Farming Technologies Incorporating Artificial Intelligence Models

In the heart of the agricultural landscape, farmers are leveraging the power of technology to optimise their yield and improve efficiency. One such innovation is Precision Farming, a method that integrates machine learning algorithms and drone image analysis to collect field data for agriculture.

Let's take a look at how this technology is being utilised by a farmer in the field.

The forecast for Field A is a promising 140 bu/acre, with a high degree of confidence. This information, along with the forecasts for Fields B and C (120 bu/acre with medium confidence and 95 bu/acre with low confidence, respectively), helps the farmer plan his harvest, logistics, and input buys.

When the confidence in the forecast is low, the farmer relies on his boots and field-data to guide him. He walks the fields, confirming the conditions first-hand.

To make the most of these forecasts, the farmer creates a prescription map. This map is generated from the yield history of the fields and current sensor data. The map is then loaded into the variable-rate controller, and safety limits are set.

The precision farming software, with its machine learning capabilities, blends sensor reads, forecasts, and crop stage into a clear target. This target is presented in millimeters or gallons per zone, providing the farmer with actionable insights.

Adopting Precision Farming Software Integrating Machine Learning Algorithms is transforming raw data into actionable maps, forecasts, and prescriptions. For instance, the software can score drone and camera images using a deep learning model, producing a disease score.

In addition, the farmer sets water targets each morning using real-time irrigation optimization and soil sensors. This ensures that the crops receive the exact amount of water they need, when they need it.

The company FarmBlick, based in Germany, is at the forefront of this technological revolution. Their precision farming software is revolutionising the way farmers approach agriculture, helping them make data-driven decisions and maximise their yields.