A New Combined Strategy for Discrimination between Types of Weed

Herrera, Pedro Javier; Dorado, José; Ribeiro, Ángela

doi:10.1007/978-3-319-03413-3_34

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Finally, the detection results were validated by annotating the images using the trained classifiers. Herrera et al [6] proposed a method for discriminating between weeds based on the combined strategy. The main objective was to achieve appropriate discrimination between the two weed groups under varying conditions of lighting and soil background texture.…”

Section: Related Workmentioning

confidence: 99%

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Prema¹,

Murugan²

2016

ELCVIA

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Weed management is the most significant process in the agricultural applications to improve the crop productivity rate and reduce the herbicide application cost. Existing weed detection techniques does not yield better performance due to the complex background and illumination variation. Hence, there arises a need for the development of effective weed identification technique. To overcome this drawback, this paper proposes a novel Angular Texture Pattern (ATP) Extraction Method for crop and weed discrimination using curvelet transformation. In our proposed work, Adaptive Median Filter (AMF) is used for filtering the impulse noise from the image. Plant image identification is performed using green pixel extraction and K-means clustering. Wrapping based Curvelet transform is applied to the plant image. Feature extraction is performed to extract the angular texture pattern of the plant image. Particle Swarm Optimization (PSO)-based Differential Evolution Feature Selection (DEFS) approach is applied to select the optimal features. Then, the selected features are learned and passed through an RVM-based classifier to find out the weed. Edge detection and contouring is performed to identify the weed in the plant image. The fuzzy rule-based approach is applied to detect the low, medium and high levels of the weed patchiness. From the experimental results, it is clearly observed that the accuracy of the proposed approach is higher than the existing Support Vector Machine (SVM) based approaches. The proposed approach achieves higher accuracy, sensitivity, specificity, Hausdorff distance, Jaccard distance and Dice distance.

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Section: Related Workmentioning

confidence: 99%

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

Prema¹,

Murugan²

2016

ELCVIA

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show abstract

“…Moreover, is necessary to adapt the method to the varying conditions existing in real field situations, since the images were taken with controlled lighting and the plants appear isolated. A preliminary study in [ 45 ] shows the Hu invariant moments obtained from regions that belong to the two weed species under study. The data from monocot weeds show that Hu moments tend to have negative values in the fifth, sixth and seventh moment, while the moments that give a positive value are close to 1 or 2.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method

Herrera

Dorado

Ribeiro

2014

Sensors

Self Cite

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An important objective in weed management is the discrimination between grasses (monocots) and broad-leaved weeds (dicots), because these two weed groups can be appropriately controlled by specific herbicides. In fact, efficiency is higher if selective treatment is performed for each type of infestation instead of using a broadcast herbicide on the whole surface. This work proposes a strategy where weeds are characterised by a set of shape descriptors (the seven Hu moments and six geometric shape descriptors). Weeds appear in outdoor field images which display real situations obtained from a RGB camera. Thus, images present a mixture of both weed species under varying conditions of lighting. In the presented approach, four decision-making methods were adapted to use the best shape descriptors as attributes and a choice was taken. This proposal establishes a novel methodology with a high success rate in weed species discrimination.

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A Real-Time IoT and Image Processing based Weeds Classification System for Selective Herbicide

Ahmad

Adnan

Chehri

2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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A New Combined Strategy for Discrimination between Types of Weed

Cited by 4 publications

References 32 publications

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

A Novel Angular Texture Pattern (ATP) Extraction Method for Crop and Weed Discrimination Using Curvelet Transformation

A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method

A Real-Time IoT and Image Processing based Weeds Classification System for Selective Herbicide

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