Wireless sensor network deployment for water use efficiency in irrigation J McCulloch, P McCarthy, SM Guru, W Peng, D Hugo, A Terhorst Proceedings of the workshop on Real-world wireless sensor networks, 46-50, 2008 | 105 | 2008 |
Aquaculture sentinels: smart-farming with biosensor equipped stock SJ Andrewartha, NG Elliott, JW McCulloch, PB Frappell J. Aquac. Res. Dev 7, 1-4, 2015 | 52 | 2015 |
Dissolved oxygen prediction in prawn ponds from a group of one step predictors A Rahman, J Dabrowski, J McCulloch Information Processing in Agriculture 7 (2), 307-317, 2020 | 45 | 2020 |
Future agriculture farm management using augmented reality M Xi, M Adcock, J McCulloch 2018 IEEE Workshop on Augmented and Virtual Realities for Good (VAR4Good), 1-3, 2018 | 35 | 2018 |
State space models for forecasting water quality variables: an application in aquaculture prawn farming JJ Dabrowski, A Rahman, A George, S Arnold, J McCulloch Proceedings of the 24th ACM SIGKDD international conference on Knowledge …, 2018 | 32 | 2018 |
Predicting shellfish farm closures using time series classification for aquaculture decision support MS Shahriar, A Rahman, J McCulloch Computers and electronics in agriculture 102, 85-97, 2014 | 24 | 2014 |
Benthic habitat mapping with autonomous underwater vehicles A Davie, K Hartmann, G Timms, M de Groot, J McCulloch OCEANS 2008, 1-9, 2008 | 24 | 2008 |
The tasmanian marine analysis network (TasMAN) GP Timms, JW McCulloch, P McCarthy, B Howell, PA De Souza, ... OCEANS 2009-EUROPE, 1-6, 2009 | 20 | 2009 |
Machine learning approach to investigate the influence of water quality on aquatic livestock in freshwater ponds M Rana, A Rahman, J Dabrowski, S Arnold, J McCulloch, B Pais Biosystems Engineering 208, 164-175, 2021 | 19 | 2021 |
An integrated framework of sensing, machine learning, and augmented reality for aquaculture prawn farm management A Rahman, M Xi, JJ Dabrowski, J McCulloch, S Arnold, M Rana, A George, ... Aquacultural Engineering 95, 102192, 2021 | 17 | 2021 |
An algorithm for the automatic analysis of signals from an oyster heart rate sensor AD Hellicar, A Rahman, DV Smith, G Smith, J McCulloch, S Andrewartha, ... IEEE Sensors Journal 15 (8), 4480-4487, 2015 | 12 | 2015 |
Ensemble feature ranking for shellfish farm closure cause identification A Rahman, C D'Este, J McCulloch Proceedings of workshop on machine learning for sensory data analysis, 13-18, 2013 | 12 | 2013 |
An end-to-end augmented reality solution to support aquaculture farmers with data collection, storage, and analysis M Xi, M Adcock, J Mcculloch Proceedings of the 17th International Conference on Virtual-Reality …, 2019 | 11 | 2019 |
Avoiding marine vehicles with passive acoustics C D'Este, B Seton, J McCulloch, D Smith, C Sharman Journal of Field Robotics 32 (1), 152-166, 2015 | 6 | 2015 |
A neural network and som based approach to analyse periodic signals: application to oyster heart-rate data AD Hellicar, A Rahman, D Smith, G Smith, J McCulloch 2014 International Joint Conference on Neural Networks (IJCNN), 2211-2217, 2014 | 5 | 2014 |
Analysis of heavy metals in marine sediment using a portable X-ray fluorescence spectrometer onboard an Autonomous Underwater Vehicle J Breen, P de Souza, G Timms, J McCulloch, R Ollington 2012 Oceans-Yeosu, 1-5, 2012 | 5 | 2012 |
Smart headset, computer vision and machine learning for efficient prawn farm management M Xi, A Rahman, C Nguyen, S Arnold, J McCulloch Aquacultural Engineering 102, 102339, 2023 | 4 | 2023 |
Time-series prediction of shellfish farm closure: A comparison of alternatives A Rahman, MS Shahriar, C D’Este, G Smith, J McCulloch, G Timms Information Processing in Agriculture 1 (1), 42-50, 2014 | 4 | 2014 |
A low-cost, long-life, drifting sensor for environmental monitoring of rivers and estuaries C D'Este, M Barnes, C Sharman, J McCulloch 2012 Oceans-Yeosu, 1-6, 2012 | 4 | 2012 |
Development of a cross discipline, experiential based, flexible delivery unit J Sargison, F Bullen, J McCulloch International Conference on Engineering Education, 1-4, 2002 | 4 | 2002 |