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 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 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, 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 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