A deep learning-integrated phenotyping pipeline for vascular bundle phenotypes and its application in evaluating sap flow in the maize stem

Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography(CT) images of stem internodes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy(R^(2)) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.

Effect of calcination temperature on the pozzolanic activity of maize straw stem ash treated with portlandite solution

The effect of calcination temperature on the pozzolanic activity of maize straw stem ash(MSSA)was evaluated.The MSSA samples calcined at temperature values of 500,700,and 850℃ were dissolved in portlandite solution for 6 h,thereby obtaining residual samples.The MSSA and MSSA residual samples were analyzed using Fourier transform infrared spectroscopy,X-ray powder diffraction scanning electron microscopy,and X-ray photoelectron spectroscopy to determine vibration bonds,minerals,microstructures,and Si 2p transformation behavior.The conductivity,pH value,and loss of conductivity with dissolving time of the MSSA-portlandite mixed solution were also determined.The main oxide composition of MSSA was silica and potassium oxide.The dissolution of the Si^(4+) content of MSSA at 500℃ was higher than those of the other calcination temperatures.The conductivity and loss of conductivity of MSSA at 700℃ were higher than those of the other calcination temperatures at a particular dissolving time due to the higher KCl content in MSSA at 700℃.C-S-H was easily identified in MSSA samples using X-ray powder diffraction,and small cubic and nearly spherical particles of C-S-H were found in the MSSA residual samples.In conclusion,the optimum calcination temperature of MSSA having the best pozzolanic activity is 500℃,but excessive agglomeration must be prevented.