Engineering topics

Matamala, Roser

Publications and source records attributed to Matamala, Roser.

At least 901 records · Page 50

DOE OSTI2023

An interlaboratory comparison of mid-infrared spectra acquisition: Instruments and procedures matter

Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSCKSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e. g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures.

58 GEOSCIENCES↗

DOE OSTI2023

Evaluation of eastern gamagrass as dual-purpose complementary bioenergy and forage feedstock to switchgrass

Switchgrass (SG) is considered a model bioenergy crop and a warm-season perennial grass (WSPG) that traditionally served as forage feedstock in the United States. To avoid the sole dependence on SG for bioenergy production, evaluation of other crops to diversify the pool of feedstock is needed. We conducted a 3-year field experiment evaluating eastern gamagrass (GG), another WSPG, as complementary feedstock to SG in one- and two-cut systems, with or without intercropping with crimson clover or hairy vetch, and under different nitrogen (N) application rates. Our results showed that GG generally produced lower biomass (by 29.5%), theoretical ethanol potential (TEP, by 2.8%), and theoretical ethanol yield (TEY, by 32.9%) than corresponding SG under the same conditions. However, forage quality measures, namely acid detergent fiber (ADF), crude protein (CP), and elements P, K, Ca, and Mg were significantly higher in GG than those in SG. Nitrogen fertilizer significantly enhanced biomass (by 1.54 Mg ha -1 ), lignin content (by 2.10 g kg -1 ), and TEY (787.12 L ha -1 ) in the WSPGs compared to unfertilized treatments. Intercropping with crimson clover or hairy vetch did not significantly increase biomass of the WSPGs, or TEP and TEY in unfertilized plots. This study demonstrated that GG can serve as a complementary crop to SG and could be used as a dual-purpose crop for bioenergy and forage feedstock in farmers' rotations.

09 BIOMASS FUELS↗

DOE OSTI2023

Root litter decomposition rates and impacts of drought are regulated by ecosystem legacy

In grassland ecosystems, about two-thirds of productivity is in roots, and therefore roots constitute a major soil organic matter input. However, influences on the rate of root litter decomposition remain unresolved, especially in the context of land-use conversion and climate change. Ecosystem legacy can affect root decomposition rates via impacts on substrate chemistry and soil environments, and this may manifest in responses of decomposition to changing temperature and moisture. Here we investigate the impacts of anthropogenic legacy effects and moderate drought on root litter decomposition rates in five "Land Use History Types": crop fields, cow pastures, remnant tallgrass prairie, and prairie restored from crop fields and pastures. We measured root losses of mass, carbon, and nitrogen over 11 months. Further, soil bulk density was unimportant for decomposition, but soil moisture content and temperature were relevant for decomposition rates while time since disturbance predicted decomposition initiation times. Furthermore, soil moisture and temperature dynamics alone could not explain the responses of decomposition rates to drought, which were positively correlated to time since disturbance. Our findings suggest that anthropogenic legacy impacts decomposition rates in grasslands, especially when soil moisture and temperature dynamics are substantially altered, and mediates soil community responses to drought.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Perennial grass root system specializes for multiple resource acquisitions with differential elongation and branching patterns

Roots optimize the acquisition of limited soil resources, but relationships between root forms and functions have often been assumed rather than demonstrated. Furthermore, how root systems co-specialize for multiple resource acquisitions is unclear. Theory suggests that trade-offs exist for the acquisition of different resource types, such as water and certain nutrients. Measurements used to describe the acquisition of different resources should then account for differential root responses within a single system. To demonstrate this, we grew Panicum virgatum in split-root systems that vertically partitioned high water availability from nutrient availability so that root systems must absorb the resources separately to fully meet plant demands. We evaluated root elongation, surface area, and branching, and we characterized traits using an order-based classification scheme. Plants allocated approximately 3/4th of primary root length towards water acquisition, whereas lateral branches were progressively allocated towards nutrients. However, root elongation rates, specific root length, and mass fraction were similar. Our results support the existence of differential root functioning within perennial grasses. Similar responses have been recorded in many plant functional types suggesting a fundamental relationship. Root responses to resource availability can be incorporated into root growth models via maximum root length and branching interval parameters.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Local adaptation of switchgrass drives trait relations to yield and differential responses to climate and soil environments

Abstract Switchgrass, a potential biofuel crop, is a genetically diverse species with phenotypic plasticity enabling it to grow in a range of environments. Two primary divergent ecotypes, uplands and lowlands, exhibit trait combinations representative of acquisitive and conservative growth allocation strategies, respectively. Whether these ecotypes respond differently to various types of environmental drivers remains unclear but is crucial to understanding how switchgrass varieties will respond to climate change. We grew two upland, two lowland, and two intermediate/hybrid cultivars of switchgrass at three sites along a latitudinal gradient in the central United States. Over a 4‐year period, we measured plant functional traits and biomass yields and evaluated genotype‐by‐environment (G × E) interaction effects by analyzing switchgrass responses to soil and climate variables. We found substantial evidence of G × E interactions on biomass yield, primarily due to deviations in the response of the southern lowland cultivar Alamo, which produced more biomass in hotter and drier environments relative to other cultivars. While lowland cultivars had the highest potential for yield, their yields were more variable year‐to‐year compared to other cultivars, suggesting greater sensitivity to environmental perturbations. Models comparing soil and climate principal components as explanatory variables revealed soil properties, especially nutrients, to be most effective at predicting switchgrass biomass yield. Also, positive correlations between biomass yield and conservative plant traits, such as high stem mass and tiller height, became stronger at lower latitudes where the climate is hotter and drier, regardless of ecotype. Lowland cultivars, however, showed a greater predisposition to exhibit these conservative traits. These results suggest switchgrass trait allocation trade‐offs that prioritize aboveground biomass production are more tightly associated in hot, dry environments and that lowland cultivars may exhibit a more specialized strategy relative to other cultivars. Altogether, this research provides essential knowledge for improving the viability of switchgrass as a biofuel crop.

09 BIOMASS FUELS↗

DOE OSTI2023

Earlier Ecological Drought Detection by Involving the Interaction of Phenology and Eco-Physiological Function

Meteorological drought indices like the Standardized Precipitation Evaporation Index (SPEI) are frequently used to diagnose “ecological drought,” despite the fact that they were not explicitly designed for this purpose. More recently developed indices like the Evaporative Stress Index (ESI), which is based on the degree of coupling between actual to potential evapotranspiration, may better capture dynamic plant response to moisture limitations. However, the skill of these indices at describing plant water stress is rarely evaluated at sub-seasonal timescales over which drought evolves. Moreover, it remains unclear how variability in phenological timing impacts and complicates early drought detection. Here, we compared the ability of ESI and SPEI to reflect the dynamics of ecological drought in forests and grasslands, based on anomalies of Gross Primary Productivity (GPP), surface conductance (G s , a proxy for stomatal conductance), soil moisture, and vapor pressure deficit. ESI performed better than SPEI in capturing the dynamics of GPP and G s , but still missed early ecological drought signals due to biases linked to earlier onset of spring leaf development. Thus, we developed a modified variant of the ESI (ESI LAI ) that accounts for the complicating effects of phenological shifts in leaf area index (LAI). The ESI LAI detected drought onset up to 7–10 weeks earlier than SPEI and ESI. Additionally, drought onset dates determined from ESI LAI are close to (±2 weeks) the dates determined from LAI-corrected anomalies of G s , and GPP, as well as the onset dates of soil water deficit and atmospheric aridity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Applying NIR and MIR spectroscopy for C and soil property prediction in northern cold-region ecosystems. Which approach works better?

Here, developing reliable predictions of soil attributes is necessary to understand northern cold-region climate-soil feedback. Calibration models using near-infrared (NIR) and mid-infrared (MIR) spectroscopy were developed to predict eight commonly measured soil properties for 119 soil samples representing a range of vegetation types, parent materials, and soil types spanning >23° of latitude from southeast Alaska to the Canadian high Arctic. In order to obtain a more accurate prediction, this study compared the performance of linear and non-linear calibration techniques, including lasso regression (Lasso), support vector machine (SVM), random forest (RF) and classic partial least squares (PLS) to predict different soil properties of these soils. Comparing the four models, we noticed that their performance was quite similar for MIR overall, while NIR achieved better results with a PLS model for our dataset. PLS coupled with MIR showed a better performance for soil parameters, such as total organic carbon (TOC), total nitrogen (TN), cation exchange capacity (CEC) and clay (R-squared of 0.9, 0.81, 0.80, and 0.84) when compared with NIR (R-squared of 0.85, 0.72, 0.81 and 0.68). However, using either MIR or NIR spectroscopy, PLS predictions for bulk density (BD) and sand content were not accurate. The variable importance analysis based on the PLS model successfully estimated the relative contribution of wavelengths influencing soil property predictions most. Overall, TOC, TN, CEC and clay mineral predictions are closely related to the occurrence of specific spectral bands in the MIR region. For example, wavelengths at 2978 and 1761 cm -1 for TOC and TN, as well as at 3064 cm -1 for CEC, were selected as the most influential predictor variables. We demonstrated that MIR spectroscopy is a powerful tool for more extensive monitoring in soils of the northern cold climate region; however, NIR could be utilized for rapid estimates when the highest accuracy is not essential.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Rays for Roots - Integrating Backscatter X-Ray Phenotyping, Modeling and Genetics to Increase Carbon Sequestration and Switchgrass Resource Use (Final Report)

To increase carbon (C) deposition in the soil and enhance crop resource use efficiency, characterizing root form and function is essential. Several root and soil traits have been linked to increased root-to-soil C transfer. Technology that could provide high-resolution characterization of many of these traits in field conditions would revolutionize our ability to study and understand how to increase C sequestration. In this effort, we developed an initial early prototype backscatter X-ray system for non-destructive imaging of root traits. We collected initial backscatter X-ray data in field and lab settings and carried out early analysis of these data. Along with this prototype, we also developed a suite of root phenotyping approaches including advanced minirhizotron image analysis, soil core imaging, and mesocosm imaging. Minirhizotron (MR) tubes are clear tubes inserted into the soil in the field and used to image roots and the surrounding soil. Our team has developed deep learning-based methods that can segment roots from soil that can learn from imprecise image-level labels. The ability to learn or fine-tune our deep learning algorithms from image-level labels allows easier and faster application of these approaches to new locations and new plant species. We have successfully implemented and applied our MR analysis approaches to thousands of switchgrass MR images collected across geographical regions. An advantage of MR imaging is the ability to collect root and soil images over time. Our soil core analysis included collecting hundreds of soil core samples from harvested switchgrass fields and imaging these cores with both X-ray CT and backscatter X-ray imaging. Initial segmentation approaches for the X-ray CT images of these cores have been developed and applied. An advantage of soil core analysis is that it preserves the three-dimensional structures of the roots and soil in the core collected. Our group also developed photogrammetry-based mesocosm root imaging and phenotyping approaches. In this approach, a plant was grown in a large mesocosm with a three-dimensional grid of thin supporting lines inserted throughout the mesocosm. After the plant (and, correspondingly, the root architecture is grown and established) the soil media was removed and the supporting lines approximately preserved the three-dimensional root architecture. Then, we applied photogrammetry techniques to create a three-dimensional digital representation of the root architecture for which we developed analysis algorithms including skeletonization. We carried out our phenotyping development with powerful switchgrass resources and physiological and agroecosystem modeling to deliver novel technology. This project contributes to multiple ARPA-E missions including reduction of foreign imports of energy, reduction of energy-related emissions including greenhouse gases, and ensuring that the United States maintains a technological lead in developing and deploying advanced energy technology. Furthermore, the developed tools could transform public and private plant breeding and could be broadly applicable to other crops and, potentially, other application areas. Our team of engineers, plant and soil scientists, and modelers i) developed an early prototype backscatter X-ray platform that can operate in field conditions; ii) developed a suite of root phenotyping and characterization approaches as described above; iii) developed and carried out plant biology and physiology roots studies and; iv) developed and implemented mechanistic physiological modeling.

42 ENGINEERING↗

DOE OSTI2022

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R 2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m 3 /m 3 ). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

A generalist–specialist trade-off between switchgrass cytotypes impacts climate adaptation and geographic range

Polyploidy results from whole-genome duplication and is a unique form of heritable variation with pronounced evolutionary implications. Different ploidy levels, or cytotypes, can exist within a single species, and such systems provide an opportunity to assess how ploidy variation alters phenotypic novelty, adaptability, and fitness, which can, in turn, drive the development of unique ecological niches that promote the coexistence of multiple cytotypes. Switchgrass, Panicum virgatum, is a widespread, perennial C4 grass in North America with multiple naturally occurring cytotypes, primarily tetraploids (4×) and octoploids (8×). Using a combination of genomic, quantitative genetic, landscape, and niche modeling approaches, we detect divergent levels of genetic admixture, evidence of niche differentiation, and differential environmental sensitivity between switchgrass cytotypes. Taken together, these findings support a generalist (8×)–specialist (4×) trade-off. Our results indicate that the 8× represent a unique combination of genetic variation that has allowed the expansion of switchgrass’ ecological niche and thus putatively represents a valuable breeding resource.

09 BIOMASS FUELS↗

DOE OSTI2022

Spatially Averaged Ice Contents of Ice-Wedge Polygon Cross-Sections to 3-m Depth, July 2013, Utqiagvik, Alaska

This dataset contains spatially averaged estimates of ice content (volume percent) for two-dimensional cross-sectional profiles (from trough center to trough center to a depth of 3 meters) of low- and flat-centered ice-wedge polygons (three of each type) located near Utqiagvik, Alaska. A combination of soil pits, trenches, and cores were used to describe, sample, and map the cross-section stratigraphy of soil horizons and ice wedges for each polygon at 6 depth intervals. Observed soil horizons were assigned to four types with increasing amounts of organic components (mineral, mineral/organic, organic/mineral, and organic). The average ice contents of each soil horizon type below the permafrost boundary and wedge ice were weighted by their cross-sectional area fractions to calculate spatially averaged estimates of ice content for each polygon. In the active layer, spatially averaged estimates of volumetric water contents were similarly determined and reported here as “ice” content to enable estimates of the soil’s structurally competent porosity and excess ice in permafrost layers. In this dataset, the file AK13_ice_contents.csv includes cross-sectional area fractions and spatially averaged ice contents for soil layers and horizon types at the six depth intervals. The file AK13_permafrost_ice_fractions.csv contains the calculated partitioning of ice contents into pore ice and excess ice fractions for the three permafrost-dominated depth intervals. In addition, there is a data dictionary file for each of these data files and a file-level metadata file. These data were generated by the Department of Energy’s Soil Carbon Response to Environmental Change Scientific Focus Area and were used as inputs to model simulations examining the consequences of thaw-affected subsidence and microtopography change on active layer thickness of low-relief polygonal tundra landscapes in a warming Arctic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.

Xu, Weihuang↗

DOE OSTI2021

Data-Driven Exploration of Climate Attractor Manifolds For Long-Term Predictability

Focal Area: This white paper responds to Focal Area 3. We seek to gain insight into decadal-scale climate predictability by applying novel manifold-finding probabilistic AI techniques to the complex data produced by Earth System models (ESMs) such as E3SM. The associated portfolio of research activities leverages DOE’s asset mix of HPC platforms, climate expertise, climate simulation codes, and AI expertise. Science Challenge: Climate and climate models are dynamical systems exhibiting properties that are interpretable through chaos theory. The theory contains an important concept that is relevant to multi-decade-scale climate prediction: a chaotic attractor. While the space containing all the possible states of the Earth’s atmosphere and ocean, the possible weather, is large, the realized states tend to stay near the smaller-dimensioned attractor. This behavior is responsible for the “order behind the irregularity” [1] of climate phenomena. Climate change can be thought of as a change in the properties of the attractor, and predicting the climate over years to decades is equivalent to predicting how those properties will change. To date, the attractor has been a useful conceptual tool, but has not been amenable to direct characterization. A new development is the advent of efficient high-dimensional manifold-finding probabilistic AI techniques, which permit a data-driven characterization of the ESM attractor and its probability distribution over weather states. Such a characterization would result in a natural dimensional reduction — a “non-linear Principal Components Analysis (PCA) adapted to climate simulation data” — leading to important advances in scenario-based long-term climate prediction, long-term prediction of water cycle extremes, ESM verification, inter-model comparison, and process model development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks

Heterogeneous environmental controls on permafrost region soil organic carbon stocks can now be quantified with spatial modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Predictability and feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model

This white paper responds to Focal Area 2. We seek to build predictive models of leaf and surface conductance of water by implementing deep learning (DL) data assimilation techniques. These new models would then be implemented in existing Land Surface Models (LSMs) and Earth System models (ESMs), generating novel water cycle feedbacks. In doing so, we would improve predictability of expected changes in land precipitation, soil moisture, and vegetation dynamics in the long-term, and the role of land cover on the impacts and feedbacks of extreme weather events in the short-term

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Using AI to build a hydrobiogeochemical soil model

Soil water content is a function of inputs from precipitation and outputs via evaporation, transpiration, lateral flow, and vertical percolation, and is sensitive to biogeochemical processes. As such, soils serve as an ideal integrator of atmospheric, hydrological, and biogeochemical processes affecting the water cycle. In addition, soil water retention capacity, infiltration rates, and hydraulic conductivity can buffer or exacerbate the effects of extreme precipitation events (e.g., flooding, runoff, subsurface transport, erosion, greenhouse gas emissions) and mitigate the impact of droughts and heat waves on land systems (e.g., fire, crop failure). However, integrating water cycle measurements spanning different land atmosphere compartments across scales is a fundamental barrier for numerical model predictability. A significant challenge is that each domain (soil, hydrology, biology, and atmosphere) typically collects different sets of data at different temporal and spatial frequencies/scales, and even different dimensionalities (2D vs 3D). To implement soil as an integrator of the water cycle in land models, we suggest that novel machine learning (ML) tools can be developed to effectively simulate complex landscapes across various domains and scales, extended to regions with sparse or no data. The ultimate goals are to improve predictive understanding of land-atmosphere interactions and to extend the predictability of current Earth System Models (ESMs) through better integration of hydrological and biogeochemical data. We envision a framework in which: (1) ML-aided data reconstructions enable the merger of data sources into a unified geospatial product; (2) automated detection techniques are used to improve the knowledge of complex soil processes and interactions; and (3) this knowledge is leveraged and incorporated into models through AI-based emulators to distinctly connect the land and atmospheric compartments of the water cycle in models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Genomic mechanisms of climate adaptation in polyploid bioenergy switchgrass

Long-term climate change and periodic environmental extremes threaten food and fuel security and global crop productivity. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation—knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass ( Panicum virgatum ). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate–gene–biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene–trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.

09 BIOMASS FUELS↗

DOE OSTI2021

Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM

We present a multiple instance learning class activation map (MIL-CAM) approach for pixel-level minirhizotron image segmentation given weak image-level labels. Minirhizotrons are used to image plant roots in situ. Minirhizotron imagery is often composed of soil containing a few long and thin root objects of small diameter. The roots prove to be challenging for existing semantic image segmentation methods to discriminate. In addition to learning from weak labels, our proposed MILCAM approach re-weights the root versus soil pixels during analysis for improved performance due to the heavy imbalance between soil and root pixels. Furthermore, the proposed approach outperforms other attention map and multiple instance learning methods for localization of root objects in minirhizotron imagery.

97 MATHEMATICS AND COMPUTING↗