Instant Insights

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, Mar­a Gyomar Gonz¡lez Gonz¡lez, Patricia Chueca and Sergio Cubero, Instituto Valenciano de Investigaciones Agrarias (IVIA), Spain; and Nuria Aleixos, Universitat Polit¨cnica de Val¨ncia, 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; Ren© 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