The top 5 winning methods will be published in Remote Sensing Special issue of “Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases”
Thank you for helping us provide innovative solutions to secure global food production
we have published an exciting UAV hyper-spectral dataset that we hope will spur innovation in developing machine learning solutions to crop disease diagnosis, providing global impact in ensuring food production
Anyone can enter the challenge, which is focused on the accurate diagnosis of Yellow Rust Disease
The benchmark model is based on our published paper,
We are looking forward to your submissions and the top 5 winning methods will be published in Remote Sensing Special issue of “Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases”
To Enter, or for more information please see the following link:
Our team in Malaysia has been working to advance our project dataset by collecting more data as often as possible.
We are working to create a high quality dataset of rice disease in drone images. The more data our partners can collect the more we hope to advance our disease detection machine learning algorithms.
This process is slow and difficult because of the limitations of agriculture, having to collect the data in time with the growing crop, identifying areas of disease (which farmers are actively trying to avoid) and travelling to the rural areas to perform the collection.
By improving out system we aim to increase the rate of collection and provide a valuable service to smallholders
We just wanted to say thank you too all our partners around the world for their continued work and input to this project despite the difficulties in their respective countries.
All your efforts are very much appreciated
Hopefully this year will be better and healthier for all
Professor Liangxiu Han gave an invited talk at the Second Crop Pest and Diseases (P&D) Remote Sensing Conference on 3oth August
Precision Agriculture: A Big Data Driven, AI enabled Approach to Crop Disease Diagnosis
The conference was focused on realising smart plant production by utilising remote sensing technologies in the areas of monitoring, early warning and control of pests and diseases of crops, forests and grasslands.
Topis of discourse covered current theories, methods, models, and systems, as well as research results and discussions of developmental trends.
We would like to once again offer our support for our partners in Malaysia in these difficiult times.
As we are now trying to evaluate our system in fields around the country, the efforts of our experts on the ground are currently restricted.
Obviously the health and safety of our team and their countryman is the highest priority so all precautions are being taken and regulations followed to ensure this
Professor Liangxiu Han was a Keynote Speaker at CropWatch-ICP training programme funded through United Nations, Economic and Social Commission for Asia and the Pacific (ESCAP), Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS)
“Precision Agriculture: A Big Data Driven, AI enabled Approach to Crop Disease Diagnosis”
Professor Han participated in a 3 – Day International Workshop discussing our work on Precision Agriculture.
Organised by the National Institute of Disaster Management (NIDM) in India, the workshop covered many aspects of driving development in symbiosis with the environment, including the role of technology in agriculture.
Professors Han’s presentation, ‘Precision Agriculture: Big data driven, AI enabled Approaches to Crop disease Diagnosis and Monitoring’ was a key contribution to this discussion
While the world is dealing with the pandemic in these troubling times we would like to express our support and appreciation to those that are working to safeguard the vulnerable and our consolation to everyone effected.
Due to the disruptions in international travel we have had to delay our scheduled project demonstration and are continuing to work towards improving our solution during these troubling times.
AgriONE 