Year: 2025
Type: article
Abstract: This article discusses the salt stress in strawberry seedlings under greenhouse conditions in summer. Spectral acquisition equipment was used to obtain spectral data, and the ambient and leaf temperatures were combined to model and analyze the relative chlorophyll content in the strawberry seedling leaves. Four different salt gradients were employed to culture the strawberry seedings: S1 (0 mmol/L NaCl), S2 (50 mmol/L NaCl), S3 (100 mmol/L NaCl), and S4 (150 mmol/L NaCl). The results indicated that the spectral curves of the strawberry seedlings in groups S3 and S4 began to differentiate after day 3 (D3), and their average canopy temperature increased by 2.5 °C and 3.1 °C, respectively. The performance of traditional machine learning models integrating leaf temperature improved by more than 80%. Under each stress treatment, the one-dimensional ResNet model integrated with leaf temperature performed the best, with root mean square and mean absolute errors below 1.7 and 1.5, respectively. These results highlight the potential of incorporating temperature as an additional factor to improve the accuracy of plant stress assessments. By integrating temperature with spectral data, the model enhances the ability to monitor plant health dynamically and provides a more comprehensive understanding of how environmental factors influence plant physiology.
Source: Agronomy
Authors: Haolin Yang, Xiaolei Zhang, Yinyan Shi, Lei Wang, Yanyu Chen, Zhongxian Wu, Wei Lu, Xiaochan Wang
Institution: Nanjing Agricultural University
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,137
Funder: Government of Jiangsu Province
OpenAlex ID: W4410590643
