Michael Jones

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Professor of Agricultural Biotechnology, Foundation Director, WA State Agricultural Biotechnology Centre, Foundation Director, Centre for Crop and Food Innovation

Murdoch University

Australia

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About

Professor Michael Jones is a Professor of Agricultural Biotechnology at Murdoch University and serves as the Foundation Director of both the WA State Agricultural Biotechnology Centre and the Centre for Crop and Food Innovation in Australia. His research interests focus on agricultural biotechnology, including CRISPR gene editing, deep learning applications in agriculture, and pest and disease detection in crops. He has published recent articles on topics such as payload optimization for image transmission over LoRa, nematode species infecting cereal crops, and advanced methodologies for improving plant resilience and productivity.

Recent Grants

Grant: Close

Introducing, testing and breeding low saponin quinoa genotypes in Australia

Open Date: 2020-07-01

Close Date: 2023-06-01

Grant: Close

Development of non-GM frost-tolerant wheat for WA growers

Open Date: 2020-05-01

Close Date: 2021-06-01

Grant: Close

Building Capacity for Small Exporters to Exploit ‘new breeding technologies’

Open Date: 2020-01-01

Close Date: 2023-01-01

Grant: Close

Genome editing to improve the dietary quality of potatoes

Open Date: 2015-01-01

Close Date: 2017-12-31

Grant: Close

The roles viruses play in the decline of terrestrial orchids in Australia’s hotspot of global biodiversity

Open Date: 2011-01-01

Close Date: 2014-12-31

Michael Jones

Research Interests

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About

Recent Grants

Articles (21)

Deep Learning based Payload Optimization for Image Transmission over LoRa with HARQ

An Update on Root Lesion Nematode Species Infecting Cereal Crops in the Southwest of Western Australia

Engineering plants using diverse CRISPR-associated proteins and deregulation of genome-edited crops

Object-level benchmark for deep learning-based detection and classification of weed species

Object-level benchmark for deep learning-based detection and classification of weed species

Editorial: Frontiers in global regulatory landscape of CRISPR-edited plants

LoRa-based outdoor localization and tracking using unsupervised symbolization

Multi-task learning model for agricultural pest detection from crop-plant imagery: A Bayesian approach

Plant disease recognition in a low data scenario using few-shot learning

Multiplex CRISPR-Cas9 Gene-Editing Can Deliver Potato Cultivars with Reduced Browning and Acrylamide

Multiplex CRISPR-Cas9 Gene-Editing Can Deliver Potato Cultivars with Reduced Browning and Acrylamide

A survey of image-based computational learning techniques for frost detection in plants

Small RNA Analyses of a Ceratobasidium Isolate Infected with Three Endornaviruses

Spillover of a Tobamovirus from the Australian Indigenous Flora to Invasive Weeds

Enabling Trade in Gene-Edited Produce in Asia and Australasia: The Developing Regulatory Landscape and Future Perspectives

Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network

Weed recognition using deep learning techniques on class-imbalanced imagery

In‐plant activation of root‐specific expression of a cytotoxic gene disrupts the development of the root‐knot nematode, Meloidogyne javanica

A survey of deep learning techniques for weed detection from images

A survey of deep learning techniques for weed detection from images

Functional characterization of target genes associated with insecticide resistance of the green peach aphid, Myzus persicae

Collaborators (6)

Dean Diepeveen

Stephen Milroy

Ferdous Sohel

A S M Mahmudul Hasan

John Fosu-Nyarko

Stephen Wylie

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