The intelligence layer that turns sensor data into better harvests — forecasting yield, catching disease early, and tuning the environment to each crop's needs.
Intelligence
A farm that improves with every crop cycle
Annadhara's machine-learning layer brings together climate readings, crop observations, recipe settings, and harvest results to help operators make better decisions.
01
Observe
Connect environmental conditions, plant development, and production records in one operating view.
02
Learn
Compare crop recipes with observed outcomes to identify useful patterns over time.
03
Recommend
Surface practical adjustments for the team to review across climate, lighting, and harvest planning.
Live operating signals
Data that supports each crop decisionClimate, crop, and production data are connected for the people running the farm.
Capabilities
What our models help the farm do
Every model is tied to a practical operating decision, from planning the harvest to checking the health of a single zone.
Harvest outlook
Yield forecasting
Turns live crop-stage, climate, and harvest data into a rolling outlook for volume and timing.
Crop inspection
Disease & stress detection
Combines crop images with site history to surface early signs of stress for a grower to review.
Climate signals
Climate optimisation
Tests likely crop and energy outcomes to propose setpoint changes the team can approve.
Fertigation
Nutrient tuning
Relates EC, pH, and uptake patterns to crop performance so recipes can be adjusted deliberately.
Equipment signals
Anomaly detection
Flags readings that diverge from a zone's usual pattern, directing operators to investigate early.
Crop trials
Recipe evolution
Compares outcomes by cultivar and growth stage to refine validated recipes one cycle at a time.
ObserveInterpretAdjust
One operating viewCrop imagery, environmental signals, and equipment status stay connected in context.
The learning loop
From farm signals to better decisions
Sensors, camera systems, and production records show what is happening in every zone. Models turn those signals into practical recommendations for the team to review.
When a change is made, the result becomes part of the next decision. Over time, every crop cycle builds a clearer picture of how each cultivar responds in this specific farm.
Human-guided by designRecommendations support the operator; operating limits and approval remain with the farm team.
Harvest planning
Harvest, pack, dispatchA shared outlook helps the team prepare each step behind a delivery.
Forecasting
Plan every harvest with better context
Yield forecasts bring crop stage, environmental history, and recent harvest records together into a rolling view of expected volume and timing.
Teams can use that view to line up harvesting, packing, and distribution around likely demand. Because growing conditions can change, the forecast is presented as a range so uncertainty remains visible before commitments are made.
Forecasts are ranges, not promisesConfidence levels help sales, operations, and logistics plan from the same realistic picture.
Governance
Clear data boundaries, from day one
A farm should know where its operational records live, who can access them, and how they can be exported. Those expectations should be set before systems go live.
01
Ownership and portability
The project agreement should set out ownership of production records and the process for exporting them.
02
Access boundaries
Roles, retention periods, and permitted uses are defined so teams know how operational data is handled.
Specific data-processing, security, and retention terms are confirmed in the project agreement.
Controlled accessFarm operations stay connected to clearly defined permissions.
1M+
Data points/day
94%
Forecast accuracy
48h
Early disease warning
18%
Yield gain from tuning
Deployment
Introduce ML without disrupting the farm
We begin with the data and decisions your team already uses, then phase models into daily operations with operators in control.
01
Audit the signals
Review sensors, records, and operating questions to identify dependable starting inputs.
02
Validate the model
Test early forecasts and alerts against past and live crop cycles before they influence workflow.
03
Deploy with the team
Introduce recommendations in advisory mode, with clear ownership and agreed safeguards.
Start with the signals on siteEvery deployment begins with the equipment, crop data, and routines already part of the farm.
Questions
Machine learning FAQs
Practical answers before you connect a first data source or introduce a new model into the farm.
Questions shaped around your farm, crop, and operating team.
No. A smaller farm can benefit when it has recurring crop cycles and reliable operational data. We focus first on the decisions where a forecast or early alert can make a practical difference.
Useful starting points include climate and irrigation logs, crop-stage records, images, and harvest weights. During the audit, we identify what is available and where new data would add value.
Models begin in advisory mode. They explain a recommendation for an operator to review; any later automation is introduced only with agreed safety limits and approval rules.
We compare outputs with historic records and parallel live observations, then monitor performance against agreed measures such as timing, quality, resource use, or forecast accuracy.
Often, yes. The assessment maps available sensors, control interfaces, and export formats so the implementation fits the systems already operating on the farm.
Put your farm's data to work
If your farm already produces sensor data, we can turn it into forecasts and recommendations within weeks.
Harvest outlook
Crop inspection
Climate signals
Fertigation
Equipment signals
Crop trials
