Insights

    Mobile Pest & Disease Model Data Analytics Platform

    Origin Group plc is a focused Agri-Services Group. The main focus of the Group is to be the leading provider of value added services, technologies and strategic inputs that support the delivery of sustainable and profitable food production solutions for primary producers. Agrii is a leading agronomy company, offering independent and innovative advice to arable, fruit and vegetable growers. Agrii harnesses the power of skilled agronomists and the best intelligence to deliver unrivalled expertise and support for sustainable and profitable farming systems in Europe.

     

    As a leading provider of agronomy services, technology and strategic advice, Agrii combines excellence and innovation with the latest research and development to ensure our customers can meet today’s farming challenges with knowledge and confidence. The challenge presented to Version 1 was to produce a means for Origin agronomists in the field to be able to retrieve the most up to date risk status for a given weather station against certain pests and diseases.

     

    Outcome/Impact

     

    The previous process was for the Origin scientists to manually retrieve the weather data, cleanse and prepare it and then using Microsoft Excel produce tables and charts to populate PDF’s for email distribution. The whole process could take some months to complete. However the new process automatically retrieves the data, generates the model and outputs to both apps daily. Therefore, the users always have the most recent data to hand when required

     

    Solution

     

    Version 1 designed and built two mobile tablet applications for iOS iPads using the Ionic framework with Angular JS on top of Apache Cordova. Both applications incorporate Mapbox Maps with points plotted using a geoSpacial library called LeafletJS. Each point on the map represents a Weather Station and the icon changes based on current risk of being effected by certain pests and diseases. The user is presented with a summary of the station details and current risk status and further detail showing details of the risk status based on crop sowing and spraying dates. The details page on the other application shows a timeline based on emergence and egg laying. Both applications use a local SQLite database refreshed daily through Azure Mobile Services pointing at an Azure SQL Database. The database is populated nightly by a two-stage process. The first stage is an Azure Webjob which runs on a schedule and calls out to the Weather Station API’s, iterates though each weather station and retrieves the required air and soil temperatures down to 15 minute intervals and place that data in a staging set of database tables. The second stage is to use this data to generate  the analytics models to output the data to both the applications.

     

    View the full case study here

     

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