Version 1
: Received: 21 June 2024 / Approved: 22 June 2024 / Online: 24 June 2024 (11:59:30 CEST)
How to cite:
Yao, J.; Wu, Y.; Liu, J.; Wang, H. Research on Drought Stress Monitoring of Winter Wheat during Critical Growth Stages Based on Improved DenseNet-121. Preprints2024, 2024061662. https://doi.org/10.20944/preprints202406.1662.v1
Yao, J.; Wu, Y.; Liu, J.; Wang, H. Research on Drought Stress Monitoring of Winter Wheat during Critical Growth Stages Based on Improved DenseNet-121. Preprints 2024, 2024061662. https://doi.org/10.20944/preprints202406.1662.v1
Yao, J.; Wu, Y.; Liu, J.; Wang, H. Research on Drought Stress Monitoring of Winter Wheat during Critical Growth Stages Based on Improved DenseNet-121. Preprints2024, 2024061662. https://doi.org/10.20944/preprints202406.1662.v1
APA Style
Yao, J., Wu, Y., Liu, J., & Wang, H. (2024). Research on Drought Stress Monitoring of Winter Wheat during Critical Growth Stages Based on Improved DenseNet-121. Preprints. https://doi.org/10.20944/preprints202406.1662.v1
Chicago/Turabian Style
Yao, J., Jianhua Liu and Hansheng Wang. 2024 "Research on Drought Stress Monitoring of Winter Wheat during Critical Growth Stages Based on Improved DenseNet-121" Preprints. https://doi.org/10.20944/preprints202406.1662.v1
Abstract
Drought stress severely affects the normal growth and development of wheat, leading to reduced yields and quality. To address the lag and limitations of traditional drought monitoring methods, this paper proposes a drought stress monitoring model for winter wheat during critical growth stages based on deep learning. By acquiring drought stress images of winter wheat during three critical growth stages: rise-jointing, heading-flowering and flowering-maturity, a dataset correlating these images with soil moisture monitoring data was established. The DenseNet121 network model was selected as the base network to extract phenotypic features of winter wheat under drought stress. Variables such as the model's training approach, changes in learning rate, and whether an attention mechanism was included, were used to conduct eight sets of model training and optimization strategy experiments. A drought stress monitoring model for winter wheat during critical growth stages based on an improved DenseNet-121 was constructed. The results showed that the average recognition accuracy of the eighth group in the test set reached 94.67%, indicating good application prospects in the recognition of drought stress levels during critical growth stages of wheat.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
