A Review on Classification Methods for Plants Leaves Recognition

Suwais, Khaled; Alheeti, Khattab M. Ali; Al_Dosary, Duaa

doi:10.14569/ijacsa.2022.0130211

Cited by 7 publications

(9 citation statements)

References 47 publications

(53 reference statements)

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“…In last decades, a large number of researches have been proposed to solve the classification of plant diseases (Suwais et al, 2022). In the section, we review and analyze significant studies of classification of leaf disease images.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of plant leaf diseases using deep neural networks in color and grayscale images

Phong

2024

J. Decis. Anal. Int. Comp.

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For a long time, plants have shown a crucial role in our life, manufacturing and industry. However, a large number of diseases have significantly affected to the production of plants. The detection and diagnosis of the diseases are necessary to improve the production of plants. In recent years, the automatic classification of plant diseases using artificial intelligence and computer vision have attracted a huge number of researches. The paper presents a classification method of plant leaf diseases using various Deep Neural Networks (e.g., Alexnet, Resnet-50, Densenet-121). The color features have significantly affected the classification accuracy. Therefore, we analyzed and compared the classification accuracy on two public datasets (Plant village leaf datasets) that consist of color and grayscale images. The method obtained the classification accuracy of 98.08% and 92% on color and grayscale images, respectively. The obtained results showed the effectiveness of the method. Based on the obtained results, the impact of color features to the classification accuracy of various Deep Neural Networks is analyzed in the paper. Moreover, the paper compares the performance of various optimization algorithms during the training process of deep neural networks to classify leaf diseases.

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Section: Literature Reviewmentioning

confidence: 99%

Classification of plant leaf diseases using deep neural networks in color and grayscale images

Phong

2024

J. Decis. Anal. Int. Comp.

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“…Colour Cooccurrence Matrix (CCM), Spatial Grey Level Dependence Matrix (SGLDM), Grey Level Co-occurrence Matrix (GLCM), and Local Binary Patterns (LBP) are couple of statistical methods utilized for textural feature extraction. The color, size, form, and texture of the leaf picture determine how different existing systems and techniques of plant recognition work [3] . In this study, several leaf classi cation techniques, such as shape-based, texture-based, and vein-based ones, are compared.…”

Section: Literature Reviewmentioning

confidence: 99%

“…al analyze various feature extraction methods and classi ers. They shed light on the advantages and disadvantages of various leaf categorization techniques [3] . Pedro Filipe Barros Silva et al stated that the Primary objectives of this thesis encompass two main aspects.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Khaled Suwais et. al de ned that the area labeling process is then given the output binary picture to produce the recognised regions [3] .The image was preprocessed by E Sandeep Kumar and Vishwanath Talasia, who also described the segmentation of images utilizing AND operations into corresponding RGB Planes, cumulative distribution function (CDF), and techniques for moment of each plant [6] . In this classi cation study, Julian Scrader et.…”

Section: Literature Reviewmentioning

confidence: 99%

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Leaf Classifications using Machine Learning

Balamurali

Tiwari

2023

Preprint

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In the field of plant ecological research, plant identification is a crucial step. An automatic leaf recognition approach for identifying plants is presented in this research. The suggested method is easy to use and effective in terms of calculation. This leaf dataset contains 340 rows, 16 attributes and 36 species. The dataset was downloaded from the UCI repository. We aim to classify the different species of leaf in the dataset using machine learning algorithms. The dataset used in this project consists of different species of the leaves that includes their specimen number, entropy, eccentricity, solidity, lobedness etc. Machine learning models like Decision Tree, Logistics Regressions, Support Vector Machine, XGBoost and AdaBoost were applied to this dataset. The accuracy, precision, F1 score and recall were calculated to assess the performance of the model. These results have implications for various learning and knowledge about different techniques. After assessing the performance of various algorithms on the raw data (without feature reduction), we observed that the performance of all algorithms were relatively low. Decision Tree and XGBoost performed comparatively well with an accuracy of 42% and 64% respectively. After applying the feature reduction using information gain and correlation matrix, we observed that the few attributes are highly correlated with each other. Hence, we removed three attributes(i.e ‘Third moment', 'Solidity', 'Average Intensity'). After performing feature reduction we see an incremental improvement in accuracy by 20–35% in Support Vector Machine, 40–70% in logistic regression, 70–90% in decision tree classifier, 75–99% in XGBoost and 25–50% in AdaBoost.

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“…While doing this, it tries to find the linear equation that maximizes the distance between the closest elements of the classes. This is called margin maximization in the literature [19][20][21][22][23]. In other words, SVM aims to create the furthest upper (hyper) plane between the vectors of two classes with a linear decision function.…”

Section: Svm Classificationmentioning

confidence: 99%

An Industrial Application Towards Classification and Optimization of Multi-Class Tile Surface Defects Based on Geometric and Wavelet Features

Coskun¹,

Yi̇ği̇t²,

Üncü³

et al. 2022

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It is possible to detect visual surface defects with software in industrial tile production and increase productivity by automating the quality control process. In this process, low error rate and low cost are important indicators. In order to eliminate this negativity and the effect of the human factor, error detection software has been developed in an artificial intelligence-based industrial artificial vision environment. Spots, scratches, cracks, pore defects, which are the most common surface defects, are classified according to 6 different geometric and wavelet transform attributes. Firstly, an industrial artificial vision environment was created. In this environment, a total of 150 tile images, equal numbers from each class, were obtained on the real-time production line. The resulting images were converted into binary images by preprocessing and filtering. For classification, the support vector machines method, which performs high in two-class classifications, is used with the one versus all approach. In classifications made using RBF kernel function using wavelet features as classification performance, a higher success was achieved in all defect classes than geometric features. Real-time application software for all these processes has been developed with the Python language on Ubuntu operating system.

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A Review on Classification Methods for Plants Leaves Recognition

Cited by 7 publications

References 47 publications

Classification of plant leaf diseases using deep neural networks in color and grayscale images

Classification of plant leaf diseases using deep neural networks in color and grayscale images

Leaf Classifications using Machine Learning

An Industrial Application Towards Classification and Optimization of Multi-Class Tile Surface Defects Based on Geometric and Wavelet Features

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