Online pattern recognition and machine learning techniques for computer-vision: Theory and applications

2010 ◽  
Vol 28 (7) ◽  
pp. 1063-1064 ◽  
Author(s):  
Bogdan Raducanu ◽  
Jordi Vitrià ◽  
Ales Leonardis
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Machine Learning Techniques ◽  
Learning Techniques

Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques

2020 ◽  
Vol 22 (3) ◽  
pp. 27-29 ◽  
Author(s):  
Paula Ramos-Giraldo ◽  
Chris Reberg-Horton ◽  
Anna M. Locke ◽  
Steven Mirsky ◽  
Edgar Lobaton
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Drought Stress ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Stress Detection ◽  
Vision Systems ◽  
Learning Techniques ◽  
Computer Vision Systems

scholarly journals Recognition of Soybean Diseases Using Machine Learning Techniques Based on Segmentation of Images Captured By UAVs

2020 ◽  
Author(s):  
Gercina Da Silva ◽  
Alessandro Ferreira ◽  
Denilson Guilherme ◽  
José Fernando Grigolli ◽  
Vanessa Weber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Computer Program ◽  
Random Forests ◽  
Machine Learning Techniques ◽  
Target Spot ◽  
Learning Techniques ◽  
Image Dataset ◽  
Segmentation Of Images ◽  
Soybean Diseases

Soybean is an important product for the Brazilian economy, however it has factors that can limit its productive income, like the diseases that are generally difficult to control. Thus, this article aims to use a computer program to recognize diseases in images obtained by a UAV in a soybean plantation. The program is based on computer vision and machine learning, using the SLIC algorithm to segment the images into superpixels. To achieve the objective, after the segmentation of the images, an image dataset was created with the following classes: mildew, target spot, Asian rust, soil, straw and healthy leaves, totaling 22,140 images. Diagrammatic scales were used to assess disease severity. The disease recognition computer program explored four supervised learning techniques: SVM, J48, Random Forest and KNN. The techniques that obtained the best performance were SVM and Random Forests, taking into account the results obtained with all the evaluation metrics used. It was found that the program is efficient to differentiate the classes of diseases treated in this article.

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