedings, 1(1) 443-486.
Rodrigues, F.A., Bramley, R.G.V., and Gobbett, D.L. (2015). Proximal soil sensing for precision agriculture: Simultaneous use of electromagnetic induction and gamma radiometrics in contrasting soils. Geoderma 243, 183–195.
Rodrigues, A.S., Marçal, A.R.S., and Cunha, M. (2009). Evaluation of data fusion methods for agricultural monitoring based on synthetic images. Remote Sensing for a Changing Europe: Proceedings of the 28th Symposium of the European Association of Remote Sensing Laboratories, 125-133.
Rosero-Vlasova, O.A., Pérez-Cabello, F., Montorio Llovería, R., and Vlassova, L. (2016). Assessment of laboratory VIS-NIR-SWIR setups with different spectroscopy accessories for characterisation of soils from wildfire burns. Biosystems Engineering, 152, 51-67.
Salampasis, M., and Theodoridis, A. (2013). Information and communication technology in agricultural development. Procedia Technology, 8, 1-3.
Schemberger, E.E., Fontana, F.S., Johann, J.A., and De Souza, E.G. (2017). Data mining for the assessment of management areas in precision agriculture. Engenharia Agrícola, 37(1), 185-193.
Schenatto, K., De Souza, E.G, Bazzi, C.L., Gavioli, A., Betzek, N.M., and Beneduzzi, H.M. (2017). Normalization of data for delineating management zones. Computers and Electronics in Agriculture, 143, 238-248.
Servadio, P., Bergonzoli, S., and Verotti, M. (2017). Delineation of management zones based on soil mechanical-chemical properties to apply variable rates of inputs throughout a field (VRA). Engineering in Agriculture, Environment and Food, 10(1), 20-30.
Shaddad, S.M., Madrau, S., Castrignanò, A., and Mouazen, A.M. (2016). Data fusion techniques for delineation of site-specific management zones in a field in UK. Precision agriculture, 17(2), 200-217.
Shannon, D.K., Clay, D.E., Sudduth, K.A., Shannon, D.K., Clay, D.E., and Kitchen, N.R. (2018). An introduction to precision agriculture. In: D. Kent Shannon, David E. Clay, and Newell R. Kitchen (Ed.), Precision Agriculture Basics. (Pp. 1–12.), American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.
Sissons, M., Abecassis, J., Marchylo, B., and Carcea, M. (2016). Durum Wheat: Chemistry and Technology (2nd Ed). AACC International, Elsevier Inc.
Stafford, J.V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76(3), 267-275.
Stewart, C.M., McBratney, A.B., and Skerritt, J.H. (2002). Site-specific durum wheat quality and its relationship to soil properties in a single field in northern New South Wales. Precision Agriculture, 3(2), 155-168.
Tagarakis, A., Liakos, V., Fountas, S., Koundouras, S., and Gemtos, T.A. (2013). Management zones delineation using fuzzy clustering techniques in grapevines. Precision Agriculture, 14(1), 18-39.
Tedone, L., Ali, S.A., and De Mastro, G. (2018). Optimization of nitrogen in durum wheat in the Mediterranean climate: the agronomical aspect and greenhouse gas (GHG) emissions.In Nitrogen in Agriculture-Updates. IntechOpen, DOI: 10.5772/intechopen.70195
Thorp, K.R., Wang, G., Bronson, K.F., Badaruddin, M., and Mon, J. (2017). Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield. Computers and Electronics in Agriculture, 136, 1-12.
Tilman, D., Balzer, C., Hill, J., and Befort, B.L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260-20264.
Todorovic, M., Cantore, V., Riezzo, E. E., Zippitelli, M., Galiano, A., and Buono, V. (2013). Hydrotech: an integrated decision support system for sustainable irrigation management (I): main algorithms and field testing. CIGR Proceedings, 1(1), 425-41.
Troccoli, A., Borrelli, G.M., De Vita, P., Fares, C., and Di Fonzo, N. (2000). Durum wheat quality: A multidisciplinary concept. Journal of Cereal Science, 32, 99–113.
Verma, A., Jatain, A., and Bajaj, S. (2018). Crop Yield Prediction of Wheat Using Fuzzy C Means Clustering and Neural Network. International Journal of Applied Engineering Research, 13(11), 9816-9821.
Veum, K.S., Sudduth, K.A., Kremer, R.J., and Kitchen, N.R. (2017). Sensor data fusion for soil health assessment. Geoderma, 305, 53-61.
Vidanapathirana, N.P. (2012). Agricultural information systems and their applications for development of agriculture and rural community, a review study. The 35th Information Systems Research Seminar in Scandinavia–IRIS, 1, 1-14.
Wagner, N.C. (2004). Wheat yield prediction modeling for localized optimization of fertilizer and herbicide application. Doctoral dissertation, Montana State University-Bozeman, College of Agriculture, 251.
Whelan, B., and Taylor, J. (2013). Precision agriculture for grain production systems. CSIRO Publishing, Clayton, Australia.
Xie, W., Xue, Y., Zhai, L., and Sang, H. (2013). Data fusion technology of multi-platform earth observation on agriculture. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1(1), 189-192.
Zhang, Q. (2017). Automation in Tree Fruit Production: Principles and Practice. (Pp. 312 ). CABI, UK.
Zhang, L., Guo, C.L., Zhao, L.Y., Zhu, Y., Cao, W.X., Tian, Y.C., Cheng, T., and Wang, X. (2016). Estimating wheat yield by integrating the WheatGrow and PROSAIL models. Field Crops Research, 192, 55-66.
Zheng, Y., Zhang, M., Zhang, X., Zeng, H., and Wu, B. (2016). Mapping winter wheat biomass and yield using time series data blended from -PROBA-V 100 and 300-m S1 products. Remote Sensing, 8(10), 824.
.