SpraySaver - Full Automated Broad-spectrum crop Pathogen Spore Detection using DNA and LFD

"Today, the majority of farmers spray fungicides prophylactically on crops to minimise risk and insure against disease ingress. Most farmers, or their consultants, spend hours inspecting crops but can't easily predict what incubating (invisible) infection is already in the crop or what may start to develop as a result of increasing pathogen presence in the environment. Weather-based disease forecasting methods have been introduced to predict when to spray crops but often have unreliable results, especially against sporadic diseases.

The market opportunity for **SpraySaver** is to transform today's '_**spray-and-pray**_' practices by offering a more reliable and precise scientific method of determining when to spray -- using locally gathered disease pathogen data and risk prediction/decision support models to assess crop disease risk. The added value to farmers is a big reduction in crop spray costs, safeguarded or better crop yields/productivity and greater effectiveness when sprays are applied.

**SpraySaver** is the world's first automated field analyser system specifically designed for early detection of crop disease pathogens within crop growing environments. One analyser can monitor a wide geographic area of around 100 Ha (dependant on local environmental conditions) and can be configured to detect multiple crop disease pathogens. Each in-field analyser transmits 4G mobile data for analysis in the cloud. Local pathogen data is analysed alongside local weather data within a disease risk model to determine risks of crop disease infection.

Pathogens that will be detected include _Sclerotinia_, which affects oilseed rape and carrots, yellow and brown rust of wheat, _Fusarium_ _graminearum_ of cereals, potato late blight, beet rust, and onion down mildew, thereby covering sporadic diseases of a wide range of crops typically grown near to each other under crop rotation. Analysis outputs at a local, regional or even national level can be viewed on multiple display devices with automatic alerts set at predetermined levels. This ambitious project will develop a better DNA quantification method, develop new assays for specific diseases of onion and wheat, and integrate detection with infection-condition models and economic models to make recomendations for spray regimes. The system will ultimately eliminate today's '_**spray-and-pray**_' practices by offering a more reliable scientific method of determining when to spray -- using locally gathered disease pathogen data and risk prediction/decision support models to assess crop disease risk. Integration of the system as a network will add robustness and reliability to the decision-making process."