Evaluating a remote sensing ‘Time Series’ approach for monitoring seasonal variability in tree health and yield forecasting in tea tree.


  • Project code: PRO-015305

  • Project stage: Closed

  • Project start date: Sunday, May 30, 2021

  • Project completion date: Monday, May 30, 2022

  • National Priority: AFI - AgriFood Innovation


Compared to other Australian tree crop industries, the Tea tree industry has had minimal exposure to technologies that have proven capabilities to support increased productivity and improve input efficiencies. This project will demonstrate the benefits of ‘freely available’ remotely sensed imagery for monitoring seasonal crop condition across years and across planting areas. This information will allow tea tree growers to benchmark tree performance and therefore be able to identify and respond to constraints such as water, nutrition, pest, disease, etc. Understanding variation in production may also support harvest segregation to maximize oil quality. Additionally, the project will match historic crop reflectance information (measured by past satellite imagery) to corresponding past seasonal yields to determine whether these ‘time series’ models, demonstrated as accurate for other tree crop industries, can also be used to predict seasonal biomass production and therefore yield in tea tree. Being able to predict yield earlier in the growing season supports better logistical planning around forward selling, processing, transport and labour requirements.
To achieve the specified outcomes, ATTIA will partner with the UNE’s AARSC. Action learning workshops as well as one to one industry extension will be held to demonstrate the technologies to growers.


AgriFood Innovation

Research Organisation