The drones used for soil monitoring would be equipped with GPS and imaging sensors, which would enable them to map the agricultural land and plan flight paths. Advanced flight planning software is used paired with Artificial intelligence algorithms, which can take into account factors such as wind speed and direction, terrain elevation, and the location of obstacles.

Artificial intelligence algorithms (specifically RNNs recurrent neural networks) are used to optimize flight paths, ensuring maximum coverage of the land while minimizing flight time and battery usage.

Recurrent Neural Networks (RNNs) are a type of neural network that are particularly well-suited for sequential data processing tasks, where data is processed in a specific order or sequence.

This is done by considering factors such as wind speed and direction, terrain, and the drone’s battery life. The neural network can learn and adapt to different field conditions and environmental factors, improving its performance over time.

The RNN can be trained on a sequence of flight paths and associated sensor and camera data to learn patterns and relationships between different features. This allows the RNN to predict the optimal flight path for the next iteration based on the previous flight path and sensor data.

For example, during the first flight, the drone may collect sensor and camera data and fly in a specific pattern. The RNN can then analyze this data and generate a model that predicts the optimal flight path for the next iteration based on the previous data. As the drone continues to fly and collect data, the RNN can continually refine its model and generate increasingly accurate predictions for future flight paths.

This is especially important for larger farms that span over long distances while maximizing efficiency and limiting costs.

Data Acquisition

To optimize flight patterns and ensure maximum coverage, a combination of 20 to 30 drones can be used to capture images of the farmland. The drones will fly overlapping patterns to correct for errors caused by noise and backscatter. Once the drone imagery is obtained, soil samples must be taken to validate the observations made by the drones. Soil samples should be collected from 50 random points to determine where soil-borne illnesses are present.

The platform used for this work will be Python and R language. In Python, geopandas is used along with packages that provide a set of commands that can be used for data analysis, such as fiano raterui. R language has two packages, sf and terra, that can be used for geospatial data analysis.

‍The sensors would need to be calibrated before each flight to ensure accurate measurements. The data collected would be stored on-board the drone or transmitted to a central database for further analysis. To ensure data quality, the drones may also be equipped with real-time quality control checks and automated error detection systems.