Modified Lawn Weed Detection: Utilization of Edge-Color Based SVM and Grass-Model Based Blob Inspection Filterbank

Watchareeruetai, Ukrit; Takeuchi, Yoshinori; Matsumoto, Tetsuya; Kumakura, Hiroaki; Ohnishi, Noboru

doi:10.1007/978-3-540-69162-4_4

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…(2006); this measure exploits the fact that the density of edges of grass is higher than broad‐leaved weeds. They extend their method further (Watchareeruetai et al. , 2008) by including the texture measure in a classifier based on support vector machines.…”

Section: Introductionmentioning

confidence: 99%

“…A more complex texture measure based on edge strength was developed by Watchareeruetai et al (2006); this measure exploits the fact that the density of edges of grass is higher than broad-leaved weeds. They extend their method further (Watchareeruetai et al, 2008) by including the texture measure in a classifier based on support vector machines. Ahmad et al (1999) used more sophisticated texture features than edge strength based on Gray level Co-occurence Matrix (GLCM) to differentiate weed from grass.…”

Section: Introductionmentioning

confidence: 99%

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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%

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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show abstract

“…The precision of the correct spray and spark rates reached 96.87% and 97.21%, respectively. 24 Nonetheless, these methods have limitations because crops and weeds may have similar morphological features. 2,25 In recent years, deep learning (DL) techniques, particularly deep convolution neural networks (DCNNs), have shown remarkable progress in image classification, object detection and instance segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…In another study, the methodology was enhanced by integrating texture and color features and employing a support vector machine in place of the Bayesian classifier, resulting in a significant improvement in weed‐detection accuracy. The precision of the correct spray and spark rates reached 96.87% and 97.21%, respectively 24 . Nonetheless, these methods have limitations because crops and weeds may have similar morphological features 2,25 …”

Section: Introductionmentioning

confidence: 99%

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks

Jin,

Han,

Zhao

et al. 2024

Pest Management Science

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BACKGROUNDAccurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf.RESULTSThe weed detection NNs, including DenseNet, GoogLeNet, and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k‐fold cross‐validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, the DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing datasets for estimating weed coverage. The inference speed of ResNet was similar to GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network (DCNN) for weed detection and coverage estimation.CONCLUSIONThese results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing the calculation of herbicide requirements for variable‐rate herbicide applications. The proposed method can be employed in a machine vision‐based autonomous site‐specific spraying system of smart sprayers.This article is protected by copyright. All rights reserved.

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“…Herbicides are widely used for weed control as they are easy to use and fast-acting. However, the use of significant amounts of herbicide causes environmental pollution and increases the cost of weed control [6,7]. Furthermore, given that the citizenry comes into direct contact with both urban and sports turfgrass leisure areas, the use of chemical herbicides is discouraged.…”

Section: Introductionmentioning

confidence: 99%

Edge detection for weed recognition in lawns

Parra

Marín²,

Yousfi³

et al. 2020

Computers and Electronics in Agriculture

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Modified Lawn Weed Detection: Utilization of Edge-Color Based SVM and Grass-Model Based Blob Inspection Filterbank

Cited by 5 publications

References 8 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

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks

Edge detection for weed recognition in lawns

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