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Advances in artificial intelligence (AI) for more effective decision making in agriculture

Code: 9781786767288
Publication date: 27/04/2020
Extent: 40 pages

Contributions by: L. J. Armstrong, Edith Cowan University, Australia; N. Gandhi, University of Mumbai, India; P. Taechatanasat, Edith Cowan University, Australia; and D. A. Diepeveen, Department of Primary Industries and Regional Development, Australia

Chapter synopsis: This chapter reviews developments in the use of artificial intelligence (AI) techniques to improve the functionality of decision support systems (DSS) in agriculture. It reviews the use of techniques such as data mining, artificial neural networks (ANN), Bayesian networks (BN), support vector machines (SVM) and association rule mining. It includes a number of case studies of practical application of these techniques to support decision making by farmers.

DOI: 10.19103/AS.2020.0069.06
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Table of contents 1 Introduction 2 Agricultural DSS using AI technologies: an overview 3 Data and image acquisition 4 Core AI technologies 5 Case study 1: AgData DSS tool for Western Australian broad acre cropping 6 Case study 2: GeoSense 7 Case study 3: Rice-based DSS 8 Summary and future trends 9 Where to look for further information 10 References

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