Instant Insights: Artificial Intelligence Applications in Agriculture

Dr Leisa Armstrong is Senior Lecturer in Computer Science and leader of the eAgriculture Research Group at Edith Cowan University, Australia. Dr Armstrong is President of the Australian Society of ICT in Agriculture as well as past President of the Asian Federation of Information Technologies in Agriculture (AFITA). She has an international reputation for her research on the use of ICT in agriculture in such areas as agricultural information and decision support systems.

• Chapter 1 - Advances in artificial intelligence (AI) for more effective decision making in agriculture: 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; 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 ReferencesChapter taken from: Sørensen, C. G. (ed.), Smart farms: Improving data-driven decision making in agriculture, Burleigh Dodds Science Publishing, Cambridge, UK, 2024, (ISBN 978 1 80146 382 9)Chapter 2 - The use of intelligent/autonomous systems in crop irrigation: Stefano Carpin, University of California-Merced, USA; Ken Goldberg, University of California- Berkeley, USA; Stavros Vougioukas, University of California-Davis, USA; Ron Berenstein, University of California-Berkeley, USA; and Josh Viers, University of California-Merced, USA; 1 Introduction 2 Related work 3 Overview of RAPID 4 Preliminary results 5 Future trends and conclusion 6 Acknowledgements 7 Where to look for further information 8 ReferencesChapter taken from: Billingsley, J. (ed.), Robotics and automation for improving agriculture, Burleigh Dodds Science Publishing, Cambridge, UK, 2019, (ISBN 978 1 78676 272 6)Chapter 3 - Advances in automated in-field grading of harvested crops: Jose Blasco, María Gyomar González González, Patricia Chueca and Sergio Cubero, Instituto Valenciano de Investigaciones Agrarias (IVIA), Spain; and Nuria Aleixos, Universitat Politècnica de València, Spain; 1 Introduction 2 Advantages of in-field sorting 3 Harvest-assist platforms 4 Case study: in-field pre-sorting of citrus 5 Summary 6 Future trends in research 7 Where to look for further information 8 ReferencesChapter taken from: Billingsley, J. (ed.), Robotics and automation for improving agriculture, Burleigh Dodds Science Publishing, Cambridge, UK, 2019, (ISBN 978 1 78676 272 6)Chapter 4 - Automated assessment of plant diseases and traits by sensors: how can digital technologies support smart farming and plant breeding?: Anne-Katrin Mahlein, Institute of Sugar Beet Research, Germany; Jan Behmann, Bayer Crop Science, Germany; David Bohnenkamp, BASF Digital Farming GmbH, Germany; René H. J. Heim, UAV Research Centre (URC), Ghent University, Belgium; and Sebastian Streit and Stefan Paulus, Institute of Sugar Beet Research, Germany; 1 Introduction 2 Digital plant disease detection 3 Complexity of host–pathogen interactions 4 Complexity in a crop stand 5 Case study: application of deep learning to foliar plant diseases 6 Summary 7 Future trends in research 8 Where to look for further information 9 Acknowledgement 10 ReferencesChapter taken from: Walter, A. (ed.), Advances in plant phenotyping for more sustainable crop production, Burleigh Dodds Science Publishing, Cambridge, UK, 2022, (ISBN 978 1 78676 856 8)