Automation in Agriculture

A platform that applies AI to satellite images to monitor field variability, predict yields, and plan field operations. 


Monitoring large areas of crops can take time and effort. Traditional crop monitoring methods are often impractical for large-scale farming, as they tend to take time and effort. Physical monitoring is labor-intensive and may not always provide farmers with the detailed data they need on time.

Crop health can vary significantly across different parts of a single field due to various factors such as soil quality, water distribution, and pest infestations. Identifying these variations is crucial as it helps in targeted interventions.

Accurate yield predictions are necessary for effective farm management and logistics, but they still need to be made due to the complex interplay of environmental factors and crop growth.

Resource optimization is essential for sustainable farming. Precise application of water, fertilizers, and pesticides is necessary to avoid over-application, which can increase costs and cause environmental harm, and under-application, which can reduce crop yields.


Scalable Monitoring: AI technology makes use of satellite imagery and computer vision algorithms to enable monitoring of vast agricultural areas efficiently. This provides farmers with a comprehensive view of their fields without physically inspecting every area.

Health and Growth Analysis: The platform applies computer vision algorithms to detect crop growth patterns and health anomalies. Analyzing color and other visual indicators can identify areas that may need attention due to pests, diseases, or nutrient deficiencies.

Predictive Analytics: AI models analyze historical and real-time data to predict yields. Understanding the relationship between crop health indicators and final results, the platform can provide farmers with more accurate forecasts.

Precision Farming: The platform helps create variable rate application (VRA) maps that guide the precise application of inputs like seeds, fertilizers, and pesticides. This is based on the variability within a field, thus optimizing resource usage and reducing waste.

Data-Driven Decisions: By turning visual data into actionable insights, farmers can make informed decisions about irrigation, harvesting times, and resource allocation.

Accessibility: The platform also focuses on making these advanced technologies accessible to farmers, which helps democratize high-tech solutions for the agricultural sector, regardless of farm size.

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Automation in Agriculture

A platform that applies AI to satellite images to monitor field variability, predict yields, and plan field operations.

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Dr. Larysa Visengeriyeva
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