Every capability below explains the problem it addresses, the data it uses and what you get back. Each one carries an honest status, so you know what you can use today.
Status: Available
6 in use today
Status: In development
4 being built and tested
Status: Planned
2 on the roadmap
Status: Available
Smart field monitoring
Soil sensors placed across the field report moisture, temperature and salinity, so each field's condition is visible without a site visit.
The problem it addresses
Conditions vary across and within fields, and manual walks only capture a moment. Changes in the soil can go unnoticed between visits.
Inputs and data sources
Soil moisture, temperature and salinity probes at root depth
Several sensor types placed where they matter in each field
The field gateway that relays every reading
Outputs
A field status view with the latest readings and their trend
History charts per sensor and per field
Data-quality flags when a sensor stops reporting or reads out of range
Workflow
1.Install sensors and register each one to a field and depth.
2.Readings travel through the gateway to your private cloud at regular intervals.
3.Review field status in the web app or on your phone and drill into any sensor's history.
Limitations. Readings describe the spot where each sensor is installed. Placement, calibration and soil type affect how representative a reading is for the whole field.
Fig 2Soil probeHover or tap a sensorwatching the wetting front
Radio head
Sends the probe's readings to the field gateway several times an hour, powered by its own small solar panel.
Wireless upload · solar powered
Sensor at 10 cm
Reads the topsoil, where water arrives first and dries out fastest.
Moisture · temperature · salinity
Sensor at 30 cm
Reads the main root zone, the depth that decides when to irrigate.
Moisture · temperature · salinity
Sensor at 60 cm
Shows whether water goes past the roots, which wastes water and washes nutrients away.
Moisture · temperature · salinity
Drip line
Delivers water at the roots. The probe shows how far each irrigation actually reaches.
Flow per emitter
Illustration with sample readings. After each irrigation pulse the wetting front moves down through the soil, and each sensor reports when the water reaches its depth.
Status: Available
In-field weather station
A weather station in your field broadcasts temperature, humidity, rainfall and wind in real time, so decisions use your field's weather.
The problem it addresses
Regional forecasts and the nearest town's station can differ a lot from the conditions over your crop, especially for rain, frost and wind.
Inputs and data sources
Air temperature and relative humidity
Rain gauge and wind speed and direction
Solar-powered station with its own wireless link
Outputs
Live weather on the field's dashboard
Rainfall and temperature history per field
Inputs for irrigation decisions and risk alerts
Workflow
1.Place the station where it represents the field.
2.It broadcasts readings in real time through the gateway.
Fig 3Weather stationHover or tap an instrumentlive conditions
Anemometer
Its cups spin with the wind; the speed tells you when spraying or drone flights are safe.
Wind speed
Wind vane
Points into the wind, so drift and frost risk can be judged for each field.
Wind direction
Temperature and humidity
The stacked plates shade the sensor from the sun while letting air through, for true air readings.
Air temperature · relative humidity
Rain gauge
Measures the rain that actually fell on the field, so irrigation can be cut back after a shower.
Rainfall
Solar panel
Keeps the station running all year without mains power.
Charging
Logger and radio
Records every reading and sends it in real time through the gateway to your private cloud.
Real-time upload
Illustration with sample readings. Measuring the weather on your own field gives local rainfall, wind and temperature instead of a forecast for the nearest town.
Status: In development
Drones and field robots
Drones and field robots collect images and other data across the field, from wide aerial views to close-ups between the rows.
The problem it addresses
Walking every row is slow, and some problems only show from above or up close. Images collected regularly reveal patterns that spot checks miss.
Inputs and data sources
Aerial images from drones flying over the field
Close-up images and measurements from robots between the rows
Flight and route plans for each field
Outputs
Maps of crop vigour and stressed areas
Images attached to each field and date
Inputs for plant health tracking and disease screening
Workflow
1.Plan a flight or a robot route for the field.
2.The drone or robot collects images and data and uploads them.
3.Review the maps and images and send flagged areas for inspection.
Limitations. Image quality depends on light, weather and flight height. Robot routes need rows wide enough for the machine.
Fig 4Drones and robotsHover or tap the drone, the map or the robotsurveying the field
Survey drone
Status: In development
Flies a planned pattern over the field and photographs it tile by tile with a downward camera.
Aerial images
Crop map
Status: In development
The images are joined into a map of the field coloured by vigour, so weak areas stand out.
Vigour map · stressed areas
Field robot
Status: In development
Drives between the rows on its own, close to the plants, where a drone cannot see under the leaves.
Row-by-row coverage
Robot camera
Status: In development
Takes close-up images of leaves and stems for plant health and disease screening.
Close-up images
Illustration with sample values. Drones and field robots are in development: they collect images that are assembled into maps and close-up records for each field.
Status: In development
AI disease detection
Photos of leaves are analysed for visual symptoms of common diseases, so possible problems can be flagged for an agronomist to check.
The problem it addresses
Many crop diseases are first visible as small leaf symptoms. Spotting them early across large areas is slow, and the first sign is often found too late to act cheaply.
Inputs and data sources
Leaf and canopy photos from phones, drones or field robots
Crop type and growth stage for the field
Recent weather, used to put a detection in context
Outputs
A list of possible symptoms with the image regions that triggered them
A confidence level for each detection
A suggestion to inspect, with the photos attached for the agronomist
Workflow
1.Photograph affected or representative plants, or use drone and robot images.
2.The model highlights possible symptoms and their confidence.
3.An agronomist reviews the result and records the confirmed diagnosis.
Limitations. Detection is a screening aid, not a diagnosis. Supported crops and disease categories are being defined during development and will be listed here when the feature is released. Image quality, lighting and early-stage symptoms all reduce accuracy.
Fig 5Leaf scannerHover or use ← → to move the camera
Flagged regions: possible symptoms for a person to confirmCamera with ring light on a gantryIllustrative results, not model output. AI disease detection is in development: it screens photos for visual symptoms and reports a confidence for each region it flags. Low-confidence results ask for a better photo, and every flag goes to an agronomist to confirm or dismiss.
Status: In development
Plant health and growth
Health indicators and growth records per field, so changes in crop condition are tracked over the season rather than noticed by chance.
The problem it addresses
Without a record over time, it is hard to tell whether a field is behind, on track, or recovering after stress.
Inputs and data sources
Field photos, drone imagery and vegetation indices
Growth-stage observations recorded by the team
Sensor and weather history for the same period
Outputs
Health indicators per field with their trend over the season
Growth-stage timeline compared with planting date
Notes and photos attached to each observation
Workflow
1.Record observations or upload imagery during the season.
2.Hexilia lines them up with sensor and weather history.
3.Compare fields and spot those that diverge from the rest.
Limitations. Indicators summarise the data supplied; they do not replace field inspection.
Fig 6Plant health and growthHover or tap a partfollowing the season
Multispectral camera
Status: In development
Looks down at the crop and records images in several light bands, from which vegetation indices are worked out.
Image bands · capture time · position along the bed
Growth stages
Status: In development
Each observation is placed on a growth-stage timeline compared with the planting date, so a field running behind stands out.
Growth stage (BBCH) · days since planting · notes and photos
Plant under stress
Status: In development
Yellowing or drooping shows up as a lower index than its neighbours; it is flagged for someone to look at in the field.
Health index · change since last visit · flag
Health index
Status: In development
One indicator per plant or field, kept over the season so its trend can be compared with the rest.
Index per plant · season trend · comparison between fields
Illustration with sample values. Plant health and growth tracking is in development: it will line up photos, imagery and growth-stage observations with sensor and weather history for each field; it does not replace field inspection.
Status: In development
Predictive alerts
Alerts when readings or forecasts cross thresholds you set, delivered early enough to plan a response.
The problem it addresses
A risk such as dry soil, frost or disease-favourable weather is easier to manage when it is seen coming, not after the damage.
Inputs and data sources
Live sensor and weather-station readings and forecasts
Thresholds you set per field and crop (for example a soil-moisture refill point)
Risk rules for conditions such as frost or prolonged leaf wetness
Outputs
An alert with the reading or forecast that triggered it
Time until the threshold is expected to be crossed, when a forecast is involved
Delivery by email or in the mobile app, with a summary per field
Workflow
1.Set thresholds and who should be notified for each field.
2.Hexilia evaluates new readings and forecasts as they arrive.
3.Acknowledge the alert and record what was done.
Limitations. Forecast-based alerts inherit the uncertainty of the weather forecast they use. Alerts are prompts to check, not instructions.
Fig 7Predictive alertsHover or tap a partwatching the trend
Soil probe
Status: In development
Reports soil moisture from the field at regular intervals, so the trend can be followed reading by reading.
Soil moisture · reading time
Weather forecast
Status: In development
Expected rain and temperature for the coming days are taken into account, so a dry spell is seen before it shows in the soil.
Expected rain · temperature · forecast window
Trend and threshold
Status: In development
The moisture trend is compared with the refill point you set for this field; the dashed ring marks that threshold.
Current level · threshold · direction of the trend
Alert on your phone
Status: In development
When the threshold is expected to be crossed, an alert reaches the mobile app or your email with the reading that triggered it.
Triggering reading · time until the threshold · field
Illustration with sample values. Predictive alerts are in development: they will compare readings and forecasts with thresholds you set and warn you before they are crossed.
Status: Available
Remote irrigation control
Monitor and control pumps and valves in real time from the mobile app, guided by soil and weather readings.
The problem it addresses
Fixed schedules water whether or not the soil needs it, and changing them means a trip to the field. That wastes water and energy in wet spells and can under-water during hot, dry ones.
Inputs and data sources
Soil moisture at root-zone depths
Rainfall from the weather station and forecasts
Pumps and valves connected to the platform
Outputs
Start, stop and schedule irrigation from the mobile app
Live status of pumps and valves
A record of what was applied and how the soil responded
Workflow
1.Connect pumps and valves and set the moisture range for each field.
2.Follow readings and start, stop or schedule irrigation from your phone.
3.Check the soil's response and adjust the schedule.
Limitations. Remote control requires compatible pumps and valves and a working connection at the field. Watering recommendations depend on sensor placement and field settings.
Fig 8Field stationHover or use ← → to move through 72 hours
Soil-moisture probe: what the logger readsDrip lines and solenoid valve: what it switchesWater reaching the soilIllustrative model, not field data. A loam root zone loses about 5.8 mm of water a day to the crop and the air. When the probe reads below 22 % volumetric water content (the refill point), the logger opens the valve; 10 drippers at 2 L/h wet the soil until the probe reads 28.5 %. Thresholds are set per field.
Status: Planned
Fertilization recommendations
Nutrient recommendations that show the soil analyses, crop needs and field history they are based on.
The problem it addresses
Applying the right amount at the right time depends on several sources of information that usually sit in different places.
Inputs and data sources
Soil laboratory analyses
Crop, target yield and growth stage
Previous applications and field history
Outputs
A suggested application with timing and amount
The rationale, listing the data used
A record of the applications actually made
Workflow
1.Upload soil analyses and record previous applications.
2.Review the suggested plan and its rationale.
3.Record what was applied.
Limitations. Planned. Recommendations will be advisory and should be checked against local regulations and agronomic advice.
Fig 9Fertilization recommendationsHover or tap a partdosing a sample plan
Soil sampling
Status: Planned
Soil samples go to a laboratory, and you upload the analysis so the recommendation starts from what is actually in your soil.
Laboratory analysis · pH · N, P, K
Suggested plan
Status: Planned
Shows the suggested amount and timing for each nutrient, with the analyses, crop needs and field history it is based on.
Amount · timing · rationale
Nutrient tanks
Status: Planned
Separate tanks hold the nitrogen, phosphorus and potassium solutions, so each one can be applied in its own amount.
N · P · K
Dosing pumps
Status: Planned
Each pump adds its nutrient at its own rate; here the rates follow a sample plan.
Rate per nutrient · L/h
Mixing and drip line
Status: Planned
The doses are mixed into the irrigation water and reach the roots through the drip line, and what was applied is recorded for the field.
Mixed solution · conductivity · application record
Illustration with sample values of a planned feature: fertilization recommendations will be advisory, based on soil analyses, crop needs and field history, and should be checked against local regulations and agronomic advice.
Status: Available
Planting and field management
One record per field for crops, planting dates and activities, so the season's history is in one place.
The problem it addresses
Field records spread across notebooks, spreadsheets and messages are hard to compare and easy to lose.
Inputs and data sources
Field boundaries and areas
Crops, varieties and planting dates
Activities such as sowing, spraying, irrigation and scouting
Fig 10Planting and field managementHover or tap a partfollowing the season
Plots
Each plot is recorded with its boundary, area, current crop and stage, from tilled soil to harvest.
Boundary · area · crop and variety · stage
Plot markers
The letter on each stake is the plot's name in the register, so what is written in the app matches what you see in the field.
Plot name · location
Activities
Tillage, sowing, irrigation, spraying and scouting are logged against the plot where they happened with their date.
Activity · date · plot
Field register
The register lists every plot with its current crop and status, and the timeline shows the season's history for each one, ready to export.
Register · timeline · exports
Illustration with sample values. Field management is available today: each plot keeps its crop, planting date and activities in one record, with a timeline for the season.
Status: Available
Harvest, yield and BI reports
Business-intelligence reports on planted area, harvests and yields, compared across fields and seasons.
The problem it addresses
Without consistent records and reports, it is hard to tell which fields, varieties or practices performed best.
Inputs and data sources
Planted area per field
Harvest quantities and dates
Sensor, weather and activity history
Outputs
Yield per hectare by field, crop and season
Reports comparing fields and seasons
Exports to share with partners and advisors
Workflow
1.Record harvest quantities as they come in.
2.Hexilia's big-data and business-intelligence algorithms compile the reports.
3.Compare results and use them in next season's plan.
Fig 12AI recommendationsHover or tap a partfrom data to a decision
Field data
Status: Planned
Soil probes, the weather station and field cameras feed the readings each suggestion is built on.
Soil · weather · images
AI model
Status: Planned
Runs in your private cloud and weighs current readings against field records and past outcomes.
Readings · field records · past outcomes
Suggestion
Status: Planned
A suggested action and its timing, with the evidence behind it and an indication of how certain it is.
Action · timing · evidence · certainty
Your approval
Status: Planned
Nothing happens until you accept, change or dismiss the suggestion; the outcome is recorded to improve later ones.
Accept · change · dismiss
Illustration with sample values. AI recommendations are planned: suggestions will come with their evidence and how certain they are, and a person decides.
Status: Available
Private cloud data storage
Your field data is stored in a private cloud and private storage, under your control.
The problem it addresses
Farm data is valuable and sensitive. Growers need to know where it is kept, who can see it and how to get it back.
Inputs and data sources
Readings from sensors and the weather station
Images from drones, robots and phones
Field records, reports and settings
Outputs
Private storage per customer, with access you control
Backups and exports of your data
A single source for reports, alerts and the mobile app
Workflow
1.Data is transmitted from the field gateway to your private cloud.
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