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DSANRM 2017 : Workshop on ‘Data Science for Agriculture and Natural Resource Management (DSANRM)'


When Dec 12, 2017 - Dec 12, 2017
Where Hyderabad, India
Abstract Registration Due Oct 9, 2017
Submission Deadline Oct 16, 2017
Notification Due Nov 23, 2017
Final Version Due Nov 30, 2017
Categories    data science   big data analytics   agriculture   natural resource management

Call For Papers

Any kind of development is incomplete without the development in agriculture and in absence of natural resources. What will happen, if we have a developed the society in terms of information and infrastructure but no access to food or natural resources? We are experiencing a tremendous technology growth and evolution of innovative applications using Data Science and Big Data Analytics (BDA). Internet of Agriculture Things (AgThings) helps us to capture big data and in upcoming decade the farms will be a great source of data. The data obtained from farms can be used for precision agriculture and will help farmers or end users to take better decisions. The objective of workshop is to discuss the potential application areas of Data Science in Agriculture and Natural Resource Management domain. This workshop will demonstrate how data integration and analytics using data science can help farmers to increase their earnings and enable them to take better decisions. The agriculture and natural resource management sector needs keen attention of data science research community for finding innovative solutions to the problem of best utilization of natural resources and to increase the yield of agro-produces. Now a days, we are witnessing the information age and we have best time to make use of available voluminous, dynamic, and real time data for taking most efficient decision for the management of natural resources and agricultural development. The objective of workshop is to provide a platform where researchers, academicians, professionals and practitioners can share state of the art technology for best utilization of natural resources and a highly productive environment for agriculture. The workshop is in conjunction with Big Data Analytics 2017 and it aims:

- To provide a forum where experts can discuss important contributions towards or research activities in the area of data science in Agriculture and Natural Resource Management.
- To discuss experimental research work in the area of Data Science, Big Data Analytics and Machine learning using any software libraries as well as BDA frameworks.
- To attract agro-experts and professionals to discuss hidden agriculture and farming patterns for the use of data in related domains.
- To discuss available datasets, acquisition, cleaning, integration and data analytics in agriculture and natural resource management.
- To discuss and identify relevant research issues for big data analytics and data science for agriculture and natural resource management.

The potential outcome of the workshop is to address challenges and issues related to Data Science in Agriculture and Natural Resource Management, data mining of weather or crop production data for estimation or prediction of disease, tools, data mining for the prediction models to solve the problem and develop frameworks for data mining in a specific domain of agricultural databases. We welcome the applications of data mining in spatial or agriculture as well as commodity market data analysis. We invite all researchers, scientists, professionals and academicians to share their interesting research, ideas, experience and results. The major topics related to large, complex, big data analytics and knowledge discovery are invited.

Submission Guidelines

Prospective authors are invited to submit original research papers (not being considered for publication elsewhere) from 15 to 20 pages in length in the LNCS style. Unformatted papers and papers beyond the page limit will not be reviewed. The submissions will be accepted through
Each paper should contain an abstract of approximately 300 words having a page limit of 20 pages including the title page, references and appendix. For preparing the manuscript, please see instructions for authors by Springer, in the Lecture Notes in Computer Science series (LNCS)
LaTeX and Word Templates & Submission Guidelines:
All submitted papers will be peer-reviewed by at least three program committee members. At least one author of each accepted paper is required to register at the workshop and present the paper. Accepted and presented papers will appear in the online workshop proceedings.

List of Topics

The workshop invites original research papers in the areas related to ‘Data Science in Agriculture and Natural Resource Management’. Topics include but are not limited to:
Agricultural Data Model including sensing and reliability
Algorithm Designing and implementation in Agriculture Databases
Big data analytics and social media for agriculture
Cloud and grid computing for agriculture
Data Mining tools for analysis of Spatial Agriculture Data
Data mining with relevance to prognosis of disease
Data mining, graph mining and data science for agriculture
Data science applications in agriculture
Data science for decision support system for natural resource management
Data Warehousing and Integration for Agriculture
Exogenous parameters estimation for increasing crop yield
Framework for mining complex and large data, e.g. a combination of experimentation, images, and use cases
Innovations in agriculture and sensing devices used in farming
Knowledge based agriculture data models
Managing water resources by data analytics
Models and tools for smart computing in agriculture
Online Algorithms and Analytics for data generated by Sensors and Machines used by green houses and precision agriculture
Ontology based study in context to data mining
Precision agriculture and smart farming
Security and privacy for big data in agriculture
Smart devices and hardware for precision agriculture
Smart farming and big data
Smart location-based services for agriculture recommendations
Smart use of natural resources by using data science
Spatial, Temporal and Sequential Agriculture Data Mining
Standards for big data and spatial data in agriculture
Various scalability techniques for processing of large databases
Visualization and analytics of agriculture data


Workshop Chair

Dr. Sanjay Chaudhary, Professor and Associate Dean, School of Engineering and Applied Science, Ahmedabad University, India
Program committee

Dr. Devesh Jinwala, Professor, Department of Computer Engineering, S V National Institute of Technology, Surat, India
Dr. Mehul Raval, School of Engineering and Applied Science, Ahmedabad University, India
Prof. Rajender Parsad, ICAR – Indian Agricultural Statistics Research Institute, New Delhi, India

Technical Program committee:

Prof. M V Joshi, Professor, DA-IICT, Gandhinagar, India
Prof. C V Jawahar, Professor, Centre for Visual Information Technology (CVIT), IIIT Hyderabad, India
Dr. Rushi Bhatt, LinkedIn, Bengaluru, India
Dr. Amit Ganatra, Dean, Faculty of Technology and Engineering, CHARUSAT, Gujarat, India
Prof. M. T. Savaliya, Vishwakarma Government Engineering College, Ahmedabad, India
Dr. Sanjay Garg, Institute of Technology, Nirma University, Ahmedabad, India
Dr. Chandra B. Singh, GRDC Associate Professor, Stored Grains Engineering, School of Engineering, University of South Australia, Australia
Dr. Dileepkumar Guntuku, Global Program Leader, Iowa State University, USA
Dr. Ratnik Gandhi, School of Engineering and Applied Science, Ahmedabad University, India
Dr. Pankesh Patel, Research Scientist, Fraunhofer Inc., USA
Dr. Saurabh Srivastava, Research Scientist & Project Manager, Conduent Labs, Bengaluru, India
Dr. Avital Bechar, The Institute of Agricultural Engineering, Israel


The conference will be held in International Institute of Information Technology (IIIT, Hyderabad), Gachibowli, Hyderabad 500 032, Telangana, INDIA

All questions about submissions should be emailed to

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