scholarly journals Plant diseases and pests detection based on deep learning: a review

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jun Liu ◽  
Xuewei Wang
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Digital Image Processing ◽  
Digital Image ◽  
Future Trend ◽  
Plant Diseases ◽  
Detection Methods ◽  
Learning Technology ◽  
Practical Applications ◽  
Advantages And Disadvantages

AbstractPlant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

Identifying multiple plant diseases using digital image processing

2016 ◽  
Vol 147 ◽  
pp. 104-116 ◽  
Author(s):  
Jayme Garcia Arnal Barbedo ◽  
Luciano Vieira Koenigkan ◽  
Thiago Teixeira Santos
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Plant Diseases

scholarly journals The Segmentation of Neutron Digital Radiography Image through the Edge Detection Method

2020 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Ayu Fitri Amalia ◽  
Widodo Budhi
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Neutron Beam ◽  
Digital Radiography ◽  
Digital Image ◽  
Digital Images ◽  
Neutron Radiography ◽  
Detection Methods

The digital image processing is one way to manipulate one or more digital images. Image segmentation has an essential role in the field of image analysis. The aim of this study was to develop an application to perform digital image processing of neutron digital radiographic images, hoping to improve the image quality of the digital images produced. The quality of edge detection could be used for the introduction of neutron digital radiographic image patterns through artificial intelligence. Interaction of neutrons with the matter mainly by nuclear reaction, elastic, and inelastic scattering. A neutron can quickly enter into a nucleus of an atom and cause a reaction. It is because a neutron has no charge. Neutrons can be used for digital imaging due to high-resolution information from deep layers of the material. The attenuated neutron beam in neutron radiography are passing through the investigated object. The object in a uniform neutron beam is irradiated to obtain an image neutron. The technique used in segmenting the neutron radiography in this study was a digital technique using a camera with a charge-coupled device (CCD), which was deemed more efficient technique compared to the conventional one. Through this technique, images could be displayed directly on the monitor without going through the film washing process. Edge detection methods were implemented in the algorithm program. It was the first step to complement the image information where edges characterize object boundaries. It is useful for the process of segmenting and identifying objects in neutron digital radiography images. The edge detection methods used in this study were Sobel, Prewitt, Canny, and Laplacian of Gaussian. According to the results of the image that have been tested for edge detection, the best image was carried out by the Canny operator because the method is more explicit. The obtained edges were more connected than the other methods which are still broken. The Canny technique provided edge gradient orientation which resulted in a proper localization.

scholarly journals Digital image processing with deep learning for automated cutting tool wear detection

2020 ◽  
Vol 48 ◽  
pp. 947-958
Author(s):  
Thomas Bergs ◽  
Carsten Holst ◽  
Pranjul Gupta ◽  
Thorsten Augspurger
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Tool Wear ◽  
Digital Image Processing ◽  
Digital Image ◽  
Cutting Tool ◽  
Cutting Tool Wear

scholarly journals Plataforma para apoio a modelagem de próteses com base em Processamento Digital de Imagens e Deep Learning

2021 ◽  
Author(s):  
Bianka Tallita Passos ◽  
Moira Cristina Cubas Fatiga Tillmann ◽  
Anita Maria da Rocha Fernandes
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
High Resolution ◽  
Image Classification ◽  
Digital Image Processing ◽  
Medical Practice ◽  
Digital Image ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Specific Label

Medical practice in general, and dentistry in particular, generatesdata sources, such as high-resolution medical images and electronicmedical records. Digital image processing algorithms takeadvantage of the datasets, enabling the development of dental applicationssuch as tooth, caries, crown, prosthetic, dental implant, andendodontic treatment detection, as well as image classification. Thegoal of image classification is to comprehend it as a whole and classifythe image by assigning it to a specific label. This work presentsthe proposal of a tool that helps the dental prosthesis specialist toexchange information with the laboratory. The proposed solutionuses deep learning to classify image, in order to improve the understandingof the structure required for modeling the prosthesis. Theimage database used has a total of 1215 images. Of these, 60 wereseparated for testing. The prototype achieved 98.33% accuracy.

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