SciForce Agriculture


That Works For Future

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Our main use cases

Species Breeding

Artificial intelligence can identify patterns in data that may not be apparent to humans. Therefore, it can more quickly and effectively identify desired traits for breeding. We are building machine learning models predicting which genes would bring the most beneficial trait to the plant. You will have only the best species geared up to nutrient and water use, staying disease-resistant, and adapting to climate change.

Field conditions management

Machine learning algorithms can predict optimal humidity, temperature, and moisture for efficient soil and water management. Thus allowing you to maximize your crop yields, while minimizing expenses.

Crop management: weed detection

Computer vision-powered solutions can identify unwanted plants, allowing farmers to apply herbicides to selected areas rather than the whole field. This can drastically decrease the harmful effects of herbicides.

IoT-based farming cycle

The core of the IoT is the data — and more data. To optimize the farming process, IoT devices installed on a farm should collect and process data in a repeated cycle that enables farmers to quickly react to emerging issues and changes in ambient conditions. We have extensive experience in optimization and automation of IoT devices data processes.

Precision Farming

Precision agriculture demonstrates the potential of AI, machine learning, and other high-tech tools.GPS, GIS, UAVs, Satellite Remote Sensing, and many other technologies are bringing innovations to old practices of farming and transforming the industry. Correct data orchestration and model tuning for specific farming tasks are key to the success of such innovative practices. Our team of data engineers and DevOps can help you with all your precision farming needs.

Anomaly detection

Anomaly detection could be integrated into the decision process to improve harvesting efficiency and influence on plant health. Using ground-based and aerial drones and possessing imaging collected, agribusinesses can quickly react to insect propagation, physical and chemical soil properties, the presence of pathogens and soil diseases, and adjust to topography variability.

Soil management

We're using AI-based models to predict the impact of soil management practices on crop yields. For example, AI-powered sensors can forecast soil moisture levels, soil fertility, and other important soil characteristics. This can help farmers make informed decisions about which practices will be most effective for their land.

Predictive analytics

Yield prediction is critical: knowledge of when it is best to harvest the crop and what crops to grow to satisfy market demands is crucial for any agro-business. Our team of experts develop custom ML models to analyze all the factors to increase yield prediction accuracy for your use case.