The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder (Alnus glutinosa L.); a diffuse to semi-diffuse-porous species, European beech (Fagus sylvatica L.); and a ring-porous species, sessile oak (Quercus petraea Liebl.). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the “learning process” defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms’ performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining.

Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. The minirhizotron data stem from various wetland types on organic soils. RootDetector showed a high capability to correctly segmenting root pixels in minirhizotron images from field observations (F1 = 0.6044; r² compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average almost 3-times higher root length/cm² per image compared to expert analysts. Analyses with RootDetector save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts.

Semantic segmentation networks are prone to oversegmentation in areas where objects are tightly clustered. In minirhizotron images with densely packed plant root systems this can lead to a failure to separate individual roots, thereby skewing the root length and width measurements.We propose to deal with this problem by adding additional output heads to the segmentation model, one of which is used with a ridge detection algorithm as an intermediate step and a second one that directly estimates root width. With this method we are able to improve detection and width measurements in densely packed roots systems without negative effects on sparse root systems.

Root phenology influences the timing of plant resource acquisition and carbon fluxes into the soil. This is particularly important in fen peatlands, in which peat is primarily formed by roots and rhizomes of vascular plants. However, most fens in Central Europe are drained for agriculture, leading to large carbon losses, and further threatened by increasing frequency and intensity of droughts. Rewetting fens aims to restore the original carbon sink, but how root phenology is affected by drainage and rewetting is largely unknown. We monitored root phenology with minirhizotrons in drained and rewetted fens (alder forest, percolation fen and coastal fen) as well as its soil temperature and water table depth during the 2018 drought. For each fen type, we studied a drained site and a site that was rewetted ~25 years ago, while all the sites studied had been drained for almost a century. Overall, the growing season was longer with rewetting, allowing roots to grow over a longer period in the year and have a higher root production than under drainage. With increasing depth, the growing season shifted to later in time but remained a similar length, and the relative importance of soil temperature for root length changes increased with soil depth. Synthesis and applications. Rewetting extended the growing season of roots, highlighting the importance of phenology in explaining root productivity in peatlands. A longer growing season allows a longer period of carbon sequestration in form of root biomass and promotes the peatlands' carbon sink function, especially through longer growth in deep soil layers. Thus, management practices that focus on rewetting peatland ecosystems are necessary to maintain their function as carbon sinks, particularly under drought conditions, and are a top priority to reduce carbon emissions and address climate change.

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