Deep learning approach for UAV-based weed detection in horticulture using edge processing

Abstract

Vision-based detection and classification systems have a great potential in supporting agricultural processes, such as monitoring fields or weed management. They build the foundation for automated weeding solutions that have the potential to reduce the amount of chemicals that have wide-ranging environmental effects. Unmanned aerial vehicles (UAVs) have the potential to operate on wet soils, whereas conventional agricultural robots only have a limited mobility in this specific environment. To operate in a UAV-based system, the vision-based detection system has to meet specific requirements in regards to the power consumption, overall detection performance, real-time capability, weight and size. This paper evaluates early research results of a vision-based deep learning approach for weed detection in horticulture, more specifically arboriculture. To our knowledge, this research has not been conducted before. The vision-based deep learning system is evaluated with images from horticultures under normal commercial growing conditions using different edge computing devices that fit the weight, size, and power-consumption requirements of a UAV-based application. These results are benchmarked against a high-performance PC-GPU-configuration. The experimental results are presented in the paper.