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

Robin Mink,Gerassimos Peteinatos,Roland Gerhards,Avishek Dutta,Markus Sökefeld,Johannes Engels,Michael Hahn

doi:10.3390/agriculture8050065

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.

Highlights

One of the major milestones in weed remote sensing technology research has been the implementation of Unmanned Aerial Vehicles (UAVs) as sensor carriers
Imagery was connected to crop planting information. By concatenating these data inputs, we propose a new methodology for improving the UAV weed mapping in arable fields
The automatic flight altitude adjustment by following a digital elevation model resulted in a steady Ground Sample Distance (GSD) of the orthomosaic images and and DEMs

Summary

Introduction

One of the major milestones in weed remote sensing technology research has been the implementation of Unmanned Aerial Vehicles (UAVs) as sensor carriers. Rasmussen et al [1] presented an estimation of plant soil cover from small and inexpensive aircraft systems evaluating the efficacy of mechanical weed harrowing in barley (Hordeum vulgare L.) and chemical weed control in oilseed rape Season site-specific weed management in sunflowers based on UAV imagery is described in Torres-Sánchez et al [2]. Both authors conclude that UAVs are useful to map weed pressure for site-specific weed management. Several UAV imaging sensors (e.g., Red, Green and Blue (RGB) and multispectral cameras), spatial resolutions and data analysis algorithms (e.g., Object-Based Image Analysis (OBIA)) are discussed in the literature [1,2,3,4,5,6,7,8,9].

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