Advances in sensor technology for sustainable crop production

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 Universit©, EPHE, UMR7619, M©tis,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 Orl©ans 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 Oph©lie 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, S£o Paulo State University (UNESP), Brazil; Phillip Lanza and Fagner A. Rontani, Louisiana State University, USA; Armando L. Brito Filho, S£o Paulo State University (UNESP), Brazil; Getºlio 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;