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

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.

Leaf Disease Prediction Using Deep Learning

“…Biotic factors such as viruses, fungi, bacteria, mites, and slugs emerge as a result of microbial infection in plants, whereas abiotic variables such as water, temperature, irradiation, and nutritional deprivation damage plant growth [9,25]. Accordingly, some sample plant leaf images with different diseases from the Plant Village dataset and different images from other datasets showing healthy and diseased plant leaves have been included in the study [21] and different images from other datasets showing healthy and diseased plant leaves have been summarized in the works of [29] and [30] accordingly. Additionally, the detail computer vision-based techniques and proccsses including field crops, image acquisition, leaf image datasets, image preprocessing (test set, training set, and validation sets), data splitting, and performance assessment methods) for plant disease detection and classification have been clearly indicated in the work.…”

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor

“…Deep neural networks (DNNs) have deep learning, which has revolutionized different areas, such as agriculture [ 8 , 9 , 10 , 11 , 12 ], education [ 13 ], finance [ 14 ], healthcare [ 15 ] and more. Deep learning networks are effective in brain tumor detection and diagnosis because they can automatically learn and extract features from large amounts of brain medical imaging data [ 16 ].…”

Enhancing Autism Severity Classification: Integrating LSTM into CNNs for Multisite Meltdown Grading

“…In recent times, the pervasive influence of artificial intelligence (AI) has become increasingly apparent, bringing about transformative changes across a spectrum of fields and enriching various facets of our everyday existence [4,5]. It has redefined how we approach education [6], fine-tuned financial strategies [7], simplified agricultural workflows [8][9][10][11][12][13][14][15][16], and elevated healthcare diagnostics to new heights [17][18][19][20][21][22][23]. As it seamlessly integrates into these diverse sectors, AI continues to demonstrate its capacity for generating unparalleled efficiencies, refining decision-making procedures, and addressing intricate challenges with a precision derived from data-driven insights [24,25].…”