NDLI: A CNN-LSTM-att hybrid model for classification and evaluation of growth status under drought and heat stress in chinese fir (Cunninghamia lanceolata)

Content Provider	Springer Nature : BioMed Central
Author	Xing, Dong Wang, Yulin Sun, Penghui Huang, Huahong Lin, Erpei
Abstract	Background Cunninghamia lanceolata (Chinese fir), is one of the most important timber trees in China. With the global warming, to develop new resistant varieties to drought or heat stress has become an essential task for breeders of Chinese fir. However, classification and evaluation of growth status of Chinese fir under drought or heat stress are still labor-intensive and time-consuming. Results In this study, we proposed a CNN-LSTM-att hybrid model for classification of growth status of Chinese fir seedlings under drought and heat stress, respectively. Two RGB image datasets of Chinese fir seedling under drought and heat stress were generated for the first time, and utilized in this study. By comparing four base CNN models with LSTM, the Resnet50-LSTM was identified as the best model in classification of growth status, and LSTM would dramatically improve the classification performance. Moreover, attention mechanism further enhanced performance of Resnet50-LSTM, which was verified by Grad-CAM. By applying the established Resnet50-LSTM-att model, the accuracy rate and recall rate of classification was up to 96.91% and 96.79% for dataset of heat stress, and 96.05% and 95.88% for dataset of drought, respectively. Accordingly, the R2 value and RMSE value for evaluation on growth status under heat stress were 0.957 and 0.067, respectively. And, the R2 value and RMSE value for evaluation on growth status under drought were 0.944 and 0.076, respectively. Conclusion In summary, our proposed model provides an important tool for stress phenotyping in Chinese fir, which will be a great help for selection and breeding new resistant varieties in future.
Related Links	https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-023-01044-8.pdf
Ending Page	13
Page Count	13
Starting Page	1
File Format	HTM / HTML
ISSN	17464811
DOI	10.1186/s13007-023-01044-8
Journal	Plant Methods
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2023-07-03
Access Restriction	Open
Subject Keyword	Plant Sciences Biological Techniques Cunninghamia lanceolata CNN-LSTM Attention mechanism Drought stress Heat stress
Content Type	Text
Resource Type	Article
Subject	Plant Science Biotechnology Genetics
Journal Impact Factor	4.7/2023
5-Year Journal Impact Factor	5.6/2023

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