Managing large agricultural areas efficiently requires continuous observation and precise data analysis. With DXAS’s Field Supervision, farmers gain access to high-resolution satellite imagery (NDVI), providing a clear visual assessment of crop health and stress levels. Additionally, our system includes a Degree Day Map (DDM), which helps track temperature-based plant growth to optimise farming schedules.

To further enhance monitoring, Field Supervision also includes soil sensors and flowmeters, providing real-time data on soil moisture, temperature, and irrigation efficiency. These tools ensure precision in water management, helping farmers optimise fertigation and prevent resource waste.

Water and fertiliser are among the most valuable resources in agriculture, but inefficient use can lead to high costs, waste, and lower yields. DXAS’s AI Smart Pulse takes the guesswork out of irrigation and fertilisation by automating these processes with real-time AI recommendations.

Using data from satellite images, soil moisture sensors, and weather forecasts, the system determines precisely when and how much water and fertiliser should be applied to each crop. The recommendations are seamlessly sent to Remote Pulse Irrigation Manager, which automatically adjusts irrigation schedules without manual intervention, ensuring that nutrients are delivered at the optimal time for maximum plant absorption.

Agriculture is unpredictable, but AI-powered analytics can help farmers stay ahead of challenges. DXAS’s CropScope platform analyses vast amounts of farming data to provide accurate forecasts, pest and disease prevention models, and yield predictions.

Using machine learning algorithms and historical agronomic data, CropScope can:

• Estimate production and crop maturity based on plant growth stages.
• Detect disease and pest risks early, allowing for proactive treatments.
• Optimise fertigation schedules to maintain soil balance and enhance plant health.
• This data-driven approach empowers farmers to make confident, informed decisions while reducing risks associated with climate fluctuations, plant diseases, and inefficient resource use.