Analysis and Prediction of Tree Nut Yield using Remote Sensing, Weather and Orchard Information

Project Information

This project will utilise a large dataset of historical yields from commercial orchards of four nut crops, walnut, pecan, macadamia and almond. It will use ground-based data such as carbohydrate samples, tree variety and density, and visual inspection. The interaction of season and variety on yields will be investigated, including consideration of irregular bearing (the tendency of trees to have high and low yielding years alternately).

These factors will be brought together with spatial-temporal datasets from satellite and weather to develop models that are predictive of both the spatial and inter-annual variability in nut tree yield (and relevant quality metrics). The yield potential and limiters of yield will be analysed, including the impact of the most important predictors of yield. These will be compared with expert predictions based on visual inspection and early sentinel tree harvest data.

The project will involve large-scale remote sensing image analysis, curating big datasets, statistical analysis, developing machine learning models with explainability, and designing visualisations that enable practical interpretation of results. As such, ideally, the student will have experience with agronomy, data science, machine learning and remote sensing. Familiarity with platforms such as Google Earth Engine, programming in Python and use of packages such as Scikit-Learn and TensorFlow would be an advantage. The project will involve field work including gathering data samples from orchards, so practical experience in agricultural settings is desirable.

The successful candidate will work alongside Associate Professor James Brinkhoff and Professor Andrew Robson at the Applied Agricultural Remote Sensing Centre within the School of Science and Technology. They will have the opportunity to collaborate closely with an industry partner, to deliver impactful outputs of practical use.

Scholarship Information

This scholarship is open to both Domestic and International applicants. The successful candidate will be awarded a $40,000 pa stipend (tax free) provided by an industry partner. Additional research funds will be available and tuition fees will be covered.  If an international candidate is successful they will also receive overseas health cover and tuition fees (for the student only, funded by the University to New England for the duration of 3 years and 6 months).

How to Apply

To apply for this scholarship, applicants must complete and submit a candidature and scholarship application. All required supporting documentation as mentioned in the application form including the following:

• Include in your application a cover letter and a CV detailing the reasons for your interest in the project and relevant experience.

For more information on submitting a candidature application please see our web page on how to apply/enrol for candidature.

Enquiries: James Brinkhoff - James.Brinkhoff@une.edu.au