Plant Disease Detection and Classification: A Systematic Literature Review

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-05, Vol.23 (10), p.4769
Main Authors: Ramanjot, Mittal, Usha, Wadhawan, Ankita, Singla, Jimmy, Jhanjhi, N Z, Ghoniem, Rania M, Ray, Sayan Kumar, Abdelmaboud, Abdelzahir
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Artificial intelligence
Classification
convolutional neural network
Data augmentation
Database industry
Databases, Factual
Deep learning
disease identification
Humans
Humidity
Illnesses
Image enhancement
image processing
Image quality
India
Literature reviews
Machine learning
Neural networks
Plant Diseases
Research methodology
Research Personnel
Review
Wheat
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23104769