Classification of broadleaf weed images using Gabor wavelets and Lie group structure of region covariance on Riemannian manifolds

Chen, Yongming; Lin, Ping; He, Yong; Xu, Zhenghao

doi:10.1016/j.biosystemseng.2011.04.003

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…Hence SVM added as a classifier. The reason for choosing k-NN as a base classifier because of its overall recognition accuracy and time complexity [28]. A more impressive work has been presented in [29] to classify the weed according to their types based on the feature obtained using combination of Gradient Field Distribution (GFD) and Grey Level Co-occurrence Matrix (GLCM).…”

Section: Hybrid Classificationmentioning

confidence: 99%

Analysis of Hybrid Classification Approach to Differentiate Dense and Non-dense Grass Regions

Chowdhury

Verma

Stockwell³

2014

Lecture Notes in Computer Science

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Abstract. Vegetation classification from satellite and aerial images is a common research area for fire risk assessment and environmental surveys for decades. Recently classification from video data obtained by vehicle mounted video in outdoor environments is receiving considerable attention due to the large number of real-world applications. However this is a very challenging task and requires novel research techniques. This paper presents an analysis of hybrid classification approach to distinguish vegetation in particularly the type of roadside grasses from videos recorded by the Queensland transport and main roads. The proposed framework can distinguish dense and non-dense grass regions from roadside video data. While most of the recent works focuses on infrared images, proposed approach uses image texture feature for vegetation region classification. Analysis of hybrid approach using texture feature and multiple classifiers is the main contribution of this research work. The classifiers include: Support Vector Machine (SVM), Neural Network (NN), kNearest Neighbor (k-NN), AdaBoost and Naïve Bayes. The different images were created from video data containing roadside vegetation in various conditions for training and testing purposes. The hybrid classification approach has been analysed on roadside data obtained and results are discussed.

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Section: Hybrid Classificationmentioning

confidence: 99%

Analysis of Hybrid Classification Approach to Differentiate Dense and Non-dense Grass Regions

Chowdhury

Verma

Stockwell³

2014

Lecture Notes in Computer Science

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“…The topological structure is used to measure how close two objects are to each other. In [80], the authors used a Lie group of region structures to measure the texture of weeds and provide information about pixel intensity and spatial features of broadleaf weeds. The smooth manifolds of local symmetries were derived at by applying the Riemannian Manifold on the leaf surface.…”

Section: Texture Features Based On Fractalsmentioning

confidence: 99%

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

Thyagharajan¹,

Raji²

2018

Arch Computat Methods Eng

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Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analysing image shapes, colours, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves. Table 2 Inferences of data sources Visual descriptors of leaf and classification No. of papers selected Geometrical descriptors 30 Leaf shape/tip/base/venation 40 Texture/texton 25

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“…The classifier is of the main parts of machine vision system, especially for classifying weeds and crops to the optimal spraying of herbicides. As stated earlier, there is no possibility of direct comparison 24 (2018) [105][106][107][108][109][110][111][112][113][114][115][116][117][118] 117 of the employed method in this study with those of other researchers. However, Table 6 compares the correct classification rate of the present study and those of two other studies.…”

Section: The Performance Comparison Of Two Ann-aco and Rbf Classmentioning

confidence: 99%

“…For example, Chen et al (2011) presented a new method, using Gabor wavelets and lie group structure of region covariance to classify broadleaf weed images on Riemannian manifolds. In their study, 320 images were used from four different weed types, namely Oxaliscorniculata L., Duchesneaindica (Andrews) Focke, Herba Glechomae L., and Ixerischinensis (Thunb.)…”

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confidence: 99%

Developing a Machine Vision System to Detect Weeds from Potato Plant

Sabzi

Abbaspour‐Gilandeh

Javadikia

2018

Tarım Bilimleri Dergisi

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Potato is one of the widely used products all over the world that has numerous nutritional properties. Similar to other crops, different weeds grow along with potatoes in agricultural fields. These weeds reduce the performance of crops due to competing with them to absorb water, light, and nutrients from soil. Accordingly, in this study, a machine vision system with the hybrid artificial neural network-ant colony algorithm (ANN-ACO) classifier was developed for a site-specific spraying considering the weed type. Potato plant and three weed types including Chenopodium album, Polygonum aviculare L., and Secale cereale L. were used in this study. A digital camera (SAMSUNG WB151F (CCD, 14.2 MP, 30f/s) was placed in the center of the video acquisition system. The distance between plants and the digital camera was fixed at 40 cm. For video acquisition, only lamps of white LED with a light intensity of 327 lux were selected. For filming in order to evaluate the proposed system, a 4-hectare area of Agria potato fields in Kermanshah-Iran (longitude: 7.03°E; latitude: 4.22°N) was selected. Employing the Gamma test, among 31 features, 5 features (Luminance and Hue corresponding to YIQ color space, Autocorrelation, Contrast, and Correlation) were selected. The correct classification accuracy for testing and training data using three classifiers of the hybrid ANN-ACO, radial basis function (RBF) artificial neural network, and Discriminant analysis (DA) was 99.6% and 98.13%, 97.24% and 91.23%, and 69.8% and 70.8%, respectively. The results show that the accuracy of DA statistical method is much lower than that of the hybrid ANN-ACO classifier. Consequently, the results of the present study can be used in machine vision system for the optimum spraying of herbicides.

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Classification of broadleaf weed images using Gabor wavelets and Lie group structure of region covariance on Riemannian manifolds

Cited by 12 publications

References 14 publications

Analysis of Hybrid Classification Approach to Differentiate Dense and Non-dense Grass Regions

Analysis of Hybrid Classification Approach to Differentiate Dense and Non-dense Grass Regions

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

Developing a Machine Vision System to Detect Weeds from Potato Plant

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