Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review

Poonam Dhiman, Amandeep Kaur, V. R. Balasaraswathi, Yonis Gulzar (), Ali A. Alwan and Yasir Hamid ()
Additional contact information
Poonam Dhiman: Department of Higher Education, Government PG College, Ambala Cantt 133001, India
Amandeep Kaur: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 713104, India
V. R. Balasaraswathi: Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankullattur 462003, India
Yonis Gulzar: Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Ali A. Alwan: Schools of Theoretical and Applied Science, Ramapo College of New Jersey, Mahwah, NJ 07430, USA
Yasir Hamid: Department of Information Security and Engineering Technology, Abu Dhabi Polytechnic, Abu Dhabi 111499, United Arab Emirates

Sustainability, 2023, vol. 15, issue 12, 1-23

Abstract: Different kinds of techniques are evaluated and analyzed for various classification models for the detection of diseases of citrus fruits. This paper aims to systematically review the papers that focus on the prediction, detection, and classification of citrus fruit diseases that have employed machine learning, deep learning, and statistical techniques. Additionally, this paper explores the present state of the art of the concept of image acquisition, digital image processing, feature extraction, and classification approaches, and each one is discussed separately. A total of 78 papers are selected after applying primary selection criteria, inclusion/exclusion criteria, and quality assessment criteria. We observe that the following are widely used in the selected studies: hyperspectral imaging systems for the image acquisition process, thresholding for image processing, support vector machine (SVM) models as machine learning (ML) models, convolutional neural network (CNN) architectures as deep learning models, principal component analysis (PCA) as a statistical model, and classification accuracy as evaluation parameters. Moreover, the color feature is the most popularly used feature for the RGB color space. From the review studies that performed comparative analyses, we find that the best techniques that outperformed other techniques in their respective categories are as follows: SVM among the ML methods, ANN among the neural network networks, CNN among the deep learning methods, and linear discriminant analysis (LDA) among the statistical techniques.This study concludes with meta-analysis, limitations, and future research directions.

Keywords: citrus fruits; deep learning; machine learning; image acquisition; classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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