Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers

By X. Pantazi, Dimitrios Moshou, A. A. Tamouridou. Computers and Electronics in Agriculture, 2019

335 citations recorded by the source corpus.

Abstract The presented approach demonstrates an automated way of crop disease identification on various leaf sample images corresponding to different crop species employing Local Binary Patterns (LBPs) for feature extraction and One Class Classification for classification. The proposed methodology uses a dedicated One Class Classifier for each plant health condition including, healthy, downy mildew, powdery mildew and black rot. The algorithms trained on vine leaves have been tested in a variety of crops achieving a very high generalization behavior when tested in other crops. An original algorithm proposing conflict resolution between One Class Classifiers provides the correct identification when ambivalent data examples possibly belong to one or more conditions. A total success rate of 95% is achieved for the total for the 46 plant-condition combinations tested.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/2a3c6858e0065285194af38b2fbc65bbdc45bf8f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

  1. Johan Potgieter
  2. Muhammad Hammad Saleem
  3. Khalid Mahmood Arif

In Plant Science

  1. Johan Potgieter
  2. Muhammad Hammad Saleem
  3. Lei Shu
  4. Mohamed Amine Ferrag
  5. Leandros A. Maglaras
  6. Khalid Mahmood Arif

Citations in the corpus