Details

Autor(en) / Beteiligte

• Iqbal, Zahid
• Khan, Muhammad Attique
• Sharif, Muhammad
• Shah, Jamal Hussain
• ur Rehman, Muhammad Habib
• Javed, Kashif

Titel

An automated detection and classification of citrus plant diseases using image processing techniques: A review

Ist Teil von

• Computers and electronics in agriculture, 2018-10, Vol.153, p.12-32

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2018

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• •Discussed challenges for detection and classification of citrus plant diseases.•Briefly explains recent studies including segmentation and classification.•Compare this review with existing state of the arts.•Discussed the advantages and drawbacks of each step with detail. The citrus plants such as lemons, mandarins, oranges, tangerines, grapefruits, and limes are commonly grown fruits all over the world. The citrus producing companies create a large amount of waste every year whereby 50% of citrus peel is destroyed every year due to different plant diseases. This paper presents a survey on the different methods relevant to citrus plants leaves diseases detection and the classification. The article presents a detailed taxonomy of citrus leaf diseases. Initially, the challenges of each step are discussed in detail, which affects the detection and classification accuracy. In addition, a thorough literature review of automated disease detection and classification methods is presented. To this end, we study different image preprocessing, segmentation, feature extraction, features selection, and classification methods. In addition, also discuss the importance of features extraction and deep learning methods. The survey presents the detailed discussion on studies, outlines their strengths and limitations, and uncovers further research issues. The survey results reveal that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy. Hence new tools are needed to fully automate the detection and classification processes.