Automated analysis of visual leaf shape features for plant classification

Saleem, Gulshan; Akhtar, Mahmood; Ahmed, Nisar; Qureshi, Waqar S.

doi:10.1016/j.compag.2018.12.038

Cited by 125 publications

(58 citation statements)

References 16 publications

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“…The proposed method uses the Flavia dataset containing images with a fixed white background. Therefore, we utilized the pre-processing and segmentation methods, which have been provided by Saleem et al [15]. In these images, all images dealing with Flavia dataset are converted to 800 x 600 size, whereas the images are halved in the Folio datasets.…”

Section: Leaf Image Pre-processing and Segmentation Phasementioning

confidence: 99%

“…In this way, the L layer is initially processed for further processing. The image is then converted to binary using the Otsu threshold method [15].…”

Section: Leaf Image Pre-processing and Segmentation Phasementioning

confidence: 99%

“…Here, the symbol "k" is characterized as a static value, and it often takes an odd value like 3, 5 ,7 and 9. Euclidean distance formula is used for measurment of distances between two entities and serves as a similarity index [15]. For instance, we have chosen 3 for the value of K. This value has been selected after testing few other values such as 5 and 7, but selection of 3 provided better results.…”

Section: Classificationmentioning

confidence: 99%

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Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification

Keivani¹,

Mazloum²,

Sedaghatfar³

et al. 2020

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The main purpose of this research is to apply image processing for plant identification in agriculture. This application field has so far received less attention rather than the other image processing applications domains. This is called the plant identification system. In the plant identification system, the conventional technique is dealt with looking at the leaves and fruits of the plants. However, it does not take into account as a cost effective approach because of its time consumption. The image processing technique can lead to identify the specimens more quickly and classify them through a visual machine method. This paper proposes a methodology for identifying the plant leaf images through several items including GIST and Local Binary Pattern (LBP) features, three kinds of geometric features, as well as color moments, vein features, and texture features based on lacunarity. After completion of the processing phase, the features are normalized, and then Pbest-guide binary particle swarm optimization (PBPSO) is developed as a novel method for reduction of the features. In the next phase, these features are employed for classification of the plant species. Different machine learning classifiers are evaluated including k-nearest neighbor, decision tree, naï ve Bayes, and multi-SVM. We tested our proposed technique on Flavia and Folio leaf datasets. The final results demonstrated that the decision tree has the best performance. The results of the experiments reveal that the proposed algorithm shows the accuracy of 98.58% and 90.02% for the "Flavia" and "Folio" datasets, respectively.

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Section: Leaf Image Pre-processing and Segmentation Phasementioning

confidence: 99%

“…In this way, the L layer is initially processed for further processing. The image is then converted to binary using the Otsu threshold method [15].…”

Section: Leaf Image Pre-processing and Segmentation Phasementioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

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Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification

Keivani¹,

Mazloum²,

Sedaghatfar³

et al. 2020

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“…Broader leaf corresponds to maturity as part of its phenological development. Several studies have already developed a non-destructive approach on estimating individual leaf area of carrot (Haug & Ostermann, 2015), cauliflower (Hamuda, Mc Ginley, Glavin, & Jones, 2017), cucumber (Xie et al, 2014), lettuce (Hernández-Hernández et al, 2016;Tian & Wang, 2009;Zou et al, 2019), maize (Burgos-Artizzu, Ribeiro, Guijarro, & Pajares, 2011), potato (Boyd, Gordon, & Martin, 2002), radish (Dang et al, 2018), sugar beet (Chebrolu et al, 2017) and tomato (Boulard, Roy, Pouillard, Fatnassi, & Grisey, 2017;de Luna, Dadios, Bandala, & Vicerra, 2019) considering the length and width of the leaf shape (Saleem, Akhtar, Ahmed, & Qureshi, 2019;Wang, Jin, Shi, & Liu, 2019).…”

Section: Introductionmentioning

confidence: 99%

Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrixs

et al. 2020

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Journal of Agricultural Science www.agrivita.ub.ac.id based on the Bureau of Plant Industry-National Crop Research and Development Center (BPI-NCRDC) of the Department of Agriculture (DOA) due to its significant impact on medicinal, culinary, and health perspectives. Loose-leaf lettuce makes up its harvest growth stage after 45 to 90 days after transplanting that normally takes 12 to 14 days from germination. The sensitive way of growing lettuce is inclusive to the beneficial application of smart farming (Ge, Zhou, Kellomäki, Peltola, & Wang, 2011; Jindarat & Wuttidittachotti, 2015). It also demands a heuristic approach of monitoring to enhance its agricultural process. The lettuce growth, similar to the other higher value leafy crops, is primarily affected by light-dependent and gas-dependent reactions. The growth and development changes are verified to the changes occurred on its fresh weight, dry weight, volume, height, surface area, root zone depth ARTICLE INFO

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“…In [10] the authors obtained 63.4 % of accuracy for 70 classes of species with simple background images (the scan and scan-like images). In [11], an approach was proposed to identify tree leaves using hand-crafted features, however, this method requires the leaf image without any occlusion and with a uniform background.…”

Section: Introductionmentioning

confidence: 99%

Plant Classification in Images of Natural Scenes Using Segmentations Fusion

Nikbakhsh

Baleghi

Agahi

2020

IJE

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This paper presents a novel approach to automatic classifying and identifying of tree leaves using image segmentation fusion. With the development of mobile devices and remote access, automatic plant identification in images taken in natural scenes has received much attention. Image segmentation plays a key role in most plant identification methods, especially in complex background images. Where there are no presumptions about leaf and background, segmentation of leaves in images with complex background is very difficult. In addition, each image has special conditions, so parameters of the algorithm must be set for each image. In this paper, image segmentation fusion is used to overcome this problem. A fast method based on maximum mutual information is used to fuse the results of leaf segmentation algorithms with different parameters. Applying Tsallis entropy and g-calculus, generalized mutual information equations are derived and used to obtain the best consensus segmentation. To evaluate the proposed methods, a dataset with tree leaf images in natural scenes and complex backgrounds is used. These images were taken from Pl@ntLeaves dataset and modified so that they do not have a presumption. The experimental results show the use of Tsallis entropy and g-calculus in image segmentation fusion, improves plant species identification.

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Automated analysis of visual leaf shape features for plant classification

Cited by 125 publications

References 16 publications

Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification

Automated Analysis of Leaf Shape, Texture, and Color Features for Plant Classification

Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrixs

Plant Classification in Images of Natural Scenes Using Segmentations Fusion

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