A robot to detect and control broad‐leaved dock (<i>Rumex obtusifolius</i> L.) in grassland

Evert, F.K. van; Samsom, J.; Polder, G.; Vijn, M.P.; Dooren, Hendrik-Jan van; Lamaker, A.; Heijden, G.W.A.M. van der; Kempenaar, C.; Zalm, Ton van der; Lotz, L.A.P.

doi:10.1002/rob.20377

Cited by 64 publications

(51 citation statements)

References 24 publications

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“…Because of the high density of the grass in a pasture, uniformity analysis fails because the mean and range values of weed and grass tiles overlap considerably. Van Evert et al. (2009, 2011) developed a robot for automatic, real‐time detection and removal of the weed using local spectral power as the texture feature.…”

Section: Introductionmentioning

confidence: 99%

The role of textures to improve the detection accuracy of Rumex obtusifolius in robotic systems

Hiremath

Heijden²,

Evert³

et al. 2012

Weed Research

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Hiremath S, Van Der Heijden G, Van Evert FK & Stein A (2012) The role of textures to improve the detection accuracy of Rumex obtusifolius in robotic systems. Weed Research.52, 430–440. Summary Rumex obtusifolius is a common weed that is difficult to control in organic farming systems. Among the proposed non‐chemical treatment methods, robotic systems to mechanically remove the weed have potential for its automatic detection and removal. This article considers a recently developed robot with a real‐time vision system capable of detecting R. obtusifolius in a pasture with the objective of improving its detection accuracy. We show that the texture measure used by the current system is equivalent to local variance and has limited value for detecting R. obtusifolius in a complex background like pasture. To improve the system, two different sets of visual texture features corresponding to Gray level Co‐occurence Matrix (GLCM) and Laws’ filter masks were investigated. Through feature selection, we determined that GLCM features of contrast, entropy and correlation were the best among the two sets of features and were 25% more accurate in estimating the taproot location than the current system. We incorporated these texture features in a new segmentation algorithm and demonstrated its robustness by testing it on a data set of 92 images with high complexity in terms of variation in illumination and weed size. The new segmentation algorithm had a detection accuracy of 90%, with an average error of 141 mm in the estimation of the location of the taproot of R. obtusifolius, compared with 308 mm with the former algorithm.

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Section: Introductionmentioning

confidence: 99%

The role of textures to improve the detection accuracy of Rumex obtusifolius in robotic systems

Hiremath

Heijden²,

Evert³

et al. 2012

Weed Research

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“…Extensive research has been conducted to address this issue and alternate techniques have been developed to control weeds in the plant line [21][22][23].…”

Section: Precision Weed Management Systemmentioning

confidence: 99%

GNSS in Precision Agricultural Operations

Pérez-Ruiz¹,

Upadhyaya²

2012

New Approach of Indoor and Outdoor Localization Systems

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“…Farmers have also experienced a decrease in the availability of workers willing to perform manual tasks such as hand weeding. Alternatives have been developed to reduce or eliminate herbicide applications, a step that is required for organic production [4–7]. …”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a Combined Cultivator and Band Sprayer with a Row-Centering RTK-GPS Guidance System

Pérez-Ruiz

Carballido

Agüera

et al. 2013

Sensors

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Typically, low-pressure sprayers are used to uniformly apply pre- and post-emergent herbicides to control weeds in crop rows. An innovative machine for weed control in inter-row and intra-row areas, with a unique combination of inter-row cultivation tooling and intra-row band spraying for six rows and an electro-hydraulic side-shift frame controlled by a GPS system, was developed and evaluated. Two weed management strategies were tested in the field trials: broadcast spraying (the conventional method) and band spraying with mechanical weed control using RTK-GPS (the experimental method). This approach enabled the comparison between treatments from the perspective of cost savings and efficacy in weed control for a sugar beet crop. During the 2010–2011 season, the herbicide application rate (112 L ha−1) of the experimental method was approximately 50% of the conventional method, and thus a significant reduction in the operating costs of weed management was achieved. A comparison of the 0.2-trimmed means of weed population post-treatment showed that the treatments achieved similar weed control rates at each weed survey date. Sugar beet yields were similar with both methods (p = 0.92). The use of the experimental equipment is cost-effective on ≥20 ha of crops. These initial results show good potential for reducing herbicide application in the Spanish beet industry.

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A robot to detect and control broad‐leaved dock (Rumex obtusifolius L.) in grassland

Cited by 64 publications

References 24 publications

The role of textures to improve the detection accuracy of Rumex obtusifolius in robotic systems

The role of textures to improve the detection accuracy of Rumex obtusifolius in robotic systems

GNSS in Precision Agricultural Operations

Development and Evaluation of a Combined Cultivator and Band Sprayer with a Row-Centering RTK-GPS Guidance System

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