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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.
Hiranya Jayakody; Paul Petrie; Hugo Jan de Boer; Mark Whitty. A generalised approach for high-throughput instance segmentation of stomata in microscope images. Plant Methods 2021, 17, 1 -13.
AMA StyleHiranya Jayakody, Paul Petrie, Hugo Jan de Boer, Mark Whitty. A generalised approach for high-throughput instance segmentation of stomata in microscope images. Plant Methods. 2021; 17 (1):1-13.
Chicago/Turabian StyleHiranya Jayakody; Paul Petrie; Hugo Jan de Boer; Mark Whitty. 2021. "A generalised approach for high-throughput instance segmentation of stomata in microscope images." Plant Methods 17, no. 1: 1-13.
Global dietary consumption strongly determines agricultural land requirements. Yet, it is currently difficult for individual consumers to quantify the environmental impact of their individual diet. One relatively easy to understand metric is the Human Appropriation of Land for Food (HALF) index. The HALF index expresses the global land area percentage required for food production were the global population to consume one specific diet. Calculation of the HALF index is not trivial, making the index not suitable for individual consumers to assess their personal diet. The aim of this research is to develop and test a new method to calculate a personalized HALF index based on a limited set of multiple-choice questions that can be answered by a typical consumer. Considering the sensitivity of the original HALF index, we developed a set of ten multiple-choice questions that focus on the type and quantity of consumed animal products in addition to staple foods and overall consumption quantity. To illustrate a potential implementation, we present our question-based HALF index calculator in the form of an online graphical user interface. Across a sample of 23 country-specific diets, the question-based HALF index closely matches the original HALF index with a regression slope of near unity (r2 = 0.94, p < 0.001). Our results indicate that the question-based HALF index can be used by individual consumers to quantify the consequences of their dietary choices on land use for agriculture.
Marije Hoff; Hugo De Boer. A Question-Based Method to Calculate the Human Appropriation of Land for Food (HALF) Index. Sustainability 2020, 12, 10597 .
AMA StyleMarije Hoff, Hugo De Boer. A Question-Based Method to Calculate the Human Appropriation of Land for Food (HALF) Index. Sustainability. 2020; 12 (24):10597.
Chicago/Turabian StyleMarije Hoff; Hugo De Boer. 2020. "A Question-Based Method to Calculate the Human Appropriation of Land for Food (HALF) Index." Sustainability 12, no. 24: 10597.
The revolutionary rise of broad-leaved (flowering) angiosperm plant species during the Cretaceous initiated a global ecological transformation towards modern biodiversity. Still, the mechanisms involved in this angiosperm radiation remain enigmatic. Here we show that the period of rapid angiosperm evolution initiated after the leaf interior (post venous) transport path length for water was reduced beyond the leaf interior transport path length for CO2 at a critical leaf vein density of 2.5–5 mm mm−2. Data and our modelling approaches indicate that surpassing this critical vein density was a pivotal moment in leaf evolution that enabled evolving angiosperms to profit from developing leaves with more and smaller stomata in terms of higher carbon returns from equal water loss. Surpassing the critical vein density may therefore have facilitated evolving angiosperms to develop leaves with higher gas exchange capacities required to adapt to the Cretaceous CO2 decline and outcompete previously dominant coniferous species in the upper canopy.
Hugo Jan De Boer; Maarten B. Eppinga; Martin Wassen; Stefan Dekker. A critical transition in leaf evolution facilitated the Cretaceous angiosperm revolution. Nature Communications 2012, 3, 1221 .
AMA StyleHugo Jan De Boer, Maarten B. Eppinga, Martin Wassen, Stefan Dekker. A critical transition in leaf evolution facilitated the Cretaceous angiosperm revolution. Nature Communications. 2012; 3 (1):1221.
Chicago/Turabian StyleHugo Jan De Boer; Maarten B. Eppinga; Martin Wassen; Stefan Dekker. 2012. "A critical transition in leaf evolution facilitated the Cretaceous angiosperm revolution." Nature Communications 3, no. 1: 1221.