A systematic review of deep learning techniques for plant diseases

被引:5
|
作者
Pacal, Ishak [1 ]
Kunduracioglu, Ismail [1 ]
Alma, Mehmet Hakki [2 ]
Deveci, Muhammet [3 ,4 ,5 ]
Kadry, Seifedine [6 ,7 ]
Nedoma, Jan [8 ]
Slany, Vlastimil [9 ]
Martinek, Radek [5 ]
机构
[1] Igdir Univ, Engn Fac, Dept Comp Engn, TR-76000 Igdir, Turkiye
[2] Fac Agr, Biosyst Engn, TR-76000 Igdir, Turkiye
[3] Natl Def Univ, Turkish Naval Acad, Dept Ind Engn, TR-34942 Istanbul, Turkiye
[4] Western Caspian Univ, Dept Informat Technol, Baku 1001, Azerbaijan
[5] VSB Tech Univ Ostrava, Dept Cybernet & Biomed Engn, Ostrava 70800, Czech Republic
[6] Lebanese Amer Univ, Dept Comp Sci & Math, Beirut, Lebanon
[7] Middle East Univ, MEU Res Unit, Amman 11831, Jordan
[8] VSB Tech Univ Ostrava, Dept Telecommun, Ostrava 70800, Czech Republic
[9] Mendel Univ Brno, Dept Agr Food & Environm Engn, Brno 61300, Czech Republic
关键词
Plant disease detection; Plant disease classification; Plant disease segmentation; Deep learning; Vision transformers; NEURAL-NETWORK; CLASSIFICATION;
D O I
10.1007/s10462-024-10944-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Agriculture is one of the most crucial sectors, meeting the fundamental food needs of humanity. Plant diseases increase food economic and food security concerns for countries and disrupt their agricultural planning. Traditional methods for detecting plant diseases require a lot of labor and time. Consequently, many researchers and institutions strive to address these issues using advanced technological methods. Deep learning-based plant disease detection offers considerable progress and hope compared to classical methods. When trained with large and high-quality datasets, these technologies robustly detect diseases on plant leaves in early stages. This study systematically reviews the application of deep learning techniques in plant disease detection by analyzing 160 research articles from 2020 to 2024. The studies are examined in three different areas: classification, detection, and segmentation of diseases on plant leaves, while also thoroughly reviewing publicly available datasets. This systematic review offers a comprehensive assessment of the current literature, detailing the most popular deep learning architectures, the most frequently studied plant diseases, datasets, encountered challenges, and various perspectives. It provides new insights for researchers working in the agricultural sector. Moreover, it addresses the major challenges in the field of disease detection in agriculture. Thus, this study offers valuable information and a suitable solution based on deep learning applications for agricultural sustainability.
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页数:39
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