Projects
Evaluating a remote sensing 'Time Series' approach for monitoring seasonal variability in tree health and yield forecasting in tea tree.
The project aimed to enhance farm decision-making through advanced remote sensing technology. Conducted by ATTIA, Farmacist, and the University of New England’s Applied Agricultural Remote Sensing Centre (AARSC), the project involved two participating growers whose farms were used as case studies.
Key achievements included the digitization of plantation boundaries and the development of time series yield prediction models using Sentinel 2 multispectral images. These models provided valuable insights into tree growth patterns, optimal harvest timing, and the impact of environmental constraints such as droughts and floods. The project demonstrated high accuracy in yield predictions, with one grower achieving 99.4% accuracy.
The project also produced crop vigour maps to support precision agriculture practices and identified the potential for mapping crop variability in tree health. Extension activities included workshops and forums that engaged growers and provided positive feedback on the technology's benefits.
Overall, the project successfully demonstrated the value of remote sensing technology in improving yield forecasting and farm management practices for tea tree growers. The findings highlighted the potential for broader industry adoption and the need for further validation and funding to enhance the technology's application.
Key achievements included the digitization of plantation boundaries and the development of time series yield prediction models using Sentinel 2 multispectral images. These models provided valuable insights into tree growth patterns, optimal harvest timing, and the impact of environmental constraints such as droughts and floods. The project demonstrated high accuracy in yield predictions, with one grower achieving 99.4% accuracy.
The project also produced crop vigour maps to support precision agriculture practices and identified the potential for mapping crop variability in tree health. Extension activities included workshops and forums that engaged growers and provided positive feedback on the technology's benefits.
Overall, the project successfully demonstrated the value of remote sensing technology in improving yield forecasting and farm management practices for tea tree growers. The findings highlighted the potential for broader industry adoption and the need for further validation and funding to enhance the technology's application.
