With the agricultural sector facing mounting pressure to reduce their carbon footprint, greater emphasis has been placed on improving existing components and practices, such as soil health and biodiversity, which have since emerged as key components to achieving regenerative agriculture.
Sensors provide the opportunity to measure crop and soil health at unparalleled scales and resolution. Key developments in sensor technology will help improve our current understanding and optimisation of the complex agricultural systems that make up our global ecosystem.
Advances in sensor technology for sustainable crop production
provides a comprehensive review of the wealth of research on key developments in sensor technology to improve monitoring and management of crop health, soil health, weeds and diseases. This collection also reviews advances in proximal and remote sensing techniques to monitor soil health, such as spectroscopy and radiometrics, as well as how sensor technology can be optimised for more targeted irrigation, site-specific nutrient and weed management.
- Assesses key developments in sensor technology to improve monitoring and management of complex agricultural systems
- Considers the growing influence of proximal crop sensors in assessing, monitoring and measuring the health of agricultural soils
- Explores the potential of remote and aerial sensing towards achieving sustainable crop production through more targeted irrigation management, site-specific nutrient management and weed management
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What others are saying...
“Much of future innovation in crop production will revolve around digital agriculture – the collection, management, interpretation and application of data. Sensor technology is a key component of this future. Thus it is exciting to see this collection about the application of sensors in sustainable crop production from these highly knowledgeable authors. This will be an important reference for students, researchers and practitioners applying sensors in crop production systems.” (Dr Richard B. Ferguson, Professor and International Soil Scientist, University of Nebraska-Lincoln, USA)
Table of contents
Part 1 Advances in remote sensing technologies
- 1.Advances in remote/aerial sensing of crop water status: Wenxuan Guo, Texas Tech University and Texas A&M AgriLife Research, USA; and Haibin Gu, Bishnu Ghimire and Oluwatola Adedeji, Texas Tech University, USA;
- 2.Advances in remote sensing technologies for assessing crop health: Michael Schirrmann, Leibniz Institute for Agricultural Engineering and Bioeconomy, Germany;
- 3.Advances in remote/aerial sensing techniques for monitoring soil health: Jeffrey P. Walker and Nan Ye, Monash University, Australia; and Liujun Zhu, Monash University, Australia and Yangtze Institute for Conservation and Development, Hohai University, China;
Part 2 Advances in proximal sensing technologies
- 4.Advances in using proximal spectroscopic sensors to assess soil health: Kenneth A. Sudduth and Kristen S. Veum, USDA-ARS, USA;
- 5.Advances in using proximal ground penetrating radar sensors to assess soil health: Katherine Grote, Missouri University of Science and Technology, USA;
- 6.Using proximal electromagnetic/electrical resistivity/electrical sensors to assess soil health: Alain Tabbagh,Sorbonne Université, EPHE, UMR7619, Métis,4 place Jussieu 75252 Paris CEDEX 05, France; and Seger Maud and Cousin Isabelle, INRAE, Centre Val de Loire, UR0272 SOLS, 2163 Avenue de la Pomme de Pin, CS40001 Ardon, F-45075 Orléans Cedex 2, France;
- 7.Using ground-penetrating radar to map agricultural subsurface drainage systems for economic and environmental benefit: Barry Allred, USDA-ARS – Soil Drainage Research Unit, USA; and Triven Koganti, Aarhus University, Denmark;
Part 3 Advances in sensor data analytics
- 8.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;
- 9.Using machine learning to identify and diagnose crop disease: Megan Long, John Innes Centre, UK;
- 10.Advances in proximal sensor fusion and multi-sensor platforms for improved crop management: David W. Franzen and Anne M. Denton, North Dakota State University, USA;
- 11.Using remote and proximal sensor data in precision agriculture applications: Luciano S. Shiratsuchi and Franciele M. Carneiro, Louisiana State University, USA; Francielle M. Ferreira, São Paulo State University (UNESP), Brazil; Phillip Lanza and Fagner A. Rontani, Louisiana State University, USA; Armando L. Brito Filho, São Paulo State University (UNESP), Brazil; Getúlio F. Seben Junior, State University of Mato Grosso (UNEMAT), Brazil; Ziany N. Brandao, Brazilian Agricultural Research Corporation (EMBRAPA), Brazil; Carlos A. Silva Junior, State University of Mato Grosso (UNEMAT), Brazil; Paulo E. Teodoro, Federal University of Mato Grosso do Sul (UFMS), Brazil; and Syam Dodla, Louisiana State University, USA;