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

作者：Poonam Dhiman, Amandeep Kaur, V. R. Balasaraswathi, Yonis Gulzar, Ali A. Alwan, Yasir Hamid · 发表于：Sustainability · 年份：2023 · DOI：10.3390/su15129643 · 被引用次数：85 · 研究领域：Spectroscopy and Chemometric Analyses、Smart Agriculture and AI、Leaf Properties and Growth Measurement

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 metho...