Buch, Englisch, Band 26, 232 Seiten, Paperback, Format (B × H): 152 mm x 229 mm, Gewicht: 346 g
Advances in detecting and forecasting crop pests and diseases
Buch, Englisch, Band 26, 232 Seiten, Paperback, Format (B × H): 152 mm x 229 mm, Gewicht: 346 g
Reihe: Burleigh Dodds Science: Instant Insights
ISBN: 978-1-80146-506-9
Verlag: Burleigh Dodds Science Publishing
The first chapter introduces the concept of machine learning to identify and diagnose crop diseases, focussing on the deep learning concept.
The second chapter discusses recent advances in crop disease forecasting models, focussing on the application of precision agriculture technologies and Earth observation satellites to identify areas at risk of possible disease outbreaks.
The third chapter explores the contribution of remote sensing in improving the ways in which plant health is monitored in response to exposure to biotic stresses, such as disease.
The fourth chapter reviews how sensor technologies in combination with informatics and modern application technologies can contribute to more effective pest control.
The fifth chapter assesses the role of decision support systems for pest monitoring and management through information technology, such as spectral indices and image-based diagnostics.
The final chapter addresses key issues and challenges in pest monitoring and forecasting models, such as the limitation of some traps in attracting insects through the use of sex pheromones.
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Nachhaltige Landwirtschaft
- Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau Gartenbau
- Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau Schädlingsbekämpfung
- Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Landwirtschaftliche Bewässerung
Weitere Infos & Material
Chapter 1 - Using machine learning to identify and diagnose crop diseases: Megan Long, John Innes Centre, UK; 1 Introduction2 A quick introduction to deep learning3 Preparation of data for deep learning experiments4 Crop disease classification5 Different visualisation techniques6 Hyperspectral imaging for early disease detection7 Case study: Identification and classification of diseases on wheat8 Conclusion and future trends9 Where to look for more information10 References
Chapter 2 - Advances in crop disease forecasting models: Nathaniel Newlands, Summerland Research and Development Centre, Science and Technology Branch, Agriculture and Agri-Food Canada, Canada; 1 Introduction2 Modeling complex, crop-disease-environment dynamics3 Big data assimilation to improve forecast quality4 Novel artificial intelligence (AI)-based methodologies5 Case study: operational, crop disease early-warning systems6 Conclusion and future trends7 Where to look for further information8 References
Chapter 3 - Advances in remote/aerial sensing technologies to assess crop health: Michael Schirmann, Leibniz Institute of Agricultural Engineering, Germany; 1 Introduction2 Remote sensing of crop health3 Remote sensing of crop diseases4 Case study: detecting stripe rust using very high-resolution imaging5 Conclusion and future trends6 Where to look for further information7 References
Chapter 4 - Precision crop protection systems: E. C. Oerke, University of Bonn, Germany; 1 Introduction2 Variability of pest incidence and pest management strategies3 Sensor use for disease management4 Sensor use for the management of invertebrate pests5 Perspectives6 References
Chapter 5 - Decision-support systems for pest monitoring and management: B. Sailaja, Ch. Padmavathi, D. Krishnaveni, G. Katti, D. Subrahmanyam, M. S. Prasad, S. Gayatri and S. R. Voleti, ICAR-Indian Institute of Rice Research, India; 1 Introduction2 Pest identification3 Pest monitoring4 Pest forecasting5 Integrated pest management (IPM)6 Case studies7 Summary and future trends8 Where to look for further information9 References
Chapter 6 - Advances in insect pest and disease monitoring and forecasting in horticulture: Irene Vänninen, Natural Resources Institute Finland (LUKE), Finland; 1 Introduction2 Addressing key issues and challenges of pest monitoring and forecasting3 Case study: whitefly sampling, monitoring and forecasting
4 Conclusion5 Future trends in research6 Where to look for further information7 References
