Instant Insights: Machine Vision Applications in Agriculture
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Instant Insights: Machine Vision Applications in Agriculture
Arcidiacono, Prof Claudia; Lowry, Dr Stephanie; Kurtser, Dr Polina; Long, Dr Megan; authors, Various; Ringdahl, Dr Ola; Ma, Dr Wei; Sauzet, Dr Ophelie; Tian, Dr Zhiwei; Gilliot, Dr Jean-Marc
Burleigh Dodds Science Publishing Limited
10/2024
116
Mole
9781835450086
15 a 20 dias
Descrição não disponível.
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 Ophelie 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, OErebro University and Umea University, Sweden; Stephanie Lowry, OErebro University, Sweden; and Ola Ringdahl, Umea 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)
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, OErebro University and Umea University, Sweden; Stephanie Lowry, OErebro University, Sweden; and Ola Ringdahl, Umea 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)
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
soil sensor;machine vision;structure;texture;soil surface roughness;Machine learning;crop disease;plant pathology;computer vision;robotics;2D/3D perception;deep learning;agricultural automation;intelligent detection;vertical farming;food production;dairy cattle;pigs;animal welfare;frames;support vector machine (SVM)
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 Ophelie 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, OErebro University and Umea University, Sweden; Stephanie Lowry, OErebro University, Sweden; and Ola Ringdahl, Umea 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)
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, OErebro University and Umea University, Sweden; Stephanie Lowry, OErebro University, Sweden; and Ola Ringdahl, Umea 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)
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
soil sensor;machine vision;structure;texture;soil surface roughness;Machine learning;crop disease;plant pathology;computer vision;robotics;2D/3D perception;deep learning;agricultural automation;intelligent detection;vertical farming;food production;dairy cattle;pigs;animal welfare;frames;support vector machine (SVM)
