Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping

Abstract

Sensor-based weed mapping in arable fields is a key element for site-specific herbicide management strategies. In this study, we investigated the generation of application maps based on Unmanned Aerial Vehicle imagery and present a site-specific herbicide application using those maps. Field trials for site-specific herbicide applications and multi-temporal image flights were carried out in maize (Zea mays L.) and sugar beet (Beta vulgaris L.) in southern Germany. Real-time kinematic Global Positioning System precision planting information provided the input for determining plant rows in the geocoded aerial images. Vegetation indices combined with generated plant height data were used to detect the patches containing creeping thistle (Cirsium arvense (L.) Scop.) and curled dock (Rumex crispus L.). The computed weed maps showed the presence or absence of the aforementioned weeds on the fields, clustered to 9 m × 9 m grid cells. The precision of the correct classification varied from 96% in maize to 80% in the last sugar beet treatment. The computational underestimation of manual mapped C. arvense and R. cripus patches varied from 1% to 10% respectively. Overall, the developed algorithm performed well, identifying tall perennial weeds for the computation of large-scale herbicide application maps.

Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

“…They report an improved weed detection accuracy, as compared with previous work on maize [3]. Plant height in conjunction with a vegetation index were also thresholded into binary maps to identify weeds in maize and sugar beet fields, and produce site-specific herbicide application maps [20]. With this body of work, there is ample precedent for the efficacy of using data on vegetation canopy height for a variety of purposes, which has not been adequately tested in weed mapping.…”

A novel approach to estimating the competitive ability of Cirsium arvense in cereals using unmanned aerial vehicle imagery

“…The automated image analysis used in this study is not biased, but it is not generally applicable because it detects all green vegetation (Rasmussen et al ., ). Algorithms have recently been developed to detect C. arvense in UAV imagery (Mink et al ., ; Rasmussen et al ., ), but these algorithms are not able to separate C. arvense from other green vegetation (Rasmussen et al ., ) or from other green vegetation at the same height as C. arvense (Mink et al ., ).…”

“…All previous competition studies used traditional weed assessment methods based on sampling techniques, where C. arvense shoots were counted or biomass was harvested in small plots (1 m 2 ). Much work on the detection of C. arvense based on images from unmanned aerial vehicles (UAV) has been carried out (Ståhl et al ., ; Garcia‐Ruiz et al ., ; Olsen et al ., ; Sørensen et al ., ; Mink et al ., ; Rasmussen et al ., ; Forero et al ., ) but methods to count overlapping C. arvense shoots are still lacking. As discussed in Rasmussen et al .…”

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

“…For example, Mink and his colleagues equipped a drone with RGB and multispectral cameras to measure vegetation indices and plant height data in order to detect weeds in maize and sugar beet fields. They were able to develop a weed height model which they used to detect weed instances, based upon the vegetation index excess green red (ExGR) and CHM [50]. Shortly after, Pflanz and his colleagues published an article which demonstrated how they were able to use an image classifier called Bag of Visual Words along with a support vector machine (SVM) to map weeds within a field of wheat.…”