Monitoring and mapping of crop fields with UAV swarms based on information gain

TL;DR

This paper presents an adaptive UAV swarm strategy for crop field monitoring that uses information gain to focus on relevant areas, improving efficiency and accuracy over traditional uniform coverage methods.

Contribution

It introduces an information gain-based adaptive approach for UAV swarms to efficiently monitor heterogeneous crop fields, reducing mapping errors compared to pre-planned methods.

Findings

Smaller mapping errors than pre-planned approaches.

Scales well with swarm size.

Efficiently targets relevant areas for feature mapping.

Abstract

Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileged perspective and motion speed. However, the need for high-resolution images for precise classification of features (e.g., detecting even the smallest weeds in the field) contrasts with the limited payload and ight time of current UAVs. Thus, it requires several flights to cover a large field uniformly. However, the assumption that the whole field must be observed with the same precision is unnecessary when features are heterogeneously distributed, like weeds appearing in patches over the field. In this case, an adaptive approach that focuses only on relevant areas can perform better, especially when multiple UAVs are employed simultaneously. Leveraging on a swarm-robotics approach, we propose a monitoring and mapping strategy that adaptively chooses the target areas based on the expected information gain, which measures the potential for uncertainty reduction due to further observations. The proposed strategy scales well with group size and leads to smaller mapping errors than optimal pre-planned monitoring approaches.