Harnessing technology to assist in environmental management

Environmental Informatics studies how information can be acquired, processed, modelled and communicated for environmental sciences and management. It is an important multidisciplinary research field covering several national research priorities, including frontier technologies, smart information use, protecting Australia from invasive diseases and pests, sustainable use of Australia’s biodiversity, responding to climate change and variability, and transforming existing industries.


ARC Research Hub for Driving Farming Productivity and Disease Prevention

Early detection of dangerous pests and plant diseases: Current detection and identification of plant diseases and pests mainly rely upon visual identification by human experts. It is labour intensive, highly dependent on the availability and experience of the diagnosticians, and lacking in quality assurance. This program will develop new sensing technologies and automatic biosecurity surveillance systems for early detection of plant diseases and pest incursions before they cause damage to the local industry and region.

Evaluating the environmental impacts of global climate change and industrial activities: This program analyses the changes of vegetation and soils and their relationship with the global climate change and industrial activities. These measurements facilitate anomalies detection, such as those caused by environmental changes, and track their progress through shifts on plant anatomy.

Machine vision for automated harvesting, grading and packing of crops: This program aims to develop image processing and machine vision techniques for the detection, recognition, and analysis of fruits and other crops in a greenhouse environment, in order to assist the development of automated harvesting systems.

Coastal environmental monitoring and management: This program aims to develop an effective and quantitative monitoring technique for Australia’s coastal area. This includes monitoring of shoreline changes and the spectral and spatial changes of seagrass distribution and quality. The monitoring results can be used for reliable assessment of environmental impacts, and assist with making remedial plan and preventative works.

Computer vision for environmental monitoring and surveillance: This program aims at developing novel computer vision and machine learning methods to identify moving objects and fusion of multi-modal image data.

Key publications

Weiping Chen and Yongsheng Gao, “Face Recognition using Ensemble String Matching”, IEEE Transactions on Image Processing, Vol. 22, No. 12, pp. 4798-4808, 2013.

Yuntao Qian, Minchao Ye, and Jun Zhou, “Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 4, pp. 2276-2291, 2013.

Lin Gu, Antonio Robles-Kelly, and Jun Zhou, “Efficient Estimation of Reflectance Parameters from Imaging Spectroscopy”, IEEE Transactions on Image Processing, Vol 22, No. 9, pp. 3548-3663, 2013.

Zhouyu Fu, Antonio Robles-Kelly, Jun Zhou. “MILIS: Multiple Instance Learning with Instance Selection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 5, pp. 958-977, 2011.

Yuntao Qian, Sen Jia, Jun Zhou, Antonio Robles-Kelly. “Hyperspectral Unmixing Via L-1/2 Sparsity-constrained Nonnegative Matrix Factorization”. IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 11, pp. 4282-4297, 2011.

Benson S. Y. Lam, Yongsheng Gao and Alan Liew, “General Retinal Vessel Segmentation Using Regularization-based Multi-concavity Modeling”, IEEE Transactions on Medical Imaging, Vol. 29, No. 7, pp. 1369-1381, 2010.

Baochang Zhang, Yongsheng Gao, Sanqiang Zhao and Jianzhang Liu, “Local Derivative Pattern versus Local Binary Pattern: Face Recognition with High-Order Local Pattern Descriptor”, IEEE Transactions on Image Processing, Vol. 19, No. 2, pp. 533-544, 2010.

Yongsheng Gao and Maylor Leung, “Face Recognition Using Line Edge Map”, IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24, No. 6, pp. 764-779, 2002

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