A Novel Image Classification Approach for Maize Diseases Recognition

Wei, Yuchen; Wei, Lisheng; Ji, Tao; Hu, Huosheng

doi:10.2174/2352096511666181003134208

Cited by 2 publications

(3 citation statements)

References 5 publications

(8 reference statements)

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“…These features are useful for classification. Type of features include color [4], [5], [23], shape [4], [5], [27] and texture such as Gray Level Co-Occurrence Matrix [4], [5], [23], [27] are commonly used. In addition, there are also more specific features such as Histogram of Gradients (HOG) [28], [30], [22], RELIEF-F [31], Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Features from Accelerated Segment Test (FAST) [30], [6].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Classification is a step for grouping features based on similarity or proximity. Various classical machine learning methods are used for classification such as Naïve Bayes [4], [30], [32], Decision Tree [4], [27], [30], [32], k-Nearest Neighbor [4], [33], Support Vector Machine with all its variants [4]- [6], [23], [25], [26], [29]- [35], Random Forest [4], [30], [32], Deep Forest [4], [36]. Neural Network [22], [24], [25], [32], and Bag of Features [6].…”

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan

Rochman

Satoto

et al. 2022

J. Phys.: Conf. Ser.

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Image classification of maize disease is an agriculture computer vision application. In general, the application of computer vision uses two methods: machine learning and deep learning. Implementations of machine learning classification cannot stand alone. It needs image processing techniques such as preprocessing, feature extraction, and segmentation. Usually, the features are selected manually. The classification uses k-nearest neighbor, naïve bayes, decision tree, random forest, and support vector machine. On the other side, deep learning is part of machine learning. It is a development of an artificial neural network that performs automatic feature extraction. Deep learning is capable of recognizing large data but requires high-speed computation. This article compare machine learning and deep learning for maize leaf disease classification. There are five research questions: how to get data, how machine learning and deep learning classify images, how the classification result compare both of them and the opportunities & challenges of research on maize leaf disease classification. The number of articles to review was 62, consisting of 18 articles using machine learning, 28 articles applying deep learning, and the rest are supporting articles.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Setiawan

Rochman

Satoto

et al. 2022

J. Phys.: Conf. Ser.

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“…With the rapid development of computers, technologies of image processing were applied widely in many areas, including quality estimation [1,2], infrared detection [3,4], disease recognition [5,6], agricultural identification [7,8], fingerprint identification [9], and many other aspects [10,11]. Especially, image analysis has become a very useful method in the study of cement microstructure based on the cement SEM (scanning electron microscope) image [12], as it is possible to analyze the mechanism of cement hydration reaction by observing the microstructure of cement in cement stone materials [13].…”

Section: Introductionmentioning

confidence: 99%

Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

Liu

Huang

et al. 2020

Applied Sciences

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Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.

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A Novel Image Classification Approach for Maize Diseases Recognition

Cited by 2 publications

References 5 publications

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

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