Anand Koirala

Anand Koirala
School of Health, Medical and Applied Sciences
Field of Research:
Information and Computing Sciences; Engineering; Technology
Professor Kerry Walsh; Professor William Guo; Dr Zhenglin Wang; Dr Cheryl McCarthy
Student Type:
Doctor of Philosophy

Contact Details

Research Details

Thesis Name

Precision Agriculture: Exploration of machine learning approaches for assessing mango crop quantity and quality

Thesis Abstract

Knowledge of crop load and timing of maturation can guide agronomic treatments, labour resource management and support market planning. In current practice, yield estimation is typically performed based on weather data, previous yield history and manual counting of flowers and fruits on the trees. Manual yield estimation is time consuming and labour intensive, so there is a need for a precise and accurate machine vision system that can replace and extend human vision in estimation of flowering and fruit count and size. This research will consider various sensor technologies, image analysis techniques and decision support systems for accessing mango crop quality and quantity.

Why my research is important/Impacts

The overall aim of this direction of work is to provide management tools to the grower to assist in timing and resourcing of harvest and reduction of labour. This research activity will contribute to the development of a decision support system based on machine vision technologies (effective in terms of cost and complexity) that can be adopted by mango growers for accurate and precise estimates of mango crop yield.


Regional University Network (RUN) Precision Agriculture Scholarship