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2026 Journal Article Corrigendum to “Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: Effects of sample size, dimensionality, and transferability” [Smart Agricultural Technology, Volume 13 (2026), Article 101936]Ashourloo, Davoud, Brider, Jason, Zhao, Yan, Jiang, Ruizhu, Zhang, Miao, Chapman, Scott, Hammer, Graeme and Potgieter, Andries B. (2026). Corrigendum to “Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: Effects of sample size, dimensionality, and transferability” [Smart Agricultural Technology, Volume 13 (2026), Article 101936]. Smart Agricultural Technology, 14 101983, 1-1. doi: 10.1016/j.atech.2026.101983 |
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2026 Journal Article CropVision: A Multiscale EO-AI-Climate-Biophysical modelling framework for predicting crop performance in Australian wheat and sorghum systemsPotgieter, Andries B., Brider, Jason, Ashourloo, Davoud, Schepen, Andrew, Zhao, Yan, Jiang, Ruizhu, Chapman, Scott, Zhang, Miao, Wu, Bingfang, Russell, Kerry and Hammer, Graeme (2026). CropVision: A Multiscale EO-AI-Climate-Biophysical modelling framework for predicting crop performance in Australian wheat and sorghum systems. in silico Plants, 8 (2) diag018, 1-18. doi: 10.1093/insilicoplants/diag018 |
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2026 Journal Article Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: effects of sample size, dimensionality, and transferabilityAshourloo, Davoud, Brader, Jason, Zhao, Yan, Jiang, Ruizhu, Zhang, Miao, Chapman, Scott, Hammer, Graeme and Potgieter, Andries B (2026). Machine learning approaches for wheat yield prediction integrating biophysical modeling and remote sensing: effects of sample size, dimensionality, and transferability. Smart Agricultural Technology, 13 101936, 1-13. doi: 10.1016/j.atech.2026.101936 |
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2026 Journal Article High resolution LiDAR and environmental variables enable fine scale predictions of hollow-bearing tree abundance across a complex temperate forest landscapeJiang, Ruizhu, Nitschke, Craig R., Trouvé, Raphael, Cripps, Jemma K., Durkin, Louise K., Lumsden, Lindy F., Nelson, Jenny L., Scroggie, Michael P., Robinson, Andrew P. and Baker, Patrick J. (2026). High resolution LiDAR and environmental variables enable fine scale predictions of hollow-bearing tree abundance across a complex temperate forest landscape. Forest Ecology and Management, 603 123440, 1-12. doi: 10.1016/j.foreco.2025.123440 |
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2025 Journal Article Characterising wheat and barley growth and phenology using multispectral remote sensing for site-specific precision agricultureZhao, Yan, Jiang, Ruizhu, Brider, Jason, Chapman, Scott and Potgieter, Andries (2025). Characterising wheat and barley growth and phenology using multispectral remote sensing for site-specific precision agriculture. In Silico Plants, 7 (2) diaf013. doi: 10.1093/insilicoplants/diaf013 |
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2024 Journal Article Seasonal dynamics of fallow and cropping lands in the broadacre cropping region of AustraliaXie, Zunyi, Zhao, Yan, Jiang, Ruizhu, Zhang, Miao, Hammer, Graeme, Chapman, Scott, Brider, Jason and Potgieter, Andries B. (2024). Seasonal dynamics of fallow and cropping lands in the broadacre cropping region of Australia. Remote Sensing of Environment, 305 114070, 1-14. doi: 10.1016/j.rse.2024.114070 |
