Plant Leaf Classification and Comparative Analysis of Combined Feature Set Using Machine Learning Techniques

Ariyapadath, Sujith

doi:10.18280/ts.380603

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…For the Swedish dataset, as shown in Table 10, the classification accuracy of 98.9% using the proposed method is only slightly lower than 99.41% of the Pearline’s method 32 and 98.99% of Ariyapadath’s method 59 …”

Section: Experiments and Analysismentioning

confidence: 92%

“…For the Swedish dataset, as shown in Table 10, the classification accuracy of 98.9% using the proposed method is only slightly lower than 99.41% of the Pearline's method 32 and 98.99% of Ariyapadath's method. 59 For the ICL dataset, as shown in Table 11, the classification accuracy is 91.5% using the proposed method, lower than Lei's 62 94.1% and Zhang's 64 94.61% and equivalent to Yang's 61 91.6%.…”

Section: Overall Evaluation Of the Plant Classification Systemmentioning

confidence: 93%

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Effective shape features for leaf classification

Zhang,

Dai,

Long

et al. 2023

J. Electron. Imag.

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.When conducting image classification, traditional and deep-learning based methods require extracting target features in different ways and then classifying targets based on the features. Therefore, extracting more informative and effective features is very important, and this problem also exists in plant leaf classification. To address the issue for traditional leaf classification, this article carefully designs two shape features [area ratio-based shape descriptor (ARSD) and corner-based shape descriptor (CSD)] and one leaf vein feature (LVF) extraction method based on statistical characteristics. Then, according to the extraction of ARSD, CSD, and LVF, a general feature extraction framework is formed, which first normalizes direction, segments region of interest, then divides it into subregions, counts the number of pixels in each sub region, and finally forms feature vectors. Finally, to visually verify the effectiveness of these features, they were applied to the classic leaf classification framework, which is adjusted to form a two-stage classification method from coarse classification of shapes and fine classification of species. In the first stage (coarse classification), leaves in three universal datasets [Flavia dataset, Swedish dataset and Intelligent Computing Laboratory (ICL) dataset] and a self-built LZU dataset are respectively divided into several shape categories only by the ARSD. Experiment results show that the ARSD has obvious advantages in shape-based rough classification. Then in the second stage (fine classification), shape, texture, vein, and color features, including ARSD, CSD, local binary patterns, gray-level co-occurrence matrix, the developed LVF, and color moments, are combined to subdivide finer species in each shape category. Extensive experiment results show that the classification accuracy on the Flavia, Swedish, ICL, and LZU datasets have reached 99.3%, 98.9%, 91.5%, and 95.3%, respectively, comparable to deep learning-based methods.

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Section: Experiments and Analysismentioning

confidence: 92%

Section: Overall Evaluation Of the Plant Classification Systemmentioning

confidence: 93%

Effective shape features for leaf classification

Zhang,

Dai,

Long

et al. 2023

J. Electron. Imag.

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show abstract

“…Table 2, 3, and 5 shows the VOI, GCE & JD of Proposed technique is low as compare to other methods. Thus, the proposed algorithm is proved to be better compared to the existing algorithms [25,26].…”

Section: Performance Evaluationmentioning

confidence: 93%

Detection of Breast Cancer Using Modified Markov Model

Aslam¹,

Narayana²,

Priya³

2022

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A new segmentation method for medical image with intensity clustering is enclosed. In the intended approach, an improved K-means and EM algorithm are combined to develop a hybrid strategy for better clumping. The intended advent aims to exploit the ability of providing well distributed clump of K-means and the closeness of clumps provided by EM. The introductory clumps are provided by the improved K-means algorithm. This introductory clumping process outcomes in canters which are distributed in the given data. These canters form the introductory variable for EM, which afterwards uses them and repeats to find the local maxima. Experiments for synthetic and real images make evident the feasibility and superiority of the projected model.

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“…To determine similarity between data points, it employs the Euclidean distance, Manhattan distance, chi-square, and cosine similarity methods. The KNN classifier performs better with fewer input variables and the same data size [30]. A Support Vector Machine (SVM) is used as a classifier to aid in the determination of suitable decision boundaries or hyperplanes for performing multiple tasks.…”

Section: Classification Algorithmsmentioning

confidence: 99%

An Effective Classifier Model for Imbalanced Network Attack Data

莈tin¹

2022

Computers, Materials &Amp; Continua

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Recently, machine learning algorithms have been used in the detection and classification of network attacks. The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98, KDD'99, NSL-KDD, UNSW-NB15, and Caida DDoS. However, these datasets have two major challenges: imbalanced data and highdimensional data. Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets. On the other hand, having a large number of features increases the runtime load on the algorithms. A novel model is proposed in this paper to overcome these two concerns. The number of features in the model, which has been tested at CICIDS2017, is initially optimized by using genetic algorithms. This optimum feature set has been used to classify network attacks with six well-known classifiers according to high f1-score and g-mean value in minimum time. Afterwards, a multi-layer perceptron based ensemble learning approach has been applied to improve the models' overall performance. The experimental results show that the suggested model is acceptable for feature selection as well as classifying network attacks in an imbalanced dataset, with a high f1-score (0.91) and g-mean (0.99) value. Furthermore, it has outperformed base classifier models and voting procedures.

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Plant Leaf Classification and Comparative Analysis of Combined Feature Set Using Machine Learning Techniques

Cited by 11 publications

References 27 publications

Effective shape features for leaf classification

Effective shape features for leaf classification

Detection of Breast Cancer Using Modified Markov Model

An Effective Classifier Model for Imbalanced Network Attack Data

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