FluroSat uses the power of agricultural science, remote sensing and machine learning
to deliver early, accurate and actionable information on crop needs and performance.
Make better-informed decisions that drive profitability and deliver measurable outcomes: improve the yields by simultaneously cutting fertiliser and water waste.

+ 10-25% yield

- 30% fertiliser

- 25% water

Testimonials

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    Introducing FluroSense
    The analytics engine of agriculture.
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    FluroSense engine
    Here is a brief explanation:

    1. We collect the data from partner platforms and APIs.

    2. Our models predict key crop parameters (growth stage, biomass, nutrient/water needs, yield potential) and use remote sensing data to adjust & tailor predictions.

    3. And Voila! FluroSense produces a stream of actionable agronomic insights: the What, the Where and the How of Precision Ag!
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    Weather data
    By accessing public and private source (IoT) of localised weather data we are able to precisely quantify the amount of rain fueling the crop growth, assess the risks of frost or disease development, as well as use this data to run our crop models.
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    Remote sensing data
    Remote sensing data from infrared cameras on drones, planes and satellites together with radar maps is used to both infer the spatial variability of the fields on the farm as well as to spot anomalies from the predicted crop growth such as abnormal growth patterns in fields, operational mistakes and sudden events like hail damage.
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    Spatial variability
    With plant/soil tests and IoT/weather information available only in a few points on the farm, it is necessary to combine it with the spatial variability of the soil and crop cover to accurately forecast nutrient and water requirements. This spatial variability is regularly estimated using various sources of remote sensing information.
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    Soil information
    Any farming system is intrinsically defined by its soil types, their quality, texture, water and nutrient holding capacity. The limited and often coarse soil data available on a country-scale is taken by our models as a starting point. Further modelling, fusion of remote sensing data and yield maps refines the soil maps to make your analytics more accurate.
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    Farm management
    Farm management records provide key piece of information about the timing, intensity and type of operations performed on the farm. These operations are human interventions to the agricultural system followed by our crop models, and get validated using the remote sensing data.
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    Scientific models
    We are using best-in-class crop models and proprietary algorithms that allow us to scale the science from a trial plot to a commercial farm. We are partnering with world-known universities, RDCs and CSIRO to deliver those models to you in a user-friendly and actionable form in which they can learn to operate on your farm.
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    Machine learning layers
    Each farm, and each field is unique, with its own cropping history, subsoil constraints and response to water and nutrient. To localise our analysis to every field we use machine learning models, which tailor the parameters (influence of soil, water, weather etc) to every field and further - to each sub-field management zone.
FluroSense ®
Remote sensing-based
early intervention crop
health monitoring platform
ProductionWise
Scientifically-validated climatics and
yield modelling and
decision support platform
CottonMap
Industry collaborative
Cotton Field Awareness Map
to minimize spray drift damage
iPad_FluroSense_disease_black
Want to see FluroSense analytics on your farm?
Get a customised demo and
a FREE 30-Day trial!
Request demo

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