The Mobile Precision Agriculture Platform is a battery-powered, lightweight 4x4 vehichle, used for probing the soil on the field and for applying mixtures of fertilizer and pH regulators, precisely synthesized in real-time, based on the measured parameters and the requirements submitted to the cloud platform. A camera is used to identify objects in the field and record images for recognizing pest.

A probe mounted at the front of the vehicle tests the soil, before the vehicle crosses over the measured ground to apply the required substances. In addition, it can be equipped with pesticide sprayers. The system is autonomous, using GPS navigation and following way-points uploaded to Azure. Spatial and temporal sensory data, together with device telemetry (temperature, battery status, etc.) are transmitted in real-time to Azure for analysis.

GIS is used visualizing spatial sensor data and perhaps also for navigation. The accelerometer provides stability control and ultrasonic sensors are used for collision detection.

The HPS is mainly used for connectivity, vision and GPS, while the FPGA is used for sensor data acquisition, vehichle control and fertilizer mixing.

The Azure Cloud architecture makes use of Azure IoT, Cosmos DB, Azure Functions and if time permits, machine learning algorithms to predict crop quality based on the parameters measured repeatedly, crop type, weather data and geographical location.

It will not be easy to quantify the crop yield results empirically, without a full season with an already working device - though research material in precision agriculture can be used for these purposes. It will however be straight-forward to quantify the equipment, labor and energy cost, as well as the environmental impact, compared with other methodologies used in (precision) agriculture.

The design will make full use of FPGA I/O and parallelism, while also outlining the solid advantages provided by the HPS and the convenience of utilizing Microsoft Azure.

Project Proposal

1. High-level project introduction and performance expectation

2. Block Diagram

3. Expected sustainability results, projected resource savings