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

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 maximise 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.

Project date

30 May 2021-30 May 2022
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Principal investigator

Alice Moore

Project funded by

Multiple industries
Alternative protein Aquaculture Cereal grains Cross industry Essential oils Fruits Game Honey bees Nuts Other rural industries Pasture, fodder & feed Poultry Pulse grains

AgriFutures Australia

AgriFutures Australia proudly focuses on building a rich future for Australian agriculture. We live and work in the regions and …
  • Location

    Australia

  • Organisation type

    Research funding body

Industries

Sustainabilities

Technology areas

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