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

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