Five ways data and diagnostics capabilities...

... work in crop and farm management

The major stages in the data and diagnostics pipeline are; data capture, sampling and analysis, machine learning, management and automation. Each of these form part of the chain of custody and actions that capture field observations and turn them into actionable insights for the grower. Data specialist Derek Scuffell explains each stage

1 Data Capture

Observations, in increasingly higher volumes, will be captured as data, by sensors that use remote monitoring by networks of robots, UAVs, phones and satellites. Observations will continue to be made and recorded by experts such as the farmer and the agronomist. These observation data are all recorded electronically and passed back to data centres in the cloud via ubiquitous wireless networks.

2 Sampling and Assay

To augment data captured by sensors and remote systems, physical samples of plants, pests, and soil may also be taken and passed to laboratories for assay. Notoriously this can be a rate-determining step in the data and diagnostics pipeline. To address this concern CHAP has developed a Lab to Field Capability of mobile crop science labs, that bring a diverse range of assay capabilities to the field, enabling rapid on-site assays and timely data collection.

3 Data Analysis

All captured data are accessed by data science teams to determine the current state of the crop ecosystem. Rich predictive algorithms can be applied to quantify the risk of pest and disease, the current health of the crop, and the likely yield and quality outcomes. These aggregated and predicted values then update digital representations of the farm, field and plant. Results are stored on the farmer’s own farm management systems and used to update a digital-twin of the field, such as the representations in Agrimetrics Field Explorer.

4 Machine Learning (ML)

ML is a branch of artificial intelligence that provides the ability for machines or computer programmes to learn and improve on what they “know”. It has great promise in agriculture where diagnostics systems will generate huge volumes of data from which ML can learn and offer insight to the grower. Supervised ML is where the computer program is trained to recognise a thing of interest. It could perhaps be images, taken from in-field sensors, of Cabbage Flea Beetle present and not present in the crop. Over time it will learn to recognise when Cabbage Flea Beetle is present. This approach is used by Hummingbird technologies to create insights about crop health, disease risk in the field.

5 Management Systems and Automation

Decision support systems and tools based on analytics and machine learning can be used by growers to monitor crops and make decisions, such as whether to apply crop protection chemistry. CropMonitor Pro and the BASF Water Stewardship Tool are both examples of this type of decision support tool. This comprehensive and accurate data about the state of the farm and diagnostics can be the basis for more extensive integrated farm management decisions. For example, the Syngenta and RSPB Bird Environmental Stewardship Tool (BEST) helps farmers make better land stewardship choices that will enhance farmland bird populations. It exploits detailed data about the sub-field location and the known effectiveness of stewardship options for boosting bird populations.

Derek Scuffell, data strategist at Knowmatics, is an expert in applying data to benefit the agrifood sector. He is also a key consultant working with CHAP on Digital Agronomy, Redesigning the Crop Protection Toolbox, and Controlled Environment Agriculture.


CHAP’s Data Diagnostics Solution has a range of capabilities, looking to provide advances in remote sensing and diagnostic technology and provide a wealth of data that can be used to predict crop growth and more importantly the pest and disease pressure. For more information, email us using the contact form below

Please note, the opinions expressed in this article are the author’s own and do not necessarily reflect the views or opinions of CHAP. 

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