Technology, Modelling and Crop Sensing
This theme include conference papers from the the conference sessions Ag tech and data analytics, New generation system modelling, Sensing crops for better decision making, and Spatial mapping tools and approaches.
A comparison between machine learning and simple mechanistic-type models for yield prediction in site-specific crop yield predictions
Dhahi Al-Shammari, Thomas F.A. Bishop, Chen Wang, Brett M. Whelan and Robert G.V. Bramley
Fusarium crown rot detected in-crop using thermal imagery and quantified reduced water use and yield in bread and durum wheat
Mitch Buster, Mike Sissons, Chris Guppy, Steven Simpfendorfer and Richard Flavel
Integrating APSIM and PROSAIL to improve prediction of crop traits in various situations from hyperspectral data using deep learning
Chen Q, Bangyou Zheng, Tong Chen and Scott Chapman
A framework for sensor-based nitrogen management using nutrient dilution and sufficiency
André Colaço, Glenn Fitzgerald, E M Perry and Bramley R G V
Estimation of plant biophysical parameters using machine learning downscaling
Mario Fajardo, Asher Bender, Jonathan Richetti, Patrick Filippi and Brett Whelan
Detecting causes of spatial variation in crop yield with interpretive machine learning
Patrick Filippi, Brett M. Whelan, Thomas F.A. Bishop
Detection of grain protein in standing wheat crops using hyperspectral sensing
Glenn J Fitzgerald, Cassandra Walker, Sahand Assadzadeh, Eileen Perry, Alex Clancy, and Joe Panozzo
Modelling intercropping highlights importance of resource competition and possibly direct plasticity effects
F Githui, V Jha, T. Thayalakumaran, B.P Christy, and G L O’Leary
Combining data-driven models and mechanistic carbon assimilation models to predict sugarcane yield for improved management
Si Yang Han, P. Filippi, and T F A Bishop
A New Generation of APSIM Dean
Neil Huth Holzworth and Drew Holzworth
Using remote sensing and big data analytics to assess rotation effects on wheat yield across the entire WA wheatbelt.
R Lawes*, G Mata, C Herrmann, J Richetti and A Fletcher
Potential for machine vision of grain crop features for nitrogen assessment
Alison McCarthy, André Colaço, Jonathan Richetti and Craig Baillie
The importance of simulation configuration to crop model development
Jonathan J. Ojeda, Hamish E. Brown, Neil Huth, Dean Holzworth, Rubí Raymundo, Robert F. Zyskowski, Sarah Sinton, Alexandre Michel
BestiaPop - A Python package to automatically generate and visualise gridded climate data for crop model applications
Jonathan J. Ojeda and Diego Perez
Above ground biomass and growth across paddocks from space for characterising soil constraints and N availability
EM Perry, KJ Sheffield, M Fajardo, and SI Akpa
Can we optimise N applications for grains using spatial estimates of N sufficiency?
E M Perry, G J Fitzgerald, A F Colaço, A Clancy, and R G V Bramley
Why ethics will be important for agricultural professionals as the agtech sector develops
S McKinnon, TF Guerin, S Hunter, E Delves, E Read, K Pellosis, and DKY Tan
Towards incorporating remote sensing in crop modelling for precision agriculture purposes
J Richetti, Y Oliver, and Lawes R A
Paddock-scale yield estimation using fused PlanetScope and Sentinel-2 imagery and crop modelling
Yuval Sadeh, Xuan Zhu, David Dunkerley, Jeffrey P. Walker, Yang Chen, and Karine Chenu
In-crop nitrogen detection of cotton – turning passive into active with the Hydraspectra™
Strath Yeo, Tim Weaver, Patrick Filippi, and Daniel K.Y. Tan
Increasing farmer awareness of the impact of agriculture on water quality with the 1622WQTM app
Peter J. Thorburn, Peter Fitch, Maria Vilas, Tony Webster, Martijn Mooij, YiFan Zhang, Jody Biggs, Adam Adham, Bertrand Dungand, Peter Baker, Roger Butler, Simon Fielke, and AaronDavis
A comparison of remote-sensing vegetation indices for assessing within-field variation of wheat yield
F Ulfa, T G Orton, Y P Dang, and N W Menzies
Data-driven modelling for nowcasting of soil water for dryland cropping in Australia
Niranjan Wimalathunge and T.F.A. Bishop