Agriculture

Pixel

Engineering

Biology

Growing season

Archaeology

Agricultural engineering

Vegetation (pathology)

Computer science

Ground sample distance

Weed

Precision agriculture

Aerial survey

Mathematics

Geography

Weed control

Agronomy

Environmental science

Artificial intelligence

Geometry

Remote sensing

Medicine

Meteorology

Altitude (triangle)

Winter wheat

Pathology

Computers and Electronics in Agriculture

Timely availability of weed infestation maps is a key prerequisite for the implementation of site-specific weed management practices. Low-altitude aerial imagery obtained from unmanned aerial vehicles (UAVs) has shown significant potential for weed detection in crops. However, most studies focused on wide-spaced row crops such as maize and sunflower and evaluated proposed methods on single or few well-characterized experimental sites at specific points in time representing a limited range of application scenarios. This study evaluated the feasibility of weed detection in on-farm wheat fields characterized by a narrow row spacing, throughout the early and late developmental stages using UAV imagery and ground-based high-resolution imagery. Image data was obtained for nine sites, representing a wide range of management and pedoclimatic conditions. These sites can be seen as a representative sample of scenarios that would be encountered in practice. A high within- and across-site as well as temporal variation was observed for weed infestation levels and weed population species composition, highlighting the need for spatially and temporally resolved weed mapping. Image-based classification of vegetation objects as crop or weed plants was achieved with an accuracy of 0.88 and 0.72 in ground-based high-resolution images and UAV-based aerial images captured from an altitude of 10 m, respectively. The accuracy of pixel-wise, vegetation-index-based weed infestation estimation during the late vegetative stages varied strongly across sites. Our results highlight the critical importance of a high ground resolution for weed detection using object-based image analysis during the critical growth stages of wheat and of robust methods that are applicable across a range of scenarios. This suggested that future research aiming at a rapid implementation of site-specific weed management in wheat should focus on the development of ground-based systems. Yet, aerial monitoring of wheat stands during late developmental stages using currently available equipment offers significant potential for reducing weed pressure with site-specific weed control measures in the context of crop rotations.

On-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat