Published February 29, 2020 | Version v1
Journal article Open

Experimental Selection of Machine learning Techniques and Image features to Detect "Cactus" Diseases

  • 1. lecturer department of Computer Science Aksum University, Ethiopia
  • 2. Professor & Head, Dharmsinh Desai University, Nadiad.
  • 1. Publisher

Description

Image is a very important data in machine learning. In order to select better features, feature extraction techniques and classifiers, intensive experiments are taken place using data. In this work, best feature, feature extraction technique and machine learning classifier are selected experimentally. Hence, bag of features were the best features experimentally out of color, texture and bag of features. Of color histogram, bag of features and GLCM (Gray-level co-occurrence matrix), bag of features extraction technique is found to be the best one experimentally. Of the machine learning classifiers shown in the scatter plot and confusion matrix, linear support vector machine is selected and the achieved accuracy is 97.2%.

Files

C5029029320.pdf

Files (986.0 kB)

Name Size Download all
md5:2b1d2a49bb0867b2211a0f065c4e88e3
986.0 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2249-8958 (ISSN)

Subjects

ISSN
2249-8958
Retrieval Number
C5029029320/2020©BEIESP