A Cloud-based, Mobile-enabled, Data-Driven Approach for Automatic Crop Disease Detection
UK Partners: Prof. Liangxiu HAN, Manchester Metropolitan University (Project Lead)
Chinese Partners: Prof. Bingfang WU and Dr. Sheng CHANG, Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Science (CAS)
The yields of crop plants are deleteriously affected by various diseases. It is estimated that almost 25% of worldwide crops are lost to diseases with the potential to cause serious economic, social and ecological problems, particularly in developing countries like China. Accurate diagnosis and quantification of crop diseases are crucial for precision crop management, allowing targeted interventions. The main aim of this project was to develop a novel automated big-data driven approach for crop disease detection to provide a rapidly reliable service to minimise the risk of crop loss from diseases in China.
In collaboration with the CropWatch team in RADI/CAS in China, this project has developed an automated, cloud-based and mobile enabled software tool for accurately and quickly diagnosing crop diseases by exploiting image data from remote sensing and smartphones/digital cameras in real fields. Currently, our system can identify three types of crop diseases including Leaf rust, Yellow rust and Septoria. The system has been implemented as standard web services and can be easily adapted to other types of disease detection and plugged into other systems for augmented automation function.
This project offers great potential for sustainable crop disease monitoring and diagnosis, thus improving the quality and the yields of crops in China. The intended end-users include Chinese government, farmers and industry (e.g. disease and pest control companies). The geographical area to be served is Hebei province, with the initial focus on wheat diseases in China.