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Smart farms: Improving data-driven decision making in agriculture

Code: 9781801463829
Edited by: Professor Claus Grøn Sørensen, Aarhus University, Denmark

The agricultural sector remains under increasing pressure to reduce its environmental impact and consequent contribution to climate change, whilst also producing enough food to feed a rapidly growing population. With the variety and volume of data, coupled with the advanced methods for data processing, a new era of digital agriculture is emerging as a possible solution to this monumental challenge.

Smart farms: improving data-driven decision making in agriculture provides a comprehensive review of the recent advances in gathering and analysing data as a means of improving farm sustainability, productivity and profitability. The book discusses the evolution of farm information management systems, highlighting current trends and challenges, as well as methods of data acquisition and analysis, including the use of artificial intelligence.

Key Features

  • Provides a detailed overview of the recent trends in farm information management systems, including their evolution and role in improving farmer decision making
  • Considers the range of data mining techniques used in decision support systems, such as artificial neural networks and support vector machines
  • Includes a selection of case studies which explore the use of decision support systems in optimising farm management and productivity
£140.00
Table of Contents

Part 1 General

  • 1.Trends in farm information management systems: Liisa Pesonen, Natural Resources Institute (LUKE), Finland;
  • 2.The role of digital technologies in achieving sustainable agriculture: Thiago L. Romanelli, André F. Colaço and João P. S. Veiga, University of São Paulo, Brazil;
  • 3.Key issues in incorporating proximal and remote sensor data into farm decision-making: Adélia M. O. Sousa, Universidade de Évora, MED, CHANGE, EarsLab, Portugal; José R. Marques da Silva, Universidade de Évora, MED, CHANGE, Agroinsider Lda, Portugal; João Serrano, Shakib Shahidian and Duarte Lobo da Silveira, Universidade de Évora, MED, CHANGE, Portugal; Manuela Simões, Universidade Nova de Lisboa, Portugal; Ana Cristina Gonçalves, Maria João P. Caldinhas and Vasco Fitas da Cruz, Universidade de Évora, MED, CHANGE, Portugal; Arilson J. de Oliveira Júnior and Silvia R. Lucas de Souza, São Paulo State University, Brazil; Diogo R. Coelho, Universidade de Évora, MED, Portugal; Patrícia Lourenço, Agroinsider Lda, Portugal; and Fátima F. Baptista, Universidade de Évora, MED, CHANGE, Portugal;
  • 4.Agri Semantics: developments to improve data interoperability to support farm information management and decision support systems in agriculture: Saba Noor, Jade Bokma and Bart Pardon, Ghent University, Belgium; Gerdien van Schaik, Utrecht University, The Netherlands; and Miel Hostens, Cornell University, USA;
  • 5.Using data mining techniques for decision support in agriculture: support vector machines: Wu Caicong, China Agricultural University, China;

Part 2 Case studies

  • 6.Developing decision support systems for irrigation/water management on farms: Fedro S. Zazueta, University of Florida, USA;
  • 7.Advances in crop disease forecasting models: Nathaniel Newlands, Summerland Research and Development Centre, Science and Technology Branch, Agriculture and Agri-Food Canada, Canada;
  • 8.Smart farming in extensive livestock production: the Australian experience: David W. Lamb, Food Agility Cooperative Research Centre/ Precision Agriculture Research Group - University of New England/ Gulbali Research Institute - Charles Sturt University, Australia;

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