Knowledge Hub
The BANDICOOT® soil probe: Real-time soil profiling for smarter decisions (2025) – Video
Soil CRC Program 2 Leader, Dr Nathan Robinson, sat down with agtech innovator Simon Edwards from the University of Tasmania to chat about the Soil CRC’s BANDICOOT® soil probe — a tool that provides actionable insights into compaction, moisture, salinity and clay content in under 30 seconds, making it practical for routine in-paddock use. Watch the video.
Assessing Soil Indicator Practices: Determinants and Variations Among Australian Farmers – Journal article
Chadha, A., Robinson, N., Ollerenshaw, A. & Larkins, J. (2026). Assessing Soil Indicator Practices: Determinants and Variations Among Australian Farmers. Soil Use and Management 42, 1: e70196. https://doi.org/10.1111/sum.70196. Read article.
Soil CRC Project Spotlight with Prof Keith Pembleton (2024) – Video
We caught up with Soil CRC Project Leader Professor Keith Pembleton (University of Southern Queensland) at the 2024 Soil CRC Participants Conference to find out about his new project, ‘Next generation tools for higher performing soils’ (4.3.007). Watch video.
The QUOLL® electronic nose: Sniffing out soil biological activity – Flyer
The Soil CRC’s QUOLL® electronic nose (e-nose) is an emerging soil sensing technology under development to support faster, cheaper and more accessible assessment of soil biological activity. Read our soiltech flyer to learn more.
The BILBY® wireless communication node: Below-ground node for soil sensor data transmission – Flyer
The Soil CRC’s BILBY® wireless communication node can be installed entirely below ground and sends data from soil moisture sensors to an above-ground receiver. Read our soiltech flyer to learn more.
The BANDICOOT® soil probe: Real-time soil profiling for smarter decisions – Flyer
The Soil CRC’s BANDICOOT® soil probe is an advanced prototype soil sensing system that delivers in-field measurements of key soil physical and chemical properties. Read our soiltech flyer to learn more.
Munsell soil colour prediction from the soil and soil colour book using patching method and deep learning techniques – Journal article
Nodi, S.S., Paul, M., Robinson, N., Wang, L., Rehman, S.U. & Kabir, A. (2025). Munsell soil colour prediction from the soil and soil colour book using patching method and deep learning techniques. Sensors, 25(1), 287. https://doi.org/10.3390/s25010287. Read the article.
Beyond soil moisture probes: improving field scale soil moisture mapping – Journal article
Weir, P. & Dahlhaus, P. (2024). Beyond soil moisture probes: improving field scale soil moisture mapping. Discover Soil, 1, 25. https://doi.org/10.1007/s44378-024-00025-0. Read the article.
Identification of high-performing soil groups in grazing lands using a multivariate analysis method – Journal article
Senanayake, I.P., Yeo, I.-Y., Robinson, N.J., Dahlhaus, P.G. & Hancock, G.R. (2024). Identification of high-performing soil groups in grazing lands using a multivariate analysis method. Soil Security, 16, 100163. https://doi.org/10.1016/j.soisec.2024.100163. Read the article.
Deep learning-based adaptive-downsampling of hyperspectral bands for soil organic carbon estimation – Journal article
Rahman, M., Teng, S.W., Murshed, M., Paul, M. & Brennan, D. (2025). Deep learning-based adaptive-downsampling of hyperspectral bands for soil organic carbon estimation. IEEE Access, 13, 95392-95409. https://doi.org/10.1109/ACCESS.2025.3574697. Read the article.