Instant Insights: Artificial Intelligence applications in agriculture

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 References
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 References
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 References
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 References