Multi-Year Leaf-Level Response to Sub-Ambient and Elevated Experimental CO2 in Betula nana

A generalised approach for high-throughput instance segmentation of stomata in microscope images

BACKGROUND

Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level.

RESULTS

The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach.

CONCLUSIONS

The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.

Stomatal frequency of Quercus glauca from three material sources shows the same inverse response to atmospheric pCO2

BACKGROUND AND AIMS

The inverse correlation between atmospheric CO2 partial pressure (pCO2) and stomatal frequency in many plants has been widely used to estimate palaeo-CO2 levels. However, apparent discrepancies exist among the obtained estimates. This study attempts to find a potential proxy for palaeo-CO2 concentrations by analysing the stomatal frequency of Quercus glauca (section Cyclobalanopsis, Fagaceae), a dominant species in East Asian sub-tropical forests with abundant fossil relatives.

METHODS

Stomatal frequencies of Q. glauca from three material sources were analysed: seedlings grown in four climatic chambers with elevated CO2 ranging from 400 to 1300 ppm; extant samples collected from 14 field sites at altitudes ranging from 142 to 1555 m; and 18 herbarium specimens collected between 1930 and 2011. Stomatal frequency-pCO2 correlations were determined using samples from these three sources.

KEY RESULTS

An inverse correlation between stomatal frequency and pCO2 was found for Q. glauca through cross-validation of the three material sources. The combined calibration curves integrating data of extant altitudinal samples and historical herbarium specimens improved the reliability and accuracy of the curves. However, materials in the climatic chambers exhibited a weak response and relatively high stomatal frequency possibly due to insufficient treatment time.

CONCLUSIONS

A new inverse stomatal frequency-pCO2 correlation for Q. glauca was determined using samples from three sources. These three material types show the same response, indicating that Q. glauca is sensitive to atmospheric pCO2 and is an ideal proxy for palaeo-CO2 levels. Quercus glauca is a nearest living relative (NLR) of section Cyclobalanopsis fossils, which are widely distributed in the strata of East Asia ranging from the Eocene to Pliocene, thereby providing excellent materials to reconstruct the atmospheric CO2 concentration history of the Cenozoic. Quercus glauca will add to the variety of proxies that can be widely used in addition to Ginkgo and Metasequoia.