Details

Autor(en) / Beteiligte

• Wu, Xue
• Deng, Hongyu
• Wang, Qi
• Lei, Liang
• Gao, Yangyang
• Hao, Gefei

Titel

Meta‐learning shows great potential in plant disease recognition under few available samples

Ist Teil von

• The Plant journal : for cell and molecular biology, 2023-05, Vol.114 (4), p.767-782

Ort / Verlag

England: Blackwell Publishing Ltd

Erscheinungsjahr

2023

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• SUMMARY Plant diseases worsen the threat of food shortage with the growing global population, and disease recognition is the basis for the effective prevention and control of plant diseases. Deep learning has made significant breakthroughs in the field of plant disease recognition. Compared with traditional deep learning, meta‐learning can still maintain more than 90% accuracy in disease recognition with small samples. However, there is no comprehensive review on the application of meta‐learning in plant disease recognition. Here, we mainly summarize the functions, advantages, and limitations of meta‐learning research methods and their applications for plant disease recognition with a few data scenarios. Finally, we outline several research avenues for utilizing current and future meta‐learning in plant science. This review may help plant science researchers obtain faster, more accurate, and more credible solutions through deep learning with fewer labeled samples. Significance Statement This paper focuses on the research related to meta‐learning based plant disease recognition with few samples. With the significant progress in deep‐learning based plant disease detection, disease diagnose is gradually intelligent. However, traditional deep‐learning has limited the existing research development because of the great demand for samples, time and effort consumption, and the lack of migration between models. Furthermore, traditional deep‐learning faces the time‐consuming and labor‐intensive nature of the available samples and the lack of transferability between models. The emergence of meta‐learning methods has solved the problem of sample annotation arising from the huge sample size, and strong feature‐extracting ability, which may be an essential tool for future crop phenotype research.