Instant Insights

Chapter 1 - Advances in machine vision technologies for the measurement of soil texture, structure and topography: Jean-Marc Gilliot, AgroParisTech Paris Saclay University, France; and Oph©lie Sauzet, University of Applied Sciences of Western Switzerland, The Geneva Institute of Technology, Architecture and Landscape (HEPIA), Soils and Substrates Group, Institute Land-Nature- Environment (inTNE Institute), Switzerland;

1 Introduction

2 Basic principles

3 Case studies

4 Conclusion and future trends

5 Where to look for further information

6 Acknowledgements

7 References

Chapter 2 - Using machine learning to identify and diagnose crop diseases: Megan Long, John Innes Centre, UK;

1 Introduction* 2 A quick introduction to deep learning

3 Preparation of data for deep learning experiments

4 Crop disease classification

5 Different visualisation techniques

6 Hyperspectral imaging for early disease detection

7 Case study: identification and classification of diseases on wheat

8 Conclusion and future trends

9 Where to look for more information

10 References

Chapter 3 - Advances in machine learning for agricultural robots: Polina Kurtser, -rebro University and Ume¥ University, Sweden; Stephanie Lowry, -rebro University, Sweden; and Ola Ringdahl, Ume¥ University, Sweden;

1 Introduction

2 Applications of machine learning in agri-robotics

3 Challenges

4 Integration and field-testing use-cases

5 Conclusion

6 Where to look for further information

7 References

Chapter 4 - Application of machine vision in plant factories: Wei Ma and Zhiwei Tian, Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, China;

1 Introduction

2 Plant growth monitoring

3 Robot operation assistance

4 Fruit grading

5 The application of deep learning in the plant factory

6 Challenges faced by machine vision in plant factories

7 Conclusion

8 Declaration of competing interest

9 Where to look for further information

10 Acknowledgements

11 References

Chapter 5 - Machine vision techniques to monitor behaviour and health in precision livestock farming: C. Arcidiacono and S. M. C. Porto, University of Catania, Italy;

1 Introduction

2 Devices for data acquisition in computer visionbased systems

3 Animal species and tasks analysed in computer vision systems for precision livestock farming

4 Key elements of computer visionbased systems: initialisation

5 Key elements of computer visionbased systems: tracking image segmentation

6 Key elements of computer visionbased systems: tracking video object segmentation

7 Key elements of computer visionbased systems: feature extraction

8 Key elements of computer visionbased systems: pose estimation and behaviour recognition

9 Case studies of precision livestock farming applications based on traditional computer vision techniques

10 Advances in computer vision techniques: deep learning

11 Case studies of precision livestock farming applications based on deep learning techniques

12 Conclusion

13 References