Data-Driven Decision Making in Precision Agriculture: The Rise of Big Data in Agricultural Systems
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Stavros Souravlas | Manos Roumeliotis | Nicoleta Tantalaki | M. Roumeliotis | S. Souravlas | Nicoleta Tantalaki
[1] Bruce V. Taylor,et al. Big Data, Trust and Collaboration: Exploring the socio-technical enabling conditions for big data in the grains industry , 2016 .
[2] '. E.J.Sadler. Modeling for precision agriculture : how good is good enough , and how can we tell ? , 2007 .
[3] D. Bui,et al. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. , 2015 .
[4] Zoran Obradovic,et al. Neural network-based software for fertilizer optimization in precision farming , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[5] Jeetendra Pande,et al. Survey of Crop Yield Estimation Models with Emphasis on Artificial Neural Network Model. , 2008 .
[6] C. L. Philip Chen,et al. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..
[7] M. Carolan. Publicising Food: Big Data, Precision Agriculture, and Co-Experimental Techniques of Addition , 2017 .
[8] G. Morota,et al. BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture , 2018, Journal of animal science.
[9] Maria J. Diamantopoulou,et al. Artificial neural networks as an alternative tool in pine bark volume estimation , 2005 .
[10] Michael E. Sykuta,et al. Big Data in Agriculture: Property Rights, Privacy and Competition in Ag Data Services , 2016 .
[11] Board on Agriculture. Precision Agriculture in the 21st Century: Geospatial and Information Technologies in Crop Management , 1998 .
[12] Juan Frausto Solís,et al. Predictive ability of machine learning methods for massive crop yield prediction , 2014 .
[13] Nitish Srivastava,et al. Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..
[14] Han Liu,et al. Challenges of Big Data Analysis. , 2013, National science review.
[15] Prem Prakash Jayaraman,et al. Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt , 2016, Sensors.
[16] A. Rao,et al. Transforming Weather Index-Based Crop Insurance in India: Protecting Small Farmers from Distress. Status and a Way Forward. Research Report IDC-8 , 2016 .
[17] Kah Phooi Seng,et al. Big data and machine learning for crop protection , 2018, Comput. Electron. Agric..
[18] Shahin Ara Begum,et al. Regression and Neural Networks Models for Prediction of Crop Production , 2013 .
[19] P. C. Robert,et al. Variability of corn/soybean yield and soil/landscape properties across a southwestern Minnesota landscape , 1999 .
[20] Huadong Guo,et al. Scientific big data and Digital Earth , 2014 .
[21] Kenneth A. Sudduth,et al. STATISTICAL AND NEURAL METHODS FOR SITE–SPECIFIC YIELD PREDICTION , 2003 .
[22] Jon Atli Benediktsson,et al. Big Data for Remote Sensing: Challenges and Opportunities , 2016, Proceedings of the IEEE.
[23] Paulo Estevão Cruvinel,et al. Big Data Environment for Agricultural Soil Analysis from CT Digital Images , 2016, 2016 IEEE Tenth International Conference on Semantic Computing (ICSC).
[24] Shaowen Wang,et al. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach , 2018, Remote Sensing of Environment.
[25] Dayton M. Lambert,et al. A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina , 2004, Precision Agriculture.
[26] Brian C. Briggeman,et al. Farmers’ Perceptions of Building Trust , 2016 .
[27] Patrizia Busato,et al. Machine Learning in Agriculture: A Review , 2018, Sensors.
[28] Bogdan Zagajewski,et al. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images , 2017 .
[29] Albert Y. Zomaya,et al. Remote sensing big data computing: Challenges and opportunities , 2015, Future Gener. Comput. Syst..
[30] Joann J. Ordille,et al. Data integration: the teenage years , 2006, VLDB.
[31] D. Edwards,et al. Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline , 2018, Agriculture.
[32] Luis Alonso,et al. Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation , 2011, IEEE Geoscience and Remote Sensing Letters.
[33] W. Salas,et al. Mapping secondary tropical forest and forest age from SPOT HRV data , 1999 .
[34] Helmut Krcmar,et al. Big Data , 2014, Wirtschaftsinf..
[35] Shashi Shekhar,et al. Spatiotemporal Data Mining: A Computational Perspective , 2015, ISPRS Int. J. Geo Inf..
[36] Enrico Biffis,et al. Satellite Data and Machine Learning for Weather Risk Management and Food Security , 2017, Risk analysis : an official publication of the Society for Risk Analysis.
[37] Yongguo Yang,et al. Comprehensive Evaluation of Machine Learning Techniques for Estimating the Responses of Carbon Fluxes to Climatic Forces in Different Terrestrial Ecosystems , 2018 .
[38] N. Kshetri. The emerging role of Big Data in key development issues: Opportunities, challenges, and concerns , 2014, Big Data Soc..
[39] Z. Li,et al. Wireless sensor networks (WSNs) in the agricultural and food industries , 2013, ICRA 2013.
[40] Keith H. Coble,et al. Big Data in Agriculture: A Challenge for the Future , 2018 .
[41] L. Plümer,et al. Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance , 2010 .
[42] Salah Sukkarieh,et al. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..
[43] Fangju Wang,et al. The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment , 1994 .
[44] Hao Wang,et al. PSVM : Parallelizing Support Vector Machines on Distributed Computers , 2007 .
[45] John M. Antle,et al. Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science , 2017, Agricultural systems.
[46] Frank Canters,et al. A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium) , 2008 .
[47] Venkat N. Gudivada,et al. Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations , 2017 .
[48] Anupam Joshi,et al. Application of neural networks: precision farming , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[49] Qian Cheng,et al. A deep convolutional neural network approach for predicting phenotypes from genotypes , 2018, Planta.
[50] Evan D. G. Fraser,et al. Opportunities and Challenges for Big Data in Agricultural and Environmental Analysis , 2018, Annual Review of Resource Economics.
[51] Steve Sonka,et al. Precision Agriculture: Not the Same as Big Data But... , 2015 .
[52] Jefersson Alex dos Santos,et al. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[53] Yuxiang Xie,et al. Multi-source heterogeneous data fusion , 2018, 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD).
[54] Carlos Guestrin,et al. Distributed GraphLab : A Framework for Machine Learning and Data Mining in the Cloud , 2012 .
[55] Bin Jiang,et al. Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges , 2015, ArXiv.
[56] Amaury Lendasse,et al. Extreme learning machines for soybean classification in remote sensing hyperspectral images , 2014, Neurocomputing.
[57] Kelly R. Thorp,et al. Precision Agriculture , 2014, Encyclopedia of Remote Sensing.
[58] S. Shekhar,et al. Agriculture Big Data ( AgBD ) Challenges and Opportunities From Farm To Table : A Midwest Big Data Hub Community † Whitepaper , 2017 .
[59] Shusen Yang,et al. Distributed Real-Time Anomaly Detection in Networked Industrial Sensing Systems , 2015, IEEE Transactions on Industrial Electronics.
[60] S. Wolfert,et al. Big Data in Smart Farming – A review , 2017 .
[61] Austin Jensen,et al. Spatial Root Zone Soil Water Content Estimation in Agricultural Lands Using Bayesian‐Based Artificial Neural Networks and High‐ Resolution Visual, NIR, and Thermal Imagery , 2017 .
[62] A. Kravchenko,et al. Correlation of Corn and Soybean Grain Yield with Topography and Soil Properties , 2000 .
[63] N. B. Anuar,et al. The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..
[64] Naixue Xiong,et al. Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks , 2017, Sensors.
[65] J. Misiurewicz,et al. Machine learning methods in data fusion systems , 2012, 2012 13th International Radar Symposium.
[66] Aysha Fleming,et al. Is big data for big farming or for everyone? Perceptions in the Australian grains industry , 2018, Agronomy for Sustainable Development.
[67] Olac Fuentes,et al. Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology , 2014 .
[68] An innovative approach based on neural networks for predicting soil component variability. , 2003 .
[69] Paresh Chandra Deka,et al. Estimation of dew point temperature using SVM and ELM for humid and semi-arid regions of India , 2018 .
[70] Andreas Kamilaris,et al. A review on the practice of big data analysis in agriculture , 2017, Comput. Electron. Agric..
[71] Joshua D. Woodard,et al. Big data and Ag-Analytics , 2016 .
[72] Alejandro Baldominos Gómez,et al. A scalable machine learning online service for big data real-time analysis , 2014, 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD).
[73] Nishikant Mishra,et al. Social media data analytics to improve supply chain management in food industries , 2017, Transportation Research Part E: Logistics and Transportation Review.
[74] Jorge Torres-Sánchez,et al. An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery , 2018, Remote. Sens..
[75] Valentyn Tolpekin,et al. A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture , 2017, Remote. Sens..
[76] S. Krishnamurthy. Dealing with high-dimensionality in large data sets Part 1 : Foundations and Basics , 2014 .
[77] S. Minaei,et al. Early detection and classification of powdery mildew-infected rose leaves using ANFIS based on extracted features of thermal images , 2016 .
[78] Nancy Alonistioti,et al. Farm management systems and the Future Internet era , 2012 .
[79] Andreas Kamilaris,et al. Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..
[80] Renata Wachowiak-Smolíkova,et al. Visual analytics and remote sensing imagery to support community-based research for precision agriculture in emerging areas , 2017, Comput. Electron. Agric..
[81] PesonenLiisa,et al. Farm management systems and the Future Internet era , 2012 .
[82] Jaesik Choi,et al. Learning Compressive Sensing Models for Big Spatio-Temporal Data , 2015, SDM.
[83] Jan G. P. W. Clevers,et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .
[84] N. Rao. Big Data and Climate Smart Agriculture - Review of Current Status and Implications for Agricultural Research and Innovation in India , 2017 .
[85] Xinfeng Zhao,et al. Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: the importance of phosphorus application rates under drought conditions. , 2009 .
[86] Sander Janssen,et al. Analysis of Big Data technologies for use in agro-environmental science , 2016, Environ. Model. Softw..
[87] Georg Ruß,et al. Data Mining of Agricultural Yield Data: A Comparison of Regression Models , 2009, ICDM.
[88] Panos M. Pardalos,et al. Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey , 2009 .
[89] Chunming Wu,et al. Analysis of Plant Breeding on Hadoop and Spark , 2016 .
[90] Ming-Hsiang Tsou. Big data: techniques and technologies in geoinformatics , 2014, Ann. GIS.
[91] Samuel Rathmanner,et al. Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making , 2017 .
[92] Isabelle Carbonell,et al. The Ethics of Big Data in Big Agriculture , 2016 .
[93] Ravindra C. Thool,et al. Big Data in Precision Agriculture Through ICT: Rainfall Prediction Using Neural Network Approach , 2016 .
[94] Xanthoula Eirini Pantazi,et al. Wheat yield prediction using machine learning and advanced sensing techniques , 2016, Comput. Electron. Agric..
[95] Edward Y. Chang,et al. Parallelizing Support Vector Machines on Distributed Computers , 2007, NIPS.
[96] Alexandros Kaloxylos,et al. A cloud-based Farm Management System: Architecture and implementation , 2014 .
[97] Córdova-Cruzatty Andrea,et al. Precise weed and maize classification through convolutional neuronal networks , 2017, 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM).
[98] Edward Y. Chang,et al. Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception , 2011 .
[99] Claudia Notarnicola,et al. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data , 2015, Remote. Sens..
[100] V. R. Thool,et al. Big data in precision agriculture: Weather forecasting for future farming , 2015, 2015 1st International Conference on Next Generation Computing Technologies (NGCT).
[101] Ingmar Nitze,et al. COMPARISON OF MACHINE LEARNING ALGORITHMS RANDOM FOREST, ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE TO MAXIMUM LIKELIHOOD FOR SUPERVISED CROP TYPE CLASSIFICATION , 2012 .
[102] Juan Frausto-Solís,et al. Predictive ability of machine learning methods for massive crop yield prediction , 2014 .
[103] Lorenzo Bruzzone,et al. Estimating Soil Moisture With the Support Vector Regression Technique , 2011, IEEE Geoscience and Remote Sensing Letters.
[104] H. Pourghasemi,et al. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran , 2016 .