Agriculture: Detection of Wheat Rust

Modern technologies enable farmers to cultivate ever-larger fields efficiently. At the same time, this means that these areas must be checked for pests and plant diseases because if overlooked, plant diseases can lead to painful harvest losses and crop failures.

Machine learning provides a remedy because large amounts of data can be generated using drones, satellite images, and remote sensors. Modern technology facilitates the collection of various measured values, parameters, and statistics, which can be monitored automatically. Farmers thus have an around-the-clock overview of soil conditions, irrigation levels, plant health, and local temperatures, despite the extensive planting of larger fields. Machine learning algorithms evaluate this data so that the farmer can use this information to react to potential problem areas at an early stage and distribute available resources efficiently.

Computer vision is of particular interest to agriculture, as the analysis of image material allows plant diseases to be detected at an early stage. Just a few years ago, plant diseases were often only noticed when they were already able to spread. The extensive spread can now be detected and stopped at an early stage using early warning systems based on computer vision. This means that farms lose less crop and save on countermeasures such as pesticides since comparatively smaller areas need to be treated.

Especially the automated detection of wheat rust has received much attention within the computer vision community. Various representatives of this aggressive fungus infest cereals in East Africa, around the Mediterranean Sea, and Central Europe, and they lead to large crop losses of wheat. Since the pest is clearly visible on the stems and leaves of cereals, it can be detected early on by trained image recognition algorithms and prevented from spreading further.