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. 2023 May 15;23(10):4769.
doi: 10.3390/s23104769.

Plant Disease Detection and Classification: A Systematic Literature Review

Ramanjot  1 , Usha Mittal  1 , Ankita Wadhawan  1 , Jimmy Singla  2 , N Z Jhanjhi  3 , Rania M Ghoniem  4 , Sayan Kumar Ray  3 , Abdelzahir Abdelmaboud  5
Affiliations

Plant Disease Detection and Classification: A Systematic Literature Review

Ramanjot et al. Sensors (Basel). .

Abstract

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.

Keywords: convolutional neural network; deep learning; disease identification; image processing; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The entire method of research utilized to produce this study.
Figure 2
Figure 2
Inclusion and exclusion techniques utilized in this review.
Figure 3
Figure 3
Number of papers, by year, from 2010 to 2022.
Figure 4
Figure 4
Parameters considered for the literature review.
Figure 5
Figure 5
Usage of various data acquisition sources.
Figure 6
Figure 6
Usage graph of different pre-processing techniques (% in descending order).
Figure 7
Figure 7
The deployment percentage for various augmentation methods.
Figure 8
Figure 8
Utilization of different feature extraction techniques in % (% in descending order).
Figure 9
Figure 9
Utilization of various extracted features in % (% in descending order).
Figure 10
Figure 10
Various techniques utilized for classification.
Figure 11
Figure 11
Percentages of techniques used for enhancing image quality.
Figure 12
Figure 12
Techniques utilized for reducing overfitting.
Figure 13
Figure 13
Species for which diagnosis was performed.
Figure 14
Figure 14
Classification accuracies of the evaluated studies.
Figure 15
Figure 15
Flowchart showing how the observations were framed.
Figure 16
Figure 16
Flowchart showing how the comparison was framed.
See this image and copyright information in PMC

References

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