Distribution of troublesome weeds at a district level is largely unknown. The lack of information on distribution of troublesome weeds means that:
- threats can be prioritised incorrectly. Some emerging weeds can be overlooked, and no control strategies developed.
- spread of emerging weeds over time and space is unknown, leading to a reactive approach instead of proactive district strategies that could prevent quick dispersal.
Aerial imagery of cane farms from drone for the purpose of weed scouting and spot spraying currently need manual interpretation by an agronomist to be converted into weed maps and spray maps. This task is time consuming and limit the adoption of the spot spray technology using drones.
Aim: This project aims to facilitate the development and early deployment of a platform that maps weeds from drone imagery and generates spray maps; and a spot-and-spray system with a clear commercialisation pathway for sugarcane. It also aims to explore diagnostic technology using satellite imagery to identify the target weeds and map their distribution at a paddock, farm and district levels.
Meet the Postdoctoral Research Fellow on this project
Mohammad Jahanbakht
Dr Mohammad Jahanbakht, James Cook University, Townsville. is an innovative software engineer and researcher with a diverse skill set spanning code development, numerical modeling, web programming, cloud technologies, data science, and machine intelligence. With a PhD in machine learning and data science, he has contributed to numerous interdisciplinary projects in environmental studies, maritime research, and biodiversity monitoring. His expertise extends to AI-powered estimation and forecasting of marine environmental parameters, edge processing of underwater image and video data, and monitoring biodiversity in farms, wetlands, and maritime ecosystems using IP cameras and aerial imagery. Mohammad has also played a key role in AI-driven land studies for drone surveys and remote sensing applications. His proficiency in big data analytics, AWS cloud services, embedded systems, and electronic design engineering underscores his commitment to technological innovation and scientific advancement.
This project, or a similar one, will include a PhD student who is commencing in coming months.
Also on this project are:
Professor Mostafa Rahimi Azghadi, James Cook University.
Emilie Fillols, Sugar Research Australia (SRA).
Dr Alex Olsen and Jerome Leray, InFarm.
Approach: Images of six target weeds in sugarcane paddocks in North and Far North Queensland will be captured by InFarm using their drones and camera sensors from January 2025 to April 2027. The project will also work with AI models and techniques for accurate detection of the target weed species using InFarm drone and potentially using satellite imagery.
The project will implement the AI models and techniques into the InFarm processing pipeline and capture and process pre-trial imagery using small drones to guide project data collection decisions. Methods include:
- Satellite imagery
- Drone sensors
- AI for weed identification: Train AI detection models (e.g., object detection, instance segmentation) using state-of-the-art techniques such as Convolutional Neural Networks or multi-modal vision-language models
- Weed map generation
Want to know more? Contact plantbiosecurity@anu.edu.au.