Big data harvesting tool could unlock smarter farming

The Phenospex platform, which uses 24-hour laser scanning to collect crop information, is an example

The Phenospex platform, which uses 24-hour laser scanning to collect crop information, is an example of one of the sources of data which can be gathered together using the new CropSight tool developed at the Norwich Research Park. Picture: John Innes Centre - Credit: John Innes Centre

A 'big data' harvesting tool has been developed at the Norwich Research Park which can unlock insights from the vast cloud of information being gathered from smart farming technology.

Although more and more information is constantly being gathered by modern farm machines and scientific sensors, big data captured by diverse technologies known collectively as the Internet of Things (IoT) is difficult to calibrate, annotate and aggregate.

This presents a major challenge for plant scientists trying to understand the dynamics between crop performance, genotypes and environmental factors, and for agronomists and farmers monitoring crops in fluctuating agricultural conditions.

The new system developed by researchers from the Earlham Institute, John Innes Centre, and University of East Anglia provides near real-time environmental and crop growth monitoring.

CropSight is a 'scalable and open-source information management system' that can be used to organise and collate vast datasets of crop performance and microclimate information.

It can be accessed both locally in the field through smart devices and via computers back at the laboratory or office. The system has already been applied to field experiments of bread wheat pre-breeding and speed breeding.

Dr Simon Griffiths of the John Innes Centre said it could have a significant impact on scalable plant phenotyping – the observable properties produced by the interaction of its genetic traits and the environment it grows in – 'leading to more efficient gene discovery, crop breeding, and ultimately end user benefits.'

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Key features of the system include automated data collection and information management, monitoring of biological experiments through network sensing devices, and daily synchronising of data and crop growth images.

Project leader Dr Ji Zhou of the Earlham Institute added: 'Through connecting environmental readings with crop growth datasets using IoT-based technologies, we have demonstrated how IoT can be applied in crop research and agricultural practices.

'Additionally, with the development of national IoT infrastructure, CropSight can be expanded to even larger scale and multiple locations, which can then help agricultural practitioners make prompt decisions across a country's arable land.'