Crop damage recognition


The current methodology of the damage assessment is done by expert sampling and field inspection. The selection of the surveyed areas are done by systematic selection, aiming uniform and similar distribution.

The extent of the cultivated area, the expected yield, or the percentage of damage is an indispensable information for estimating the damage. For areas with significantly different average yield or damage, the relevant data need to be determined separately, and the affected areas must be assessed with a field GPS device.


In contrast, the agricultural damage recognition and estimation environment supported by our machine learning algorithm, provides a method which provides accurate damage estimation without a detailed inspection of the affected area. This is supported by a computer based damage recognition algorithm that recognizes agricultural damage caused by game, ice, inland water, wind and fire on field crops.


During remote sensing, we collect information about an object or its surface without having direct physical connection, which results that we can simultaneously obtain information from large areas. With the appearance of unmanned aerial vehicles, new perspectives have emerged in the use of technology for agricultural purposes, including estimation of wildlife damage and natural damage.

By analyzing the aerial images, we have the opportunity to examine and classify the different patches among the field. Images and recordings collected by standard methodology allow the use of automated pattern recognition algorithms to facilitate field work.

The pattern of different damages differ from the non-damaged ones. Based on this, automated identification is feasible.

Crop damage recognition with our deep learning algorythm

The analysis determine the type of damage and the size of the affected area. The use of drone technology in damage estimation provides a novel solution. Deep learning algorithms which run on high-resolution aerial images are completely new and innovative tools that have hardly been used in agriculture before.

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Advantages of UAVAI

Faster damage recognition

Easy to interpret unified reports

Affordable and cost saver

Reliable and verifiable results


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