Research Thesis Topic
Deep Learning-Based Precision Agriculture to Model Coffee Yield with Soil Fertility Properties
Coffee is the second most traded commodity in African, American and Asian nations. Coffee yield is significantly dependent on environmental, climatic and soil fertility conditions and especially, on soil fertility constituents whose correct proportions impact the productivity. As it is not known what soil fertility conditions the best for optimum yield are, this remain challenging task for agricultural modellers.
Biophysical models require a good knowledge of the processes in soil-plant-atmosphere continuum that affects crop yield. Owing to the complexity and parameterisation of fertility properties for crop simulation models, statistical analytics built in an intelligent model can provide a new a predictive approach for yield optimisation through carefully selected soil fertility properties. The soil organic matter containing most of the soil’s reserve of nitrogen and large portions of phosphorus and sulphur plays a vital role for nutrient availability and absorption by plants but when the process of selecting and reducing variables is automated, predictors chosen might not highlight the biological assumptions.
The purpose of this project is to investigate robust data-intelligent methods [i.e., deep learning based convolutional neural networks, long short-term memory networks and random forest] to model the relationships between soil fertility parameters and coffee yield for smallholder farms. The project will determine the best performing model and relevant organic matter, available potassium and sulphur, etc as the predictors of coffee yield. The project will suit students with background in agriculture, environmental science, mathematics, computing and climate or atmospheric physics. Students will learn about neural networks, machine learning in agriculture, expert systems with smart tools and evaluation of models in real-time forecasting crop yield.
The research project is strongly centred on agribusiness and sustainable agriculture.
This project is suitable for PhD, Research or Coursework Masters Thesis. It provides opportunity to publish in high quality Q1 journals. The research student will be part of the Advanced Data Analytics Research Group under Prof Ravinesh Deo.
For more details see https://eportfolio.usq.edu.au/view/view.php?id=116719
- Institute for Agriculture and the Environment
- International Centre for Applied Climate Sciences
- School of Agricultural and Environmental Sciences
- Agricultural Biotechnology
- Agriculture, Land and Farm Management
- Artificial Intelligence and Image Processing
- Atmospheric Sciences
- Ecological Applications
- Environmental Science and Management
- Other Earth Sciences
- Other Environmental Sciences
- Doctor of Philosophy (DPHD)
- Doctor of Philosophy (DPHD)
- Master of Research (MRES)
- Master of Research (MRES)
Please review the admission requirements for the academic program associated with this Thesis Topic
The project suits students with a background in agriculture, environmental science, mathematics, computing and climate/atmospheric physics. Students will learn about neural network models, machine learning in agriculture, expert systems with smart tools and evaluation of models in real-time forecasting crop yield.