Pests and Diseases Monitoring and Forecasting Algorithms, Technologies, and Applications

Our very own Professor Langxiu Han has been asked to serve as a guest Editor for the Special Issue of Frontiers in Remote Sensing

Plant pests and diseases cause an annual average of 40% global food failure addressed by FAO and more than 100 billion dollars in loss of forest and grass resources. Scientific prevention and control of pests and diseases in agriculture, forestry and grass is important to ensure food security, ecological and environmental safety, etc. At present, the accuracy of individual identification of agricultural, forestry and grass pests and diseases are low, making it difficult to achieve accurate outpost warning, occurrence environment monitoring and multiple pest and disease type differentiation, resulting in the inability to achieve early detection and control of pests and diseases. With the rapid development of remote sensing, big data, and artificial intelligence technologies, information technology has been widely used in agriculture, forestry and grass pest and disease precision monitoring and forecasting. Digital precision monitoring and forecasting of major pests and diseases have become a major development trend in the agriculture, forestry, and grass industry.

If you are interested in submitting a paper or interested in more information please visit the website at the link below:

Abstract Submission Deadline 30 April 2023
Manuscript Submission Deadline 15 August 2023

https://www.frontiersin.org/research-topics/52691/pests-and-diseases-monitoring-and-forecasting-algorithms-technologies-and-applications

The Applications and Trends of Remote Sensing and Artificial Intelligence in Agriculture

Our very own Professor Langxiu Han has been asked to serve as a guest Editor for the Special Issue of Frontiers in Remote Sensing

The development of Remote Sensing (RS) and Artificial Intelligence (AI) techniques has drawn increasing interest and started a novel area of research applications in crop monitoring. RS imageries provide wide-source and real-time data for growth condition monitoring, pests, and diseases detection, etc., which are followed by the usage of AI for effective and efficient data mining to obtain new, insightful information to support practical guidance and further applications. Combining the advantages of RS and AI, automatic and fast processing and modeling of crop growth applications can be achieved. To build an RS-AI system for solving complex problems, researchers will comprehensively complete tasks from data acquisition to model construction, and achievements in any tasks will promote the development of RS-AI in crop monitoring.

If you are interested in submitting a paper or interested in more information please visit the website at the link below:

Abstract Submission Deadline 11 March 2023
Manuscript Submission Deadline 09 July 2023

https://www.frontiersin.org/research-topics/52125/the-applications-and-trends-of-remote-sensing-and-artificial-intelligence-in-agriculture#overview

Digital Agriculture: Applications with Remote Sensing and AI

Professor Liangxiu Han has been invited to present our research,

A Big Data Driven AI Enabled Approach to Precision Agriculture

at a one day workshop hosted at the Space Park Leicester

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The University of Leicester is hosting a BBSRC Impact Accelerator funded workshop on Digital Agriculture: Applications with Remote Sensing and AI.

There have been rapid advances in the observations of agricultural land, much of it provided by satellite data. This results in more data that needs to be processed efficiently, more information that needs to be analysed and more interpretation needed to understand the impact of agricultural practices on food security and, for example, the contribution to net-zero policies. Today, we hear a lot about the uses of AI technologies such as deep learning models and we seek better understand of how they can be used for in agricultural applications

Global Challenge of Crop Disease Diagnosis Results

Our global competition has come to an end and we would like to thank all those involved in making it such a success

11 teams competed and the final leaderboard shows the incredible success

https://www.kaggle.com/competitions/beyond-visible-spectrum/leaderboard

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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

Frontiers in Plant Science Special Edition

We would like to cordially invite you to contribute an article to the Special Issue of
Frontiers in Plant Science (IF: 6.627)

Special Issue: Intelligent Monitoring of Agricultural Pests and Diseases

For more information on the issue, please visit the website at:
https://lnkd.in/e-Ru67RT

Papers may be submitted from now until 28 February 2023 and will be published on an ongoing basis

Enter Our Global Challenge of Crop Disease Diagnosis!

We would like to welcome all participients worldwide to enter our Global Challenge of Crop Disease Diagnosis!

https://www.kaggle.com/competitions/beyond-visible-spectrum

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As part of the upcoming conference,

The third conference of Monitoring, Early Warning, and Scientific Management of Vegetation Pests and Diseases

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

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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,

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

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”

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To Enter, or for more information please see the following link:

https://www.kaggle.com/competitions/beyond-visible-spectrum

Please help us provide innovative solutions to global food

Continuation of Data Collection

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

A wish for a happy and healthy new year

This year has been challenging for everyone

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

Many Thanks

Second Crop Pest and Diseases (P&D) Remote Sensing Conference

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.

Covid Restrictions

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