Lian Xu CITS3200 Project - AI in Agriculture

A web-based interface for the early detection of crop disease and stress

Accurate and timely monitoring of crop stress and disease (e.g., salt, frost and yellow rust), plays a key role in precision agriculture, especially to enhance sustainable crop production and food security. In recent years, a number of deep learning methods using remote sensing (e.g., multispectral and hyperspectral data) data for early crop disease and stress detection have been developed and implemented by researchers at UWA.

In order to be used by farmers and researchers, a web-based platform needs to be developed to:

allow users to upload remote sensing samples and get the classification results (e.g., stress or healthy);
allow users to upload remote sensing images and get the distribution maps of the crop stress and disease across their fields;
allow researchers to benchmark new algorithms using our provided datasets by uploading their results.

In this web-based platform, users first choose a type of disease (or stress) detection and input the data, then an already implemented AI method (i.e., Python code using Pytorch, Tensorflow or Keras) to automatically output crop stress and disease classification results or distribution maps online. The platform needs to be connected to a server where the detection task by AI can be done very quickly. Students will implement the Python codes for online AI-based early detection of crop disease and stress and benchmarking.

A confidentiality agreement will need to be signed.

Client

Contact: Lian Xu
Phone: 6488 2708
Email: [email protected]
Preferred contact: Email
Location: UWA Crawley Campus

IP Exploitation Model

The IP exploitation model requested by the Client is: An agreement to joint exploitation of any IP that is created

Department of Computer Science & Software Engineering
The University of Western Australia
Last modified: 23 July 2021
Modified By: Michael Wise