Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley

Abstract

Information on temporal and spatial variation in weed seedling populations within agricultural fields is very important for weed population assessment and management. Most of all, it allows a potential reduction in herbicide use, when post-emergence herbicides are only applied to field sections with weed infestation levels higher than the economic weed threshold; a review of such work is provided. This paper presents a system for site-specific weed control in sugarbeet (Beta vulgaris L.), maize (Zea mays L.), winter wheat (Triticum aestivum L.) and winter barley (Hordeum vulgare L.), including online weed detection using digital image analysis, computer-based decision making and global positioning systems (GPS)-controlled patch spraying. In a 4-year study, herbicide use with this map-based approach was reduced in winter cereals by 60% for herbicides against broad-leaved weeds and 90% for grass weed herbicides. In sugarbeet and maize, average savings for grass weed herbicides were 78% in maize and 36% in sugarbeet. For herbicides against broad-leaved weeds, 11% were saved in maize and 41% in sugarbeet.

Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

“…The conditions for using the GreenSeeker™ tool and for achieving the digital photography were optimized in relation to the weed canopy, the horizontal location of the reading, walking speed, and the number of readings per sample area. These determinations were based on studies using the GreenSeeker TM and WeedSeeker sensors (Sui et al, 2008, Grohs et al, 2009) and digital photography (Gerhards & Christensen, 2003;Rasmussen et al, 2007).…”

“…For example, several parameters related to the positioning of the camera, different filters and imaging settings were studied to quantify the coverage of weeds (Rasmussen et al, 2007). Moreover, images from digital photographs were also used for the detection and spraying of herbicides in real-time instrumentations (Gerhards & Christensen, 2003). The quality of information provided by digital photographs is dependent on the resolution of the images.…”

“…However, several studies have shown that the distribution of weeds occurs heterogeneously in both spatial and temporal scales (JuradoExposito et al, 2004). Therefore, the utilization of prior estimates of weed effects and sampled determinations of weed density may not result in an accurate diagnosis of weed interference (Gerhards & Christensen, 2003). These limitations can explain the low utilization of weed interference models and economic weed threshold indices by farmers during the planning and execution of weed control.…”

Nonconventional Weed Management Strategies for Modern Agriculture

“…The findings of a 5-yr study were encouraging, as the predictions about dose reduction, economic status, and competition intensity were precise (Christensen et al 2009). Modeling for weed dynamics in wheat, sugar beet, corn, and barley also provided good results in terms of precision and decision support (Gerhards and Christensen 2003). The modeling approach is very impressive for the implementation of precision weed management; however, there are certain limitations with it.…”

The Relationship Between Patch Spraying Cost and Target Weed Distribution

“…in cereal fields published in a report for the Home Grown Cereals Authority in the United Kingdom (Lutman et al, 1998) with a critical density of >3 plant/m 2 and a minimum patch size of 25 m 2 ; (ii) 14 maps of 'scutch grass' (Elymus repens L. Gould) delineated by field scouting after harvest from a farm in Ireland located in Co. Kilkenny (52.6 degrees north, 7.1 degrees west) with a critical density of c. 10 plants/m 2 ; (iii) 9 maps delineating blackgrass (Alopecurus myosuroides Huds.) in cereals and sugar beet published by Gerhards and Christensen (2003), with a critical density of >5 plants/m 2 ; and (iv) 4 maps published by Barroso et al (2001) delineating sterile wild oat (Avena Sterilis) infestations mapped by 4 different methods: counting panicle contacts, scoring panicle density from the ground, scoring panicle density from a combine and counting seed rain on the ground. 5 plants/m 2 was defined as the critical density.…”