High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons

Nair, Richard; Strube, Martin; Hertel, Martin; Kolle, Olaf; Rolo, Víctor; Migliavacca, Mirco

doi:10.1093/jxb/erac427

Cited by 9 publications

(4 citation statements)

References 94 publications

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“…The Majadas de Tiétar (MANIP experiment) MR dataset was collected from 10 custom-built automated MR prototypes in a semi-arid tree-grass ecosystem in central Spain [ 58 , 59 ]. The original dataset used here comprises 250 images (2592 × 2944 pixels; Fig.…”

Section: Methodsmentioning

confidence: 99%

Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models

Baykalov,

Bussmann,

Nair

et al. 2023

Plant Methods

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Background Manual analysis of (mini-)rhizotron (MR) images is tedious. Several methods have been proposed for semantic root segmentation based on homogeneous, single-source MR datasets. Recent advances in deep learning (DL) have enabled automated feature extraction, but comparisons of segmentation accuracy, false positives and transferability are virtually lacking. Here we compare six state-of-the-art methods and propose two improved DL models for semantic root segmentation using a large MR dataset with and without augmented data. We determine the performance of the methods on a homogeneous maize dataset, and a mixed dataset of > 8 species (mixtures), 6 soil types and 4 imaging systems. The generalisation potential of the derived DL models is determined on a distinct, unseen dataset. Results The best performance was achieved by the U-Net models; the more complex the encoder the better the accuracy and generalisation of the model. The heterogeneous mixed MR dataset was a particularly challenging for the non-U-Net techniques. Data augmentation enhanced model performance. We demonstrated the improved performance of deep meta-architectures and feature extractors, and a reduction in the number of false positives. Conclusions Although correction factors are still required to match human labelled root lengths, neural network architectures greatly reduce the time required to compute the root length. The more complex architectures illustrate how future improvements in root segmentation within MR images can be achieved, particularly reaching higher segmentation accuracies and model generalisation when analysing real-world datasets with artefacts—limiting the need for model retraining.

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Section: Methodsmentioning

confidence: 99%

Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models

Baykalov,

Bussmann,

Nair

et al. 2023

Plant Methods

Self Cite

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“…At the organ-structure level, pigments such as chlorophylls or xanthophylls (which change from weeks to months) can control the photosynthetic lightharvesting complexes or reflect leaf flushing or senescence (Latowski et al 2011, Magney et al 2019. The individual plant structure can be divided into depth change from days to weeks, whereas canopy and stem biomass change gradually from weeks to months (Zhang et al 2019, Nair et al 2023. The organization of vegetation at scales ranging from ecosystems to larger scale encompasses more than the three essential components of individual plants mentioned above.…”

Section: Structural and Physiological Characteristics Of Vegetationmentioning

confidence: 99%

Regulation of the global carbon and water cycles through vegetation structural and physiological dynamics

Li,

Duveiller,

Wieneke

et al. 2024

Environ. Res. Lett.

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Vegetation plays an essential role in regulating carbon and water cycles, e.g. by taking up atmospheric CO2 through photosynthesis and by transferring soil water to the atmosphere through transpiration. Vegetation function is shaped by its structure and physiology: vegetation structure is determined by the amount of materials for plants and how it is organised in space and time, while vegetation physiology controls the instantaneous response of vegetation function to environmental conditions. Recognizing and disentangling these aspects of vegetation is key to understanding and predicting the response of the terrestrial biosphere to global change. This is now possible, as comprehensive measurements from Earth observations, both from satellites and the ground, provide invaluable data and information. This review introduces and describes vegetation structure and physiology, and summarises, compares, and contextualises recent literature to illustrate the state of the art in monitoring vegetation dynamics, quantifying large-scale vegetation physiology, and investigating vegetation regulation on the changes of global carbon and water fluxes. This includes results from remote sensing, in-situ measurements, and model simulations, used either to study the response of vegetation structure and physiology to global change, or to study the feedback of vegetation to global carbon and water cycles. We find that observation-based work is underrepresented compared with model-based studies. We therefore advocate further work to make better use of remote sensing and in-situ measurements, as they promote the understanding of vegetation dynamics from a fundamental data-driven perspective. We highlight the usefulness of novel and increasing satellite remote sensing data to comprehensively investigate the structural and physiological dynamics of vegetation on the global scale, and to infer their influence on the land carbon sink and terrestrial evaporation. We argue that field campaigns can and should complement large-scale analyses together with fine spatio-temporal resolution satellite remote sensing to infer relevant ecosystem-scale processes.

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“…Many modern approaches to this problem rely on machine learning (ML) methods to reach from regional or global scales (Lapeyre et al, 2020;Poggio et al, 2021)). But in EMEs and similar contexts, ML also offers another complementary tool: data streams which are either difficult to capture or process (Nair et al, 2023) can be gap filled or interpreted with higher confidence and representativeness. Further development of techniques currently only possible at laboratory or homogenous agricultural scale may allow dynamic sub annual time series of difficult to measure parameters such as photosynthetic capacity (Heckmann et al, 2017), nutrient pools both in biomass and available in soil (Tan et al, 2022), or phenological dynamics beyond leaves, especially those belowground (Wang et al, 2022).…”

Section: New Data For Eme-model Synthesismentioning

confidence: 99%

Ideas and perspectives: Beyond model evaluation – combining experiments and models to advance terrestrial ecosystem science

Caldararu

Rolo

Stocker

et al. 2023

Preprint

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Abstract. Ecosystem manipulative experiments are one of the most powerful tools to understand terrestrial ecosystem responses to global change because they measure real responses in real ecosystems. However, their scope is limited in space and time due to cost and labour intensity. This makes generalising results from such experiments difficult, which creates a conceptual gap between local scale process understanding and global scale future predictions. Recent efforts have seen results from such experiments used in combination with dynamic global vegetation models, most commonly to evaluate model predictions under global change drivers. However, there is much more potential in combining models and experiments. Here, we discuss the value and potential of a workflow for using ecosystem experiments together with process-based models to enhance the potential of both. We suggest that models can be used prior to the start of an experiment to generate hypotheses, identify data needs and in general guide experimental design. Models, when adequately constrained with observations, can also predict variables which are difficult to measure frequently or at all, and together with the data can provide a more complete picture of ecosystem states. Finally, models can be used to help generalise the experimental results in space and time, by providing a framework in which process understanding derived from site-level experiments can be incorporated. We also discuss the potential for using manipulative experiments together with models in formalised model-data integration frameworks for parameter estimation and model selection, a path made possible by the increasing number of ecosystem experiments and diverse observation streams. The ideas presented here can provide a roadmap to future experiment - model studies.

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High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons

Cited by 9 publications

References 94 publications

Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models

Semantic segmentation of plant roots from RGB (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models

Regulation of the global carbon and water cycles through vegetation structural and physiological dynamics

Ideas and perspectives: Beyond model evaluation – combining experiments and models to advance terrestrial ecosystem science

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