Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG

Zhang, Shanwen; Wang, Haoxiang; Huang, Wenzhun; You, Zhu-Hong

doi:10.1016/j.ijleo.2017.11.190

Cited by 221 publications

(71 citation statements)

References 6 publications

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“…The PDDS uses external feature extraction technique, which helps to reduce the classification time with maximum accuracy. this system, Contrast Limited Intensity Adjustment (CLIA) approach is used for image enhancement purpose and feature extraction, SURF extraction technique with GOA is used as feature optimization/selection [14]. For the training and classification of plant diseases using their leaf, DNN is used as a classifier.…”

Section: Experiments Architecturementioning

confidence: 99%

Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks

Al-bayati¹,

Üstündağ²

2020

IJCIS

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Apple leaf disease is the foremost factor that restricts apple yield and quality. Usually, much time is taken for disease detection with the existing diagnostic techniques; therefore, farmers frequently miss the best time for preventing and treating diseases. The detection of apple leaf diseases is a significant research problem, and its main aim is to discover an efficient technique for disease leaf image diagnosis. This article has made an effort to propose a method that can detect the disease of apple plant leaf using deep neural network (DNN). Plant diseases detection system (PDDS) architecture is designed. Speeded up robust feature (SURF) is used for feature extraction and Grasshopper Optimization Algorithm (GOA) for feature optimization, which helps to achieve better detection and classification accuracy. Classification parameters, such as Precision, Recall, F-measure, Error, and Accuracy is computed, and a comparative analysis has been performed to depict the effectiveness of the proposed work.

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Section: Experiments Architecturementioning

confidence: 99%

Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks

Al-bayati¹,

Üstündağ²

2020

IJCIS

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“…Generally, fuzzy superpixel based clustering [45,46] takes place in recent researches to improve the execution efficiency of color segmentation algorithms. Superpixel defined as a large number of small and independent areas with different sizes and shapes derived from an image [47].…”

Section: Superpixel-based Fuzzy C-means Clusteringmentioning

confidence: 99%

Development of Machine Vision System for Pen Parts Identification under Various Illumination Conditions in an Industry 4.0 Environment

Bagheri¹,

Venn²,

Mosaddegh³

2020

Preprint

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The fourth Industrial Revolution, well-known as "Industry 4.0", based on the integration of information and communication technologies, has introduced significant improvements in manufacturing. However, vision systems still experience various impracticalities in dealing with the effect of complex lighting on the systems platform. Therefore, a machine vision system for automatic identification of pen parts under varying lighting conditions at a digital learning factory is proposed. The developed vision system presented a straightforward approach by effectively minimizing the environmental lighting effect on the identification process. First, the obtained information of the designed vision framework is exported to a program, where a reduction of non-uniform illumination is achieved through the implementation of Retinex image enhancement techniques. Then, the color-based Fuzzy C-means (FCM) algorithm, including improved mark watershed segmentation, is employed for pen parts object classification. Finally, the position features of the selected pen part are reported. The process applied to a total number of 210 upper pen parts (caps) and 241 lower pen parts (tubes) images under different lighting scenarios. Results indicate that average parts identification precision for cap and tube parts is different and equals to 98.64% and 95.26%, respectively. The present methodology provides a promising scheme that can be feasibly adapted for other industrial Color-based object recognition applications.

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“…Second, it is susceptible to the initial centroids and always converges at a local optimum. In addition, the clustering results may be different in various trials [27,28]. In order to overcome above-mentioned limitations, an adaptive strategy whose initial cluster centers were specified according to the grayscale distribution of photomicrographs was employed to separate the regions with different maceral components.…”

Section: Image Segmentation Based On Adaptive K-means Clusteringmentioning

confidence: 99%

Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

Wang

Lei

et al. 2019

Energies

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The accurate measurement of vitrinite reflectance (especially for mean maximum vitrinite reflectance, MMVR) is an important issue in the fields of coal mining and processing. However, the application of MMVR has been somewhat hampered by the subjective and the time-consuming characteristic of manual measurements. Semi-automated methods that are oversimplified might affect the accuracy in measuring MMVR values. To address these concerns, we propose a novel MMVR measurement strategy based on machine learning (MMVRML). Considering the complex nature of coal, adaptive K-means clustering is firstly employed to automatically detect the number of clusters (i.e., maceral groups) in photomicrographs. Furthermore, comprehensive features along with a support vector machine are utilized to intelligently identify the regions with vitrinite. The largest region with vitrinite in each photomicrograph is gridded for further regression analysis. Evaluations on 78 photomicrographs show that the model based on random forest and 15 simplified grayscale features achieves the state-of-the-art root mean square error of 0.0424. In addition, to facilitate the usage of petrologists without strong expertise in the machine learning domain, we released the first non-commercial standalone software for estimating MMVR.

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Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG

Cited by 221 publications

References 6 publications

Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks

Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks

Development of Machine Vision System for Pen Parts Identification under Various Illumination Conditions in an Industry 4.0 Environment

Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

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