CSFL: A novel unsupervised convolution neural network approach for visual pattern classification

2017 ◽  
Vol 30 (5) ◽  
pp. 311-324 ◽  
Author(s):  
Sadaqat ur Rehman ◽  
Shanshan Tu ◽  
Yongfeng Huang ◽  
Guojie Liu
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Convolution Neural Network ◽  
Visual Pattern ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Plant Leaf Diseases Prediction and Classification Using Optimization Based Convolution Neural Network

2020 ◽  
pp. 174-176
Author(s):  
Mohan M ◽  
Vijayaganth V ◽  
Naveenkumar M
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Convolution Neural Network ◽  
Support Vector ◽  
Network Approach ◽  
Plant Leaves ◽  
Neural Network Approach ◽  
Plant Leaf ◽  
Network Support ◽  
Highlight Extraction

Plant leaf diseases and ruinous bugs are a significant test in the horticulture area. Quicker and an exact forecast of leaf diseases in plant could assist with building up an early treatment strategy while extensively decreasing financial misfortunes. Current progressed advancements in profound learning permitted analysts to amazingly improve the presentation and exactness of article identification and acknowledgment frameworks. A profound learning-based way to deal with recognize leaf illnesses in various plants utilizing pictures of plant leaves. The picture handling ventures for plant illness recognizable proof incorporate obtaining of pictures, pre-preparing, division and highlight extraction. Focus in predominantly on the most used order systems in illness location of plants, for example, Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and Artificial Neural Network. It has been seen from the examination that advancement Convolutional Neural Network approach gives better precision contrasted with the conventional methodologies. Optimization based CNN convolution neural network the proposed framework can viably recognized various sorts of diseases with the capacity to manage complex situations from a plant's region.

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