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Manuscript deadline
25 October 2020

Cover image - Acta Agriculturae Scandinavica Section B  Soil & Plant Science

Acta Agriculturae Scandinavica Section B Soil & Plant Science

Special Issue Editor(s)

Dr. Gunasekaran Manogaran, Big Data Scientist, University of California, Davis, USA
[email protected]

Dr. Hassan Qudrat-Ullah, Professor of Decision Sciences, School of Administrative Studies York University, Toronto, Canada
[email protected]

Dr. Qin Xin, Full Professor of Computer Science, Faculty of Science and Technology, University of the Faroe Islands, Faroe Islands. Denmark
[email protected]

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Envisage Computer Modelling and Statistics for Agriculture

As a new interdisciplinary research area, computer modelling and statistics for agriculture has attracted more and more attention from researchers and practitioners industry as well as the academics.  Right from the origin of nature, agriculture plays a vital role in the day to day human lives. However, with the unprecedented growing population, agriculture is facing a huge threat, due to the under utilization of resources and improper production in meeting people's demands. Hence, the process of convergence of advanced computational methodologies for agriculture can significantly result in numerous benefits with enhanced productivity measures. In general, agriculture is a continuous development process progressively changes from time to time with various environmental factors. In consideration of dynamic environmental scenarios and adverse population growth, advanced measures have to be taken in agriculture to meet global demands.  A systematic analysis of various environmental factors such as temperature, humidity, soil PH measures, and environmental conditions can greatly enhance agricultural productivity with advanced protection measures.  The data collected from agricultural lands and farms shall be used to analyze the growth of the crops and agricultural productivity measures. It is further used to find topography of the soil, resources present in the soil, suitability of plants for the field, and several other factors. The use of precise computing measures can enhance agricultural productivity with reduced risk of agricultural bugs. Thus, innovations in computational intelligence and statistics can greatly influence agricultural productivity with advanced techniques and methodologies.

The interpretation of computing devices has developed into a union of multiple technologies. In agriculture, implementing computational and statistical intelligence to analyze various agricultural data sources such as rainfall, humidity, wind speed, pest infestation, and soil content can effectively automate farming processes with improved crop forecasting and minimized risk of climatic changes and natural hazards. Besides, statistical analysis enables the system to make knowledgeable decisions that can improve the quantity and quality of the agricultural products with lesser efforts to crop management. Advanced computer modelling techniques in farming might bring an ardent future to farmers. Moreover, monitoring farm and agricultural fields from anywhere and everywhere can be possible only through advanced techniques. Using device based gathering and mapping of data in agriculture will be immensely helpful with the help of current intelligence and real-time analysis.

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Submission Instructions

Select "Envisage Computer Modelling and Statistics for Agriculture" when submitting your paper to scholarOne

This special issue aims to address present-day challenges in agriculture. But not limited to the following:

  • Role of internet of things and statistical analysis across smart farms
  • Advances in system simulations and modelling for agricultural computational analysis and decision making
  • Parallel and distributed simulation methodologies for crop monitoring and pest control
  • Decision making with advanced computational modelling and statistical approaches
  • Statistical modelling for precision agriculture
  • Convergence of artificial intelligence with statistical modelling for enhanced decision making and prediction in agriculture
  • Quality control and production improvement with computer modelling approaches
  • Big data with statistical modelling to enhance agricultural productivity
  • Sustainable computing models and innovations for agriculture
  • Modelling sensor data to obtain useful information for agriculture
  • Computing modelling for knowledge discovery and representation

Important dates

Paper Submission Deadline 25-10-2020
Author notification 15-12-2020
Revised papers submission 15-01-2021
Final Acceptance 20-03-2021

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