Automated Monitoring

Feature publications

Rod Connolly's publications relating to automated monitoring. 
See the range of automation services available through the FishID Team.
Estimating enhanced fish production on restored shellfish reefs using automated data collection from underwater videos
Connolly et al. 2024 Journal of Applied Ecology
​Aquatic ecologists routinely quantify fish abundance to assess reef restoration success, yet estimating long-term fish production remains challenging. We identified, counted and sized thousands of juvenile fish using a massive number of camera deployments. The automation of videos enabled reliable, scalable estimates of biomass enhancement, for the entire fish community including commercially important species.
Out of the shadows: automatic fish detection from acoustic cameras
Connolly et al. 2023 Aquatic Ecology
Aquatic ecologists increasingly use computer vision to monitor underwater animals, but poor visibility or night-time pose challenges. We demonstrate FishID software reliably detects and counts fish from acoustic camera imagery – especially using acoustic shadows – substantially reducing monitoring time, effort and cost.
Automating the analysis of fish abundance using object detection: optimizing animal ecology with deep learning 
Ditria et al. 2020 Frontiers in Marine Science
Aquatic ecologists routinely count animals for conservation and management. Advances in underwater cameras and drones have made data collection safer and easier. We show how FishID automation identifies and counts animals far more efficiently, accurately and reproducibly than even expert scientists. The saving in time and costs allow substantially more field replication and thus more robust findings. 
Artificial intelligence meets citizen science to supercharge ecological monitoring
McClure et al. 2020 Patterns
Citizen science and artificial intelligence (AI) are often used in isolation for ecological monitoring, but their strategic integration offers significant opportunities. Here, we highlight how combining citizen science and AI can improve conservation outcomes, outlining key benefits and challenges.

Automated monitoring publications

2025
  • Bruce T... Connolly RM, Herrera C, et al (2025) Large-scale and long-term wildlife research and monitoring using camera traps: a continental synthesis. Biological Reviews 100:530-555​ PDF

2024
  • Connolly RM, Herrera C, Rasmussen J, Buelow CA, Sievers M, Jinks KI, Brown CJ, Lopez-Marcano S, Sherman CDH, Martínez-Baena F, Martin B, Baring R, Reeves SE (2024) Estimating enhanced fish production on restored shellfish reefs using automated data collection from underwater videos. Journal of Applied Ecology 61:633-646 ​PDF
  • Herrera C, Connolly RM, Rasmussen JA, McNamara G, Murray TP, Lopez-Marcano S, Moore M, Campbell MD, Alvarez F (2024)  Drone insights: unveiling beach usage through AI powered people counting. Drones 8:579 ​​PDF

​2023
  • Connolly RM, Jinks KI, Shand A, Taylor MD, Gaston TF, Becker A, Jinks EL (2023) Out of the shadows: automatic fish detection from acoustic cameras. Aquatic Ecology 57:833-844 PDF
  • Kitchingman ME, Sievers M, Lopez-Marcano S, Connolly RM (2023) Fish use of restored mangroves matches that in natural mangroves regardless of forest age. Restoration Ecology 31:13806 PDF

2022
  • Connolly RM, Jinks KI, Herrera C, Lopez-Marcano S (2022) Fish surveys on the move: adapting automated fish detection and classification frameworks for videos on a remotely operated vehicle in shallow marine waters. Frontiers in Marine Science 9:918504 PDF
  • Ditria EM, Buelow CA, Gonzalez-Rivero M, Connolly RM (2022) Artificial intelligence and automated monitoring for conservation of marine ecosystems: a perspective. Frontiers in Marine Science 9:918104 PDF
2021
  • Connolly RM, Fairclough DV, Jinks EL, Ditria EM, Jackson G, Lopez-Marcano S, Olds AD, Jinks KI (2021) Improved accuracy for automated counting of fish in baited underwater videos for stock assessment. Frontiers in Marine Science 8:658135 ​PDF
  • Lopez-Marcano S, Jinks EL, Buelow CA, Brown CJ, Wang D, Kusy B, Ditria EM, Connolly RM (2021) Automatic detection of fish and tracking of movement for ecology. Ecology and Evolution 11:8254-8263 PDF
  • Pearson RM, Collier CJ, Brown CJ, Rasheed MA, Bourner J, Turschwell MP, Sievers M, Connolly RM (2021) Remote estimation of aquatic light environments using machine learning: a new management tool for submerged aquatic vegetation. Science of the Total Environment 782:146886 PDF
  • Ditria EM, Jinks EL, Connolly RM (2021) Automating the analysis of fish grazing behaviour from videos using image classification and optical flow. Animal Behaviour 177:31-37 ​PDF
  • Provost EJ, Coleman MA, Butcher PA, Colefax A, Schlacher TA, Bishop MJ, Connolly RM, Gilby BL, Henderson CJ, Jones A, Lastra M, Maslo B, Olds AD, Kelaher BP (2021) Quantifying human use of sandy shores with aerial remote sensing technology: the sky is not the limit. Ocean and Coastal Management 211:105750 PDF
  • ​Kimball ME, Connolly RM, Alford SB, Colombano DD, James WR, Kenworthy MD, Norris GS, Ollerhead J, Ramsden S, Rehage JS, Sparks EL, Waltham NJ, Worthington TA, Taylor MD (2021) Novel applications of technology for advancing tidal marsh ecology. Estuaries and Coasts 44:1568-1578 PDF
  • Ditria EM, Connolly RM, Jinks EL, Lopez-Marcano S (2021) Annotated video footage for automated identification and counting of fish in unconstrained seagrass habitats. Frontiers in Marine Science 8:629485 PDF
  • ​Lopez-Marcano S, Brown CJ, Sievers M, Connolly RM (2021) The slow rise of technology: computer vision techniques in fish population connectivity. Aquatic Conservation: Marine and Freshwater Ecosystems 31:210-217 PDF
  • Mandal R, Becken S, Connolly RM, Stantic B (2021) Residual attention network vs real attention on aesthetic assessment. In: Hong TP, Wojtkiewics K, Chawuthai R, Sitek P (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS, Springer, Thailand, pp 310-320 doi:10.1007/978-981-16-1685-3_26 PDF
  • ​​Becken S, Friedl H, Stantic B, Connolly RM, Chen J (2021) Climate crisis and flying: social media analysis traces the rise of ‘Flightshame’. Journal of Sustainable Tourism 29:1450-1469​ PDF

2020​
  • Ditria EM, Sievers M, Lopez-Marcano S, Jinks EL, Connolly RM (2020) Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats. Environmental Monitoring and Assessment 192:698 PDF
  • McClure EC, Sievers M, Brown CJ, Buelow CA, Ditria EM, Hayes MA, Pearson RM, Tulloch VJD, Unsworth RKF, Connolly RM (2020) Artificial intelligence meets citizen science to supercharge ecological monitoring. Patterns 1:100109 PDF
  • Ditria EM, Lopez-Marcano S, Sievers M, Jinks EL, Brown CJ, Connolly RM (2020) Automating the analysis of fish abundance using object detection: optimizing animal ecology with deep learning. Frontiers in Marine Science 7:429 PDF
  • Scott N, Le D, Becken S, Connolly RM (2020) Measuring perceived beauty of the Great Barrier Reef using eye-tracking technology. Current Issues in Tourism 23:2492-2502 PDF​
  • Graham, A, Marouchos, A, Martini, A, Fischer A, Seet, B-C, Guihen D, Williams G, Symonds, J, Ross D, Soutar, J, Heasman, K, Lea, MA, Leary M, Sikka P, King P, Cossu R, Adams S, Arachillage SJ, Albert S, Cahoon S, Bird S, Connolly RM, Edwards S, Bannister R (2020) Autonomous marine systems at offshore aquaculture and energy sites. Final Report Blue Economy CRC.
2019
  • Becken S, Connolly RM, Chen J, Stantic B (2019) A hybrid is born: integrating collective sensing, citizen science and professional monitoring of the environment. Ecological Informatics 52:35-45 PDF​

2018
  • Mandal R, Connolly RM, Schlacher TA, Stantic B (2018) Assessing fish abundance from underwater video using deep neural networks. IEEE IJCNN 2018:1-6. DOI: 10.1109/IJCNN.2018.8489482 PDF

2017
  • Becken S, Stantic B, Chen J, Alaei A, Connolly RM (2017) Monitoring the environment and human sentiment on the Great Barrier Reef: assessing the potential of collective sensing. Journal of Environmental Monitoring 203:87-97 PDF​