Agriculture has become increasingly important in the face of growing population and climate change. New monitoring technologies enabled through continuous satellite observations can help to improve the efficiency of food production down to the level of individual fields. To achieve this, it is essential to accurately delineate the boundaries of agricultural fields. This is not always a straightforward task, particularly in regions where fields are irregularly shaped or have complex boundaries.
In recent years, deep convolutional neural networks (CNNs) have emerged as a powerful tool for image recognition and classification. By leveraging large datasets and powerful computational resources, these models can learn to identify complex patterns and structures in images. In the context of agriculture, CNNs can be trained to recognize the boundaries of agricultural fields from satellite data.
The challenge with using satellite data for field boundary recognition is that the resolution of the data is often too low to accurately identify the boundaries of individual fields. For example, Sentinel-2 data is typically available at a resolution of 10m per pixel. However, the boundaries of many fields are much smaller than this, and may not be visible at this resolution. In the context of field boundary recognition, super-resolution can be used to enhance the resolution of the satellite data to a level where the boundaries of individual fields are visible.
Training a deep CNN to recognize the boundaries of agricultural fields from super-resolved satellite data enables the accurate identification of individual field boundaries at a resolution of 2.5m per pixel. This is a significant improvement over the original resolution of the Sentinel-2 data, and allows for much more precise delineation of field boundaries.
One of the key advantages of using a deep CNN for field boundary recognition is that it can be trained to automatically detect differences between the predicted boundaries and the officially registered field boundaries. This allows for the automatic detection of inaccuracies or errors in the registration of field boundaries. The use of deep CNNs for field boundary recognition greatly improves the efficiency and accuracy of precision agriculture. Accurately delineating the boundaries of individual fields enables farmers to more effectively manage their crops, reduce waste, and improve yields.