Abstract
Digital image processing is commonly used in plant health and growth analysis, aiming to improve research efficiency and repeatability. One focus is analysing the morphology of stomata, with the aim to better understand the regulation of gas exchange, its link to photosynthesis and water use and how they are influenced by climatic conditions. Despite the key role played by these cells, their microscopic analysis is largely manual, requiring intricate sample collection, laborious microscope application and the manual operation of a graphical user interface to identify and measure stomata. This research proposes a simple, end-to-end solution which enables automatic analysis of stomata by introducing key changes to imaging techniques, stomata detection as well as stomatal pore area calculation. An optimal procedure was developed for sample collection and imaging by investigating the suitability of using an automatic microscope slide scanner to image nail polish imprints. The use of the slide scanner allows the rapid collection of high-quality images from entire samples with minimal manual effort. A convolutional neural network was used to automatically detect stomata in the input image, achieving average precision, recall and F-score values of 0.79, 0.85, and 0.82 across four plant species. A novel binary segmentation and stomatal cross section analysis method is developed to estimate the pore boundary and calculate the associated area. The pore estimation algorithm correctly identifies stomata pores 73.72% of the time. Ultimately, this research presents a fast and simplified method of stomatal assay generation requiring minimal human intervention, enhancing the speed of acquiring plant health information.
Highlights
The size and density of stomata have been studied as important plants traits since the early 19th century (Banks, 1805)
Recent works suggest that stomatal closure under water stress could result in vein embolism, which can cause the plant water transport system to collapse (Brodribb et al, 2016)
Stomata shape and behaviour are identified as direct indicators of plant health and the surrounding environmental conditions (Beerling and Chaloner 1993a; Beerling and Chaloner 1993b; Sadras et al, 2012)
Summary
The size and density of stomata have been studied as important plants traits since the early 19th century (Banks, 1805). Analysis of stomata is an important aspect of paleoecology; for example, stomatal index (i.e. the ratio between the number of stomata and epidermal cells) of fossil plant cuticles can provide valuable insights into the atmospheric carbon dioxide levels in a given era (Beerling and Chaloner 1993b; Beerling and Royer, 2002). The undulation index (waviness of stomata cell wall), which is physiologically affected by light, correlates well with growing degree-days (GDD), which provides information on seasonal change (Smith et al, 2010; Wagner-Cremer et al, 2010; Wagner-Cremer and Lotter, 2011) in a given period of time. Microscope analysis of stomata plays a major role in present day agriculture as well as modelling climate change over long periods of time
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