Machine vision applications in agriculture

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 Ophélie 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 taken from: Lobsey, C. and Biswas, A. (ed.), Advances in sensor technology for sustainable crop production, Burleigh Dodds Science Publishing, Cambridge, UK, 2023, (ISBN: 978 1 78676 977 0)

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 taken from: Lobsey, C. and Biswas, A. (ed.), Advances in sensor technology for sustainable crop production, Burleigh Dodds Science Publishing, Cambridge, UK, 2023, (ISBN: 978 1 78676 977 0)

Chapter 3 - Advances in machine learning for agricultural robots: Polina Kurtser, Örebro University and Umeå University, Sweden; Stephanie Lowry, Örebro University, Sweden; and Ola Ringdahl, Umeå 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 taken from: van Henten, E. and Edan, Y. (ed.), Advances in agrifood robotics, Burleigh Dodds Science Publishing, Cambridge, UK, 2024, (ISBN: 978 1 80146 277 8)

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 taken from: Kozai, T. and Hayashi, E. (ed.), Advances in plant factories: New technologies in indoor vertical farming, Burleigh Dodds Science Publishing, Cambridge, UK, 2023, (ISBN: 978 1 80146 316 4)

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

Chapter taken from: Berckmans, D. (ed.), Advances in precision livestock farming, Burleigh Dodds Science Publishing, Cambridge, UK, 2022, (ISBN: 978 1 78676 471 3)