Controlling Crops to Meet Demand
Harvest with Confidence
TensoAI uses proprietary AI models to extrapolate data from Controlled Environment Software that powers your growing environment. TensoAI works with all major CEA software, such as Ridder, Bluelab and PlantOS for our models to easily integrate with your dataset. We are always growing our library of collaborators to help facilitate onboarding.
Correlating your plant data allows us to predict the quantity and quality metrics, such as Harvest, Acid and Sugar levels, that are important to drive decisions with supply chain challenges.
For some spaces, we use a Federated Learning Technology that aids in augmenting the accuracy of our models without risking data security. We utilize privacy-preserving tools to ensure that all data-ownership rules are adhered to and your data is always safe.
We understand the challenges of growing crops. Our goal is to provide insight and knowledge about your crops, with the least amount of resources from you.
01
Historical Analysis
Much of the heavy lifting comes from setting up the framework to perform analytics. Data Collection is an intrigrual step in providing analytics for future reports. Data such as Temperature, Humidity, Vpd, 3rd-party sources and Historical Data is gathered in this step.
A Historical Analysis is performed to understand multiple parameters that effect your unique crop growth.
02
Weekly Insight Reports
Being part of the growing season is how we understand the complexities of fruit production, and the parameters important in the packaging process. We extend the original report to include such information as, Precision Irrigation Schedule, Predictive Insights, Yield Insights, and Quality Metrics.
03
Quality and Quantity Forecasting
Utilizing historical data, we train our AI models to deliver predictive information on Quality and Quantity metrics before harvest time. Our AI uses a proprietary model that injests data established in the previous steps. You can access information such as, Harvest Time Prediction, and Quality/Quantity forecasting.
Unlock your Data Today
Predict Harvest Metrics
Harvesting is a stressful time, and having mission-critical data helps in making time-dependent decisions for your crop. Predicting Quality and Quantity metrics such as Harvest, Sugar and Acid levels that drive revenue will greatly allow farms to navigate upcoming supply chain issues.
Predictive data leads to predictive scheduling, which are seen to have 5% increase in Productivity and 8% increase in Employee Retention.
Plant Modelling
Our Plant Modelling module allows growers to get insights on controlling their plant. By controlling environmental factors, such as, Temperature, Humidity, Solar Radiation, and Wavelength, certain outcomes in Quality and Quantiy can be achieved. Predicting the outcomes can drastically reduce costs in developing new crop strategies.
We focus our insights around 3 outcomes: Energy Usage, Quality and Quantity metrics.
Irrigation Scheduling
Water availability is a vital metric in creating algorithms around crop growth. Irrigation Scheduling according to predictive weather, Soil capacity, quantity and quality metrics is important in future harvesting predictions.
Our Irrigation module can be used in 2 modes: Minimal water-use (reduce crop loss), Maximize Harvest (Quality/Quantity metrics).
No Expensive Cost
Here at TensoAI, we know all hardware is expensive. We focus on delivering value on data already accessible via Weather Stations, Sattelite, Goverments and Public Organizations. If more data is needed for your farm, we recommend multiple hardware suppliers for a low-cost solution to data capture.
Camera Images
A feedback loop plays an important role in our model's understanding of the complexities of plant growth. Feedback loops such as crop images can greatly improve our model accuracy, and reduce the amount of time needed for development.
Images taken from above the canopy can help quantify plant growth during the vegetative stage, while images under the canopy aid in the flowering/fruiting stage.
Many low-cost options are available, and our AI models can process any images. Get in touch to discuss!
Integrating Systems
Here at TensoAI, we believe in integrating existing systems already in place. We work with major software platforms to bring added value to the customer with the least hassle possible.
Our Federated Learning Technology (Seed Network) allows integration into major databases through privacy-perserving tools. Data Privacy is at the center of this technology, and has allowed for numerous models to be built upon already.
01. Data Connection
Creating access to the controlled environment software, and other 3rd party sensors with our edge device is essential in deploying our Models. These read-only connection serves as a bridge to train, test and deploy prediction models for further insights.
TensoAI uses Federated Learning to ensure privacy preserving AI keeping data ownership at the forefront of all of our development.
02. Historical Data Analysis
Every growing environment is different. From multiple crop varieties to variable growing techniques and different environments, data plays an important role in understanding how your crops respond. TensoAI is dedicated to understanding what makes your growing unique and validates it through your data.
In this step, we analyze your historical data to pinpoint specific markers that affect your unique crop quality and quantity metrics.
03. Algorithm Insights
Algorithms play an important role in understanding potential future harvests. Utilizing the specific markers identified in the previous step, TensoAI simulates different growing metrics and aims in providing insight to growers that would save time and resources. Currently, our algorithms focus on irrigation and fertigation scheduling with 2 modes: minimal water use and maximize harvest Quality or Quantity.
04. Predictive Models
Utilizing much of the knowledge gained in the previous steps, TensoAI puts it into action here. Our proprietary AI model predicts the Quantity and Quality of your crop at time of harvest. We do this by running our models daily to injest new data, without the need for any grower intervention. Our models use Federated Learning to ensure all data pipelines are secure, and no data is ever copied out. Daily Predicted values are easily integrated into other data visualization platforms for the grower and owner.
Both Testing and Training of data must be first accomplished before any predicted values are given. We strive to achieve above 85% accuracy, and utilize a Federated Learning platform to achieve this at a consistent rate.
Family Growing
- Federated Learning AI
- Weekly Knowledge Team Reports
- 1 Year Historical Yield data needed
Greenhouse or Indoor Farm
- Proprietary Cloud AI
- Dedicated Knowledge Team Meetings
- 3 Year Historical Yield data needed
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Data digitization and AI models has never been closer