Good projects begin with an end in mind...
VineIQ aims to develop an analytics platform for precision viticulture using commodity hardware and open-source software. The ultimate goal is to help vineyard managers make data-driven predictions and decisions. To this end, the project plan includes four core functions:
Sense. Use cost-effective, commercial-off-the-self hardware sensors to measure temperature, humidity, solar radiation, rain accumulation, soil moisture, and wind speed/direction, etc.
Collect. Integrate with sensor hardware vendors by connecting to third-party APIs to collect and store all the data in the cloud using an instance of QuestDB (database).
Visualize. Use Grafana to build dashboards with gauges and time series charts of various historical data points. Calculate second-order metrics such as vapor pressure deficit (VPD), growing degree days (GDD), etc.
Predict. Implement machine and deep learning models using PyTorch to make predictions such as leaf wetness duration, risk index for frost, risk index for powdery mildew, irrigation management, and spray drift management. Eventually (but not immediately), expand capabilities to include nutrient management and crop yield estimation using hyper-spectral imaging cameras.
Would it be possible to put this in a satellite image down the road for different vineyard blocks.