Search

Abstract Background Senescence is a complex developmental process that is regulated by a multitude of environmental, genetic, and physiological factors. Optimizing the timing and dynamics of this process has the potential to significantly impact crop adaptation to future climates and for maintaining grain yield and quality, particularly under terminal stress. Accurately capturing the dynamics of senescence and isolating the genetic variance component requires frequent assessment as well as intense field testing. Here, we evaluated and compared the potential of temporally dense drone-based RGB- and multispectral image sequences for this purpose. Regular measurements were made throughout the grain filling phase for more than 600 winter wheat genotypes across three experiments in a high-yielding environment of temperate Europe. At the plot level, multispectral and RGB indices were extracted, and time series were modelled using different parametric and semi-parametric models. The capability of these approaches to track senescence was evaluated based on estimated model parameters, with corresponding parameters derived from repeated visual scorings as a reference. This approach represents the need for remote-sensing based proxies that capture the entire process, from the onset to the conclusion of senescence, as well as the rate of the progression. Results Our results indicated the efficacy of both RGB and multispectral reflectance indices in monitoring senescence dynamics and accurately identifying key temporal parameters characterizing this phase, comparable to more sophisticated proximal sensing techniques that offer limited throughput. Correlation coefficients of up to 0.8 were observed between multispectral (NDVIred668-index) and visual scoring, respectively 0.9 between RGB (ExGR-index) and visual scoring. Sub-sampling of measurement events demonstrated that the timing and frequency of measurements were highly influential, arguably even more than the choice of sensor. Conclusions Remote-sensing based proxies derived from both RGB and multispectral sensors can capture the senescence process accurately. The sub-sampling emphasized the importance of timely and frequent assessments, but also highlighted the need for robust methods that enable such frequent assessments to be made under variable environmental conditions. The proposed measurement and data processing strategies can improve the measurement and understanding of senescence dynamics, facilitating adaptive crop breeding strategies in the context of climate change.

Abstract Background Quantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions. Results We present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR. Conclusions The proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.

A solid understanding of plant growth is crucial for maintaining future crop productivity in the face of climate change. Remote sensing of crop functional traits using optical satellite imagery provides a valuable tool for determining effective management techniques that reduce risk and enhance agroecosystem resilience. However, atmospheric disturbances limit the availability of imagery, leading to gaps and noise in the trait time series. Thus, accurate crop growth modelling necessitates time series reconstruction methods. One promising approach is incorporating physiological priors, such as the influence of environmental variables like air temperature on plant growth. We propose a novel method that combines Sentinel-2 Green Leaf Area Index (GLAI) observations with three temperature driven reconstruction techniques that describe the physiological relationship between growth and temperature in winter wheat. By employing a probabilistic ensemble Kalman filtering data assimilation scheme, we can integrate high-resolution air temperature data and satellite imagery while quantifying uncertainties. Our results suggest that assimilating Sentinel-2 GLAI and temperature-response-based growth rates allows for the reconstruction of physiologically meaningful GLAI time series. Moreover, our proposed method outperforms state-of-the-art approaches based on logistic functions in terms of physiological plausibility, fitting requirements, and representation of high GLAI values. Our approach requires fewer satellite observations compared to traditional remote sensing time series algorithms, making it suitable for agricultural areas with high cloud cover. Consequently, it has significant potential to improve the reliability of crop productivity assessments based on remote sensing data.

The use of dendrometers to measure the stem diameter (SD) of trees provides information about their actual water stress levels. The Scholander chamber is currently the gold standard for measuring stem water potential and thus for quantifying the water status of trees, despite being a laborious method, especially for apple trees. The aim of this study was to analyze dendrometer data to assess the water stress in drip-irrigated ‘Gala’ apple trees (Malus x domestica Borkh). A trial was performed in Grens, Switzerland, during the 2022 season. Three different irrigation treatments were applied (T1 = 100 % soil moisture-based irrigation, T2 = 30 % less than T1, and T3 = without irrigation). The irrigation treatments notably affected fruit production. Trees in T3 exhibited a significant yield loss of 26 %, whereas treatment T2 yielded 13 % less than the reference treatment T1. Although daily changes in the SD correlated with vapor pressure deficit (VPD) (r2 = 0.84), limiting the amount of water available to the plant disrupted this correlation (r2 = 0.27), with stem water potentials dropping by −1.7 MPa and a noticeable shift in stem movement (shrinking/expanding). Finally, we developed a suitable linear regression model that compared the shift in slope and offset of regression lines fitted for SD and VPD during initial non-stressed conditions (reference) with those under stressed conditions in order to estimate stem water potential. By integrating dendrometer parameters with VPD, the model effectively predicted stem water potential values. These findings suggest that dendrometers are effective indicators of water stress in apple trees. Further refinement of the model in field conditions could enhance the use of these sensors for irrigation management, providing more precise guidance on the timing and amount of water applied.

Abstract Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availability of accurate spatial information on crop growth, vigor, and health status at large scales. Challenges persist particularly in interpreting remote sensing signals within commercial crop production due to the variability in canopy appearance resulting from diverse factors. Recently, high-resolution imagery captured from unmanned aerial vehicles has shown significant potential for calibrating and validating methods for remote sensing signal interpretation. We present a comprehensive multi-scale image dataset encompassing 35,000 high-resolution aerial RGB images, ground-based imagery, and Sentinel-2 satellite data from nine on-farm wheat fields in Switzerland. We provide geo-referenced orthomosaics, digital elevation models, and shapefiles, enabling detailed analysis of field characteristics across the growing season. In combination with rich meta data such as detailed records of crop husbandry, crop phenology, and yield maps, this data set enables key challenges in remote sensing-based trait estimation and precision agriculture to be addressed.

This study evaluates the efficiency of an automated irrigation system using dendrometer sensors in apple orchards and compares it to a standard grower commercial irrigation approach based on soil moisture sensors. An algorithm was developed to balance daily stem shrinkage (water loss) and expansion (water uptake), aiming for a stable dendrometer signal. The dendrometer-based irrigation system (DENDRO) significantly reduced water use—by 38 % in 2022 and more than 45 % in 2023—while maintaining yields similar to those of the soil moisture-based system (SOIL). The DENDRO responded quite well to plant water stress, as indicated by stem water potential (WP). Although the tested algorithm proved to be efficient, the results also indicated the potential for optimization. One example is shortening the averaging period used to calculate stem recovery (RΔ). The SOIL method was effective in fruit production but proved to be less efficient in reflecting water needs. Alternative approaches, including FAO-based irrigation (FAO) and a linear regression model combining dendrometer parameters and climatic data (MODEL), were also assessed. The FAO method tended to overestimate water requirements, while the MODEL method showed promise for dynamic irrigation adjustment based on climatic conditions and dendrometer values. Overall, the findings highlight the advantage of integrating plant-based sensors, such as dendrometers, for more precise irrigation management in orchard systems, leading to more sustainable water use without compromising crop yield.

The acceleration of materials discovery has gained paramount importance due to its potential to overcome constraints in emerging technologies. Extensive exploration has been undertaken into three pivotal approaches: combinatorial synthesis, high-throughput characterization, and computational techniques, all aimed at unveiling new materials. This review article delves into recent progress in these domains. Combinatorial synthesis, especially in the development of thin-film materials libraries, emerges as a potent method for efficiently generating comprehensive multinary materials systems and composition gradients spanning the entire spectrum of required compositions. High-throughput characterization techniques play a pivotal role in assessing the compositional, structural, and functional attributes of materials within these libraries, yielding multidimensional datasets. Concurrently, recent advancements in computational materials science have notably expedited the discovery process by enabling high-throughput calculations and simulations of potential materials systems. These collective endeavors foster a more robust correlation between composition, processing, structure, and properties, facilitating the forecast and design of future materials through data-driven materials discovery. This approach allows for efficient optimization of newly identified materials. Furthermore, materials informatics, an integral element of this process, plays a crucial role in managing and extracting valuable insights from the vast data generated during materials discovery.

Abstract Switzerland’s livestock production causes high environmental costs and depends strongly on feed imports. While plant-based protein demand increases, the local grain legume production is negligible ( ~ 9000 hectares). Here, we investigated the potential of sustainable legume protein production based on an expert survey followed by a quantitative analysis based on yield, soil, terrain and climate data. Pea, soybean and faba bean showed high potential for Swiss agriculture given adaptions in policy, pricing and breeding. The potential grain legume production area was 107,734 hectares on suitable arable land (Scenario I). Switzerland’s self-sufficiency could be increased by cutting imports and maximizing legume production on 181,479 hectares (Scenario II) in expense of grassland and fodder maize. This would replace approximately 41% of animal protein consumption with plant-based protein, preserving 32% of milk and 24% of meat protein. In conclusion, domestic legume production could be substantially increased while improving human and environmental health.

Canopy temperature (CT) is often interpreted as representing leaf activity traits such as photosynthetic rates, gas exchange rates, or stomatal conductance. This interpretation is based on the observation that leaf activity traits correlate with transpiration which affects leaf temperature. Accordingly, CT measurements may provide a basis for high throughput assessments of the productivity of wheat canopies during early grain filling, which would allow distinguishing functional from dysfunctional stay-green. However, whereas the usefulness of CT as a fast surrogate measure of sustained vigor under soil drying is well established, its potential to quantify leaf activity traits under high-yielding conditions is less clear. To better understand sensitivity limits of CT measurements under high yielding conditions, we generated within-genotype variability in stay-green functionality by means of differential short-term pre-anthesis canopy shading that modified the sink:source balance. We quantified the effects of these modifications on stay-green properties through a combination of gold standard physiological measurements of leaf activity and newly developed methods for organ-level senescence monitoring based on timeseries of high-resolution imagery and deep-learning-based semantic image segmentation. In parallel, we monitored CT by means of a pole-mounted thermal camera that delivered continuous, ultra-high temporal resolution CT data. Our results show that differences in stay-green functionality translate into measurable differences in CT in the absence of major confounding factors. Differences amounted to approximately 0.8°C and 1.5°C for a very high-yielding source-limited genotype, and a medium-yielding sink-limited genotype, respectively. The gradual nature of the effects of shading on CT during the stay-green phase underscore the importance of a high measurement frequency and a time-integrated analysis of CT, whilst modest effect sizes confirm the importance of restricting screenings to a limited range of morphological and phenological diversity.

This study investigated the performance of cocoa trees within an irrigated cocoa plantation situated in the semi-arid region of Bahia, Brazil. Two treatments were compared: “full sun,” where cocoa trees were not shaded, and “shade,” where trees were covered with a shading net absorbing 30 % of the radiation. The number of leaves and the leaf area index (LAI) were assessed using destructive method on 8 trees. In addition, new flushing of leaves, categorized into four flushing stages, were assessed visually on a weekly basis during two years. The variation of the stem diameter was measured using dendrometer sensors (n = 12 trees). Yield parameters like dry bean yield and number of fruits (healthy and aborted) were assessed on 40 trees per treatment. Both treatments, performed well in the semi-arid region. Generative parameters, such as dry bean yield (±2,000 kg/ha), fruit healthy and abortion rate per plot, were unaffected by full sun and shade treatments. The treatments showed high fruit abortion rates of (±60 %), showing that there's still much room for yield optimization. Additionally, stem diameter of the trees showed a significant reduction of the stem growth (daily increase of stem diameter) and maximum daily shrinkage (daily variation of stem diameter) during the flushing of new leaves. This implies that the emergence of new leaves significantly influences stem growth, consequently affecting the fruits which are growing on the stem. This assumption was corroborated by the significantly increased fruit abortion rate during the flushing of new leaves (stages 1 & 2). These findings highlight the potential of dendrometers to quantify this effect what can be used in future to optimize management practices. By doing so, more effective strategies can be developed to enhance cocoa yield and overall productivity in semi-arid regions.

Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar–environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions.

Time series of optical imagery allow one to derive land surface phenology metrics. These metrics are only complete with a statement about their uncertainty. A source of uncertainty is the radiometry of the sensor. We propagated radiometric uncertainties within a Monte Carlo framework into phenological metrics using the TIMESAT approach based on time series of the normalized difference vegetation index (NDVI), three-band enhanced vegetation index (EVI), and green leaf area index (GLAI) derived from radiative transfer modeling. In addition, we studied the effect of propagated uncertainties on scene preclassification. We focused on Sentinel-2 multispectral imager top-of-atmosphere data since quantitative estimates of radiometric uncertainties are available. Propagation was carried out for a growing season over an agricultural region in Switzerland. Propagated uncertainties had little impact on the classification except for spectrally mixed pixels. Effects on the spectral indices and GLAI were more pronounced. In detail, the GLAI was more uncertain due to the ill-posedness of radiative transfer model inversion (median relative uncertainty for all crop pixels and Sentinel-2 scenes: 4.4%) than EVI (2.7%) and NDVI (1.1%). Regarding phenology, metrics exhibited largest uncertainties in the case of GLAI. The magnitude of uncertainty in the metrics depends on the interscene error correlation, which we assumed to be either zero (uncorrelated) or one (fully correlated) since the actual correlation is unknown. If uncertainties are fully correlated, uncertainties in metrics are small (two to three days) but take values up to greater ten days under the uncorrelated assumption. Thus, our work provides guidance for the interpretation of phenological metrics.

Abstract Crop diversification is a potential strategy to increase the stability and productivity of crops, while reducing pathogen pressures and pesticide requirements. Crop variety mixtures provide some of these diversification benefits and their cultivation is fully compatible with current mechanized agronomic practices. However, the development of optimal variety mixtures is a long, labour‐intense process requiring extensive field trials. High throughput field phenotyping (HTFP) methods provide promising applications in field testing because they allow for precise, repeatable, and rapid measurements of crop properties. Here, we evaluated the use of HTFP for developing high‐performing oat (Avena sativa) variety mixtures by testing its suitability to predict diversity yield benefits from repeated canopy measurements across the growing season. Analyzing 26 mixtures of five varieties, we found significant overyielding at harvest, that is, mixtures were on average more productive than expected based on component pure stands. This grain yield overyielding was well predicted from deviations between mixture and pure stand canopy cover estimations, derived from HTFP mid‐way through the growing season. This shows that (i) positive interactions between oat varieties occur already at an early stage, (ii) such interactions lead to increased potential for light interception, (iii) HTFP offers rapid, scalable methods to screen for performant variety mixtures.

A broad compositional range of Nb-Ti anodic memristors with volatile and self-rectifying behaviour was studied using a combinatorial screening approach. A Nb-Ti thin-film combinatorial library was co-deposited by sputtering, serving as the bottom electrode for the memristive devices. The library, with a compositional spread ranging between 22 and 64 at.% Ti was anodically oxidised, the mixed oxide being the active layer in MIM-type structures completed by Pt discreet top electrode patterning. By studying I–U sweeps, memristors with self-rectifying and volatile behaviour were identified. Moreover, all the analysed memristors demonstrated multilevel properties. The best-performing memristors showed HRS/LRS (high resistive state/low resistive state) ratios between 4 and 6 × 105 and very good retention up to 106 successive readings. The anodic memristors grown along the compositional spread showed very good endurance up to 106 switching cycles, excluding those grown from alloys containing between 31 and 39 at.% Ti, which withstood only 10 switching cycles. Taking into consideration all the parameters studied, the Nb-46 at.% Ti composition was screened as the parent metal alloy composition, leading to the best-performing anodic memristor in this alloy system. The results obtained suggest that memristive behaviour is based on an interfacial non-filamentary type of resistive switching, which is consistent with the performed cross-sectional TEM structural and chemical characterisation.

Chemosensor technology for trace gases in the air always aims to identify these compounds and then measure their concentrations. For identification, traceable methods are sparse and relate to large appliances such as mass spectrometers. We present a new method that uses the alternative traceable measurement of the ionization energies of trace gases in a way that can be miniaturized and energetically tuned. We investigate the achievable performance. Since tunable UV sources are not available for photoionization, we take a detour via impact ionization with electrons, which we generate using the photoelectric effect and bring to sharp, defined energies on a nanoscale in the air. Electron impact ionization is thus possible at air pressures of up to 900 hPa. The sensitivity of the process reaches 1 ppm and is equivalent to that of classic PID. With sharpened energy settings, substance identification is currently possible with an accuracy of 30 meV. We can largely explain the experimental observations with the known quantum mechanical models.

This article describes the data from an online survey conducted at a farm management course in Switzerland. The survey was conducted in German and French between April and May 2021. It was emailed to teachers and students at agricultural education centres across Switzerland that offer a farm management program. In the first part, the survey investigated whether digital technologies were taught in agricultural training, and, more specifically, in basic training or in the farm management course. Next, it investigated teachers’ and students’ general perceptions of digital technologies in plant production and animal husbandry. The survey further included questions about information sources individuals use to learn more about digital technologies in agriculture. In a subsequent part, students who already owned or co-owned a farm were asked whether they use a farm management information system and were planning to use more digital technologies in the future. For this, we used three items investigating perceived ease of use, which were derived from a previous study and four items using a trans-theoretical model of adoption. Finally, all participants provided basic sociodemographic data and answered items related to environmental concern, based on an existing scale. The survey can be used and adapted to different contents, aiming to investigate perception and adoption of farm management information systems and study the course content, how individuals acquire knowledge or how they perceive digital technologies.

The development of miniaturized and portable sensing devices is crucial to meeting the high processing capacity demands of contemporary computing systems. Hence, the conceptualization of memristive sensors for hydroxide-containing liquids is proposed in this study. Metal-insulator-metal (MIM) structures were formed on electrochemically anodized Hf thin films with Pt patterned as top electrodes. These MIM memristive structures were integrated into a crossbar array, allowing the investigation of a high number of potential memristor sensors. The MIM structures have demonstrated sensing possibilities in the detection of the hydroxyl ion in D-glucose, used as a standard solution. The sensing method was based on the resistive state ratio extracted from I-U sweeps measurements. Analytical characterization of the memristor sensor was done based on the resistive state ratio in relation to different concentrations of a standard solution drop cast directly on the surface of the device. Linearity was found for D-glucose concentrations ranging from 10 mM to 80 mM with a reasonable corresponding correlation factor (R2=0.96809). Additionally, D-glucose incorporation in anodic oxide was studied by XPS to investigate its effect on conductive filaments formation. A carbon bonded by a single covalent bond to oxygen (O-C-O) was detected, confirming the proposed sensing mechanism defined by the glucose penetrating the oxide/electrode interface.

Carbonized lignin powder was used as a salt bridge for a silver-silver chloride reference electrode. This easy-to-prepare reference electrode exhibited excellent stability in saturated potassium chloride solution. In addition, the electrochemical impedance spectra showed that the prepared reference electrode is stable in acidic, neutral, and basic aqueous solutions (pH 1 - 12) and has similar impedances to its glass frit equivalent.

Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling. Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions. Besides direct effects on green leaf area in terms of leaf damage, stressors often anticipate or accelerate physiological senescence, which may multiply their negative impact on grain filling. Here, we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots (stems + leaves) based on deep learning models for semantic segmentation and color properties of vegetation. A vegetation segmentation model was trained using semisynthetic training data generated using image composition and generative adversarial neural networks, which greatly reduced the risk of annotation uncertainties and annotation effort. Application of the models to image time series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis. Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations (r ≈ 0.9). Contrasting patterns were observed for plots with different levels of foliar diseases, particularly septoria tritici blotch. Our results suggest that tracking the chlorotic and necrotic fractions separately may enable (a) a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and (b) investigation of interactions between biotic stress and physiological senescence. The high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.

Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed “frost damage index” (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.

We set out to demonstrate the development of a highly conductive polymer based on poly-(3,4-ethylenedithia thiophene) (PEDTT), PEDOTs structural analogue historically notorious for structural disorder and limited conductivities. The caveat therein was previously described to lie in intra-molecular repulsions. We demonstrate how a tremendous >2600-fold improvement in conductivity and metallic features, such as magnetoconductivity can be achieved. This is achieved through a careful choice of the counter-ion (sulphate) and the use of oxidative chemical vapour deposition (oCVD). It is shown that high structural order on the molecular level was established and the formation of crystallites tens of nanometres in size was achieved. We infer that the sulphate ions therein intercalate between the polymer chains, thus forming densely packed crystals of planar molecules with extended π-systems. Consequently, room-temperature conductivities of above 1000 S cm−1 are achieved, challenging those of conventional PEDOT:PSS. The material is in the critical regime of the metal–insulator transition.

Abstract Background Root system architecture and especially its plasticity in acclimation to variable environments play a crucial role in the ability of plants to explore and acquire efficiently soil resources and ensure plant productivity. Non-destructive measurement methods are indispensable to quantify dynamic growth traits. For closing the phenotyping gap, we have developed an automated phenotyping platform, GrowScreen-Agar, for non-destructive characterization of root and shoot traits of plants grown in transparent agar medium. Results The phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280 Arabidopsis plants within 15 min. The potential of the platform has been demonstrated by quantifying phenotypic differences within 78 Arabidopsis accessions from the 1001 genomes project. The chosen concept ‘plant-to-sensor’ is based on transporting plants to the imaging position, which allows for flexible experimental size and design. As transporting causes mechanical vibrations of plants, we have validated that daily imaging, and consequently, moving plants has negligible influence on plant development. Plants are cultivated in square Petri dishes modified to allow the shoot to grow in the ambient air while the roots grow inside the Petri dish filled with agar. Because it is common practice in the scientific community to grow Arabidopsis plants completely enclosed in Petri dishes, we compared development of plants that had the shoot inside with that of plants that had the shoot outside the plate. Roots of plants grown completely inside the Petri dish grew 58% slower, produced a 1.8 times higher lateral root density and showed an etiolated shoot whereas plants whose shoot grew outside the plate formed a rosette. In addition, the setup with the shoot growing outside the plate offers the unique option to accurately measure both, leaf and root traits, non-destructively, and treat roots and shoots separately. Conclusions Because the GrowScreen-Agar system can be moved from one growth chamber to another, plants can be phenotyped under a wide range of environmental conditions including future climate scenarios. In combination with a measurement throughput enabling phenotyping a large set of mutants or accessions, the platform will contribute to the identification of key genes.

Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.

The development of novel materials with coexisting volatile threshold and non-volatile memristive switching is crucial for neuromorphic applications. Hence, the aim of this work was to investigate the memristive properties of oxides in a Hf–Nb thin-film combinatorial system deposited by sputtering on Si substrates. The active layer was grown anodically on each Hf–Nb alloy from the library, whereas Pt electrodes were deposited as the top electrodes. The devices grown on Hf-45 at.% Nb alloys showed improved memristive performances reaching resistive state ratios up to a few orders of magnitude and achieving multi-level switching behavior while consuming low power in comparison with memristors grown on pure metals. The coexistence of threshold and resistive switching is dependent upon the current compliance regime applied during memristive studies. Such behaviors were explained by the structure of the mixed oxides investigated by TEM and XPS. The mixed oxides, with HfO2 crystallites embedded in quasi amorphous and stoichiometrically non-uniform Nb oxide regions, were found to be favorable for the formation of conductive filaments as a necessary step toward memristive behavior. Finally, metal–insulator–metal structures grown on the respective alloys can be considered as relevant candidates for the future fabrication of anodic high-density in-memory computing systems for neuromorphic applications.

Understanding the interaction of plant growth with environmental conditions is crucial to increase the resilience of current cropping systems to a changing climate. Here, we investigate PhenoCams as a high-throughput approach for field phenotyping experiments to assess growth dynamics of many different genotypes simultaneously in high temporal (daily) resolution. First, we develop a method that extracts a daily phenological signal that is normalized for the different viewing geometries of the pixels within the images. Second, we investigate the extraction of the in season traits of early vigor, leaf area index (LAI), and senescence dynamic from images of a soybean (Glycine max) field phenotyping experiment and show that it is possible to rate early vigor, senescence dynamics, and track the LAI development between LAI 1 and 4.5. Third, we identify the start of green up, green peak, senescence peak, and end of senescence in the phenological signal. Fourth, we extract the timing of these points and show how this information can be used to assess the impact of phenology on harvest traits (yield, thousand kernel weight, and oil content). The results demonstrate that PhenoCams can track growth dynamics and fill the gap of high temporal monitoring in field phenotyping experiments.

Abstract Background Recent advances in high throughput phenotyping have made it possible to collect large datasets following plant growth and development over time, and those in machine learning have made inferring phenotypic plant traits from such datasets possible. However, there remains a dirth of datasets following plant growth under stress conditions along with methods for inferring them using only remotely sensed data, especially under a combination of multiple stress factors such as drought, weeds and nutrient deficiency. Such stress factors and their combinations are commonly encountered during crop production and being able to accurately detect and treat such stress conditions in an automated and timely manner can provide a major boost to farm yields with minimal resource input. Results We present a generic framework for remote plant stress phenotyping that consists of a dataset with spatio-temporal-spectral data following sugarbeet crop growth under optimal, drought, low and surplus nitrogen fertilization, and weed stress conditions, along with a machine learning based methodology for systematically inferring these stress conditions from the remotely measured data. The dataset contains biweekly color images, infra-red stereo image pairs and hyperspectral camera images along with applied treatment parameters and environmental factors like temperature and humidity, collected over two months. We present a plant agnostic methodology for deriving plant trait indicators such as canopy cover, height, hyperspectral reflectance and vegetation indices along with a spectral 3D reconstruction of the plants from the raw data to serve as a benchmark. Additionally, we provide fresh and dry weight measurements for both the above (canopy) and below (beet) ground biomass at the end of the growing period to serve as indicators of expected yield. We further describe a data driven, machine learning based method to infer water, Nitrogen and weed stress using the derived plant trait indicators. We use the plant trait indicators to evaluate 8 different classification approaches from which the best classifier achieved a mean cross validation accuracy of $$\approx$$ ≈ 93, 76 and 83% for drought, nitrogen and weed stress severity classification respectively. We also show that our multi-modal approach significantly improves classifier performance over using any single modality. Conclusion The presented framework and dataset can serve as a valuable reference for creating and comparing processing pipelines which extract plant trait indicators and infer prevalent stress factors from remote sensing data under a variety of environments and cropping conditions. These techniques can then be deployed on farm machinery or robots enabling automated, precise and timely corrective interventions for maximising yield.

Atmospheric corrosion is one of the major sources of hydrogen in a high-strength-steel product in service. Even low concentrations of absorbed hydrogen can cause a hydrogen embrittlement-related material degradation. The extent of atmospheric corrosion and thus the related hydrogen entry is highly dependent on the environmental parameters, such as the relative humidity. The present work focused on the hydrogen entry at low relative humidity, where atmospheric corrosion rates are expected to be low. Hydrogen insertion and distribution in CP1000 steel induced by corrosion under dried and rewetted single droplets of aqueous NaCl and MgCl2 solution were studied using the Scanning Kelvin Probe (SKP) and the resulting amounts of diffusible hydrogen were analyzed using thermal desorption mass spectrometry (TDMS). Corrosion product analyses were carried out with SEM/EDX, XRD, and Mössbauer spectroscopy. The results revealed the strong impact of salt type and concentration on the hydrogen entry into steel. The hygroscopic effect of MgCl2 and the formed corrosion products were responsible for the prolonged insertion of hydrogen into the steel even at very low levels of relative humidity.

A thin film combinatorial library deposited by co-sputtering of Hf, Nb and Ta was employed to characterise fundamental properties of the Hf-Nb-Ta system. Compositional mappings of microstructure and crystallography revealed similarities in alloy evolution. Distinct lattice distortion was observed upon addition of hexagonal Hf, leading to amorphisation of alloys containing more than 32 at.% Hf and less than 27 and 41 at.% Nb and Ta, respectively. Volta potential and open circuit potential mappings indicated minimal values for the highest Hf concentration. Localised anodisation of the library by scanning droplet cell microscopy revealed valve metal behaviour. Oxide formation factors above 2 nm V−1 were identified in compositional zones with high amounts of Nb and Ta. Fitting of electrochemical impedance spectroscopy data allowed electrical permittivity and resistivity of mixed oxides to be mapped. Their compositional behaviours were attributed to characteristics of the parent metal alloys and particularities of the pure oxides. Mott–Schottky analysis suggested n-type semiconductor properties for all Hf–Nb–Ta oxides studied. Donor density and flat-band potential were mapped compositionally, and their variations were found to be related mainly to the Nb amount. Synergetic effects were identified in mappings of Hf-Nb-Ta parent metals and their anodic oxides.

Flexible circuits: anodization makes them all! A simple concept of scanning head-guided anodization is shown to be highly expandable to fabricate various electronic components. A team led by Professor Siegfried Bauer from Johannes Kepler University Linz, Austria develops a universal and patternable printing protocol of anodic oxides for a full range of circuit components for flexible devices. The researchers employ a scanning droplet cell microscope to anodize the pre-deposited thin metal films to form dielectric layers with good control in both lateral dimension and vertical thickness. They demonstrate the versatility of the on-site anodization methods by fabricating oxides-based resistors, diodes, transistors and memristors, and multilayer capacitors with a record-high areal capacity of 4 µF cm−2. The approach is cheap, adaptable, and thus ideal for rapid-prototyping of metal oxides circuits for various applications.

Activated carbons derived from viscose fibers were prepared using potassium hydroxide, carbon dioxide, or water vapor as activation agents. The produced activated carbon fibers were analyzed via scanning electron microscopy and energy dispersive X-ray spectroscopy, and their porosity (specific surface area, total pore volume, and pore size distribution) was calculated employing physisorption experiments. Activated carbon fibers with a specific surface area of more than 2500 m2 g−1 were obtained by each of the three methods. Afterwards, the suitability of these materials as electrodes for electrochemical double-layer capacitors (supercapacitors) was investigated using cyclic voltammetry, galvanostatic measurements, and electrochemical impedance spectroscopy. By combining CO2 and H2O activation, activated carbon fibers of high purity and excellent electrochemical performance could be obtained. A specific capacitance per electrode of up to 180 F g−1 was found. In addition, an energy density per double-layer capacitor of 42 W h kg−1 was achieved. These results demonstrate the outstanding electrochemical properties of viscose-based activated carbon fibers for use as electrode materials in energy storage devices such as supercapacitors.

The aim of this study was to develop memristors based on Nb2O5 grown by a simple and inexpensive electrochemical anodization process. It was confirmed that the electrolyte selection plays a crucial role in resistive switching due to electrolyte species incorporation in oxide, thus influencing the formation of conductive filaments. Anodic memristors grown in phosphate buffer showed improved electrical characteristics, while those formed in citrated buffer exhibited excellent memory capabilities. The chemical composition of oxides was successfully determined using HAXPES, while their phase composition and crystal structure with conductive filaments was assessed by TEM at the nanoscale. Overall, understanding the switching mechanism leads towards a wide range of possible applications for Nb memristors either as selector devices or nonvolatile memories.

Abstract Background Phenotyping technologies are expected to provide predictive power for a range of applications in plant and crop sciences. Here, we use the disease pressure of Beet Cyst Nematodes (BCN) on sugar beet as an illustrative example to test the specific capabilities of different methods. Strong links between the above and belowground parts of sugar beet plants have made BCN suitable targets for use of non-destructive phenotyping methods. We compared the ability of visible light imaging, thermography and spectrometry to evaluate the effect of BCN on the growth of sugar beet plants. Results Two microplot experiments were sown with the nematode susceptible cultivar Aimanta and the nematode tolerant cultivar BlueFox under semi-field conditions. Visible imaging, thermal imaging and spectrometry were carried out on BCN infested and non-infested plants at different times during the plant development. Effects of a chemical nematicide were also evaluated using the three phenotyping methods. Leaf and beet biomass were measured at harvest. For both susceptible and tolerant cultivar, canopy area extracted from visible images was the earliest nematode stress indicator. Using such canopy area parameter, delay in leaf growth as well as benefit from a chemical nematicide could be detected already 15 days after sowing. Spectrometry was suitable to identify the stress even when the canopy reached full coverage. Thermography could only detect stress on the susceptible cultivar. Spectral Vegetation Indices related to canopy cover (NDVI and MCARI2) and chlorophyll content (CHLG) were correlated with the final yield (R = 0.69 on average for the susceptible cultivar) and the final nematode population in the soil (R = 0.78 on average for the susceptible cultivar). Conclusion In this paper we compare the use of visible imaging, thermography and spectrometry over two cultivars and 2 years under outdoor conditions. The three different techniques have their specific strengths in identifying BCN symptoms according to the type of cultivars and the growth stages of the sugar beet plants. Early detection of nematicide benefit and high yield predictability using visible imaging and spectrometry suggests promising applications for agricultural research and precision agriculture.

Canopy temperature (CT) has been related to water-use and yield formation in crops. However, constantly (e.g., sun illumination angle, ambient temperature) as well as rapidly (e.g., clouds) changing environmental conditions make it difficult to compare measurements taken even at short time intervals. This poses a great challenge for high-throughput field phenotyping (HTFP). The aim of this study was to i) set up a workflow for unmanned aerial vehicles (UAV) based HTFP of CT, ii) investigate different data processing procedures to combine information from multiple images into orthomosaics, iii) investigate the repeatability of the resulting CT by means of heritability, and iv) investigate the optimal timing for thermography measurements. Additionally, the approach was v) compared with other methods for HTFP of CT. The study was carried out in a winter wheat field trial with 354 genotypes planted in two replications in a temperate climate, where a UAV captured CT in a time series of 24 flights during 6 weeks of the grain-filling phase. Custom-made thermal ground control points enabled accurate georeferencing of the data. The generated thermal orthomosaics had a high spatial accuracy (mean ground sampling distance of 5.03 cm/pixel) and position accuracy [mean root-mean-square deviation (RMSE) = 4.79 cm] over all time points. An analysis on the impact of the measurement geometry revealed a gradient of apparent CT in parallel to the principle plane of the sun and a hotspot around nadir. Averaging information from all available images (and all measurement geometries) for an area of interest provided the best results by means of heritability. Correcting for spatial in-field heterogeneity as well as slight environmental changes during the measurements were performed with the R package SpATS. CT heritability ranged from 0.36 to 0.74. Highest heritability values were found in the early afternoon. Since senescence was found to influence the results, it is recommended to measure CT in wheat after flowering and before the onset of senescence. Overall, low-altitude and high-resolution remote sensing proved suitable to assess the CT of crop genotypes in a large number of small field plots as is required in crop breeding and variety testing experiments.

The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in wheat. High throughput methods to assess senescence dynamics in large field trials will allow for (i) indirect selection in early breeding generations, when yield cannot yet be accurately determined and (ii) mapping of the genomic regions controlling the trait. The aim of this study was to develop a robust method to assess senescence based on hyperspectral canopy reflectance. Measurements were taken in three years throughout the grain filling phase on >300 winter wheat varieties in the spectral range from 350 to 2500 nm using a spectroradiometer. We compared the potential of spectral indices (SI) and full-spectrum models to infer visually observed senescence dynamics from repeated reflectance measurements. Parameters describing the dynamics of senescence were used to predict GY and GPC and a feature selection algorithm was used to identify the most predictive features. The three-band plant senescence reflectance index (PSRI) approximated the visually observed senescence dynamics best, whereas full-spectrum models suffered from a strong year-specificity. Feature selection identified visual scorings as most predictive for GY, but also PSRI ranked among the most predictive features while adding additional spectral features had little effect. Visually scored delayed senescence was positively correlated with GY ranging from r = 0.173 in 2018 to r = 0.365 in 2016. It appears that visual scoring remains the gold standard to quantify leaf senescence in moderately large trials. However, using appropriate phenotyping platforms, the proposed index-based parameterization of the canopy reflectance dynamics offers the critical advantage of upscaling to very large breeding trials.

Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping. Nevertheless, methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare. We aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery. We used RGB images (ground sampling distance of 3 mm pixel-1) acquired by repeated flights (≥ 2 flights per week) to quantify temporal changes of visible leaf area. To exploit the information contained in the multitude of viewing angles within the RGB images, we processed them to multiview ground cover images showing plant pixel fractions. Based on these images, we trained a support vector machine for the beginning of stem elongation (GS30). Using the GS30 as key point, we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling, respectively. Our results show that determination coefficients of predictions are moderate for plant count (R2=0.52), but strong for tiller count (R2=0.86) and GS30 (R2=0.77). Heritabilities are superior to manual measurements for plant count and tiller count, but inferior for GS30 measurements. Increasing the selection intensity due to throughput may overcome this limitation. Multiview image traits can replace hand measurements with high efficiency (85–223%). We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.

Biomass-derived carbons are very attractive materials due to the possibility of tuning their properties for different energy-related applications. Various pore sizes, conductivities and the inherent presence of heteroatoms make them attractive for different electrochemical reactions, including the implementation of electrochemical capacitors or fuel cell electrodes. This contribution demonstrates how different biomass-derived carbons prepared from the same precursor of viscose fibers can reach appreciable capacitances (up to 200 F g−1) or a high selectivity for the oxygen reduction reaction (ORR). We find that a highly specific surface area and a large mesopore volume dominate the capacitive response in both aqueous and non-aqueous electrolytic solutions. While the oxygen reduction reaction activity is not dominated by the same factors at low ORR overpotentials, these take the dominant role over surface chemistry at high ORR overpotentials. Due to the high selectivity of the O2 reduction to peroxide and the appreciable specific capacitances, it is suggested that activated carbon fibers derived from viscose fibers are an attractive and versatile material for electrochemical energy conversion applications.

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.

Microstructures and nanostructures can be used to reduce the adhesion of the cells on the auxiliary material. Therefore, the aim of our work was to fabricate laser-induced hierarchical microstructures and nanostructures by femtosecond laser-treatment (wavelength 1040 nm, pulse length 350 fs, repetition rates in the kHz range) to reduce the cell adhesion. Additionally, surface chemistry modification by optimized electrochemical anodization was used to further reduce the cell adhesion. For testing, flat plates and bone screws made of Ti-6Al-4V were used. Bone-forming cells (human osteoblasts from the cell line SAOS-2) were grown on the bone implants and additional test samples for two to three weeks. After the growth period, the cells were characterized by scanning electron microscopy (SEM). While earlier experiments with fibroblasts had shown that femtosecond laser-processing followed by electrochemical anodization had a significant impact on cell adhesion reduction, for osteoblasts the same conditions resulted in an activation of the cells with increased production of extracellular matrix material. Significant reduction of cell adhesion for osteoblasts was only obtained at pre-anodized surfaces. It could be demonstrated that this functionalization by means of femtosecond laser-processing can result in bone screws that hinder the adhesion of osteoblasts.

Anodic HfO2 memristors grown in phosphate, borate, or citrate electrolytes and formed on sputtered Hf with Pt top electrodes are characterized at fundamental and device levels. The incorporation of electrolyte species deep into anodic memristors concomitant with HfO2 crystalline structure conservation is demonstrated by elemental analysis and atomic scale imaging. Upon electroforming, retention and endurance tests are performed on memristors. The use of borate results in the weakest memristive performance while the citrate demonstrates clear superior memristive properties with multilevel switching capabilities and high read/write cycling in the range of 106. Low temperature heating applied to memristors shows a direct influence on their behavior mainly due to surface release of water. Citrate-based memristors show remarkable properties independent on device operation temperatures up to 100 °C. The switching dynamic of anodic HfO2 memristors is discussed by analyzing high resolution transmission electron microscope images. Full and partial conductive filaments are visualized, and apart from their modeling, a concurrency of filaments is additionally observed. This is responsible for the multilevel switching mechanism in HfO2 and is related to device failure mechanisms.

New management practices must be developed to improve yam productivity. By allowing non-destructive analyses of important plant traits, image-based phenotyping techniques could help developing such practices. Our objective was to determine the potential of image-based phenotyping methods to assess traits relevant for tuber yield formation in yam grown in the glasshouse and in the field. We took plant and leaf pictures with consumer cameras. We used the numbers of image pixels to derive the shoot biomass and the total leaf surface and calculated the ‘triangular greenness index’ (TGI) which is an indicator of the leaf chlorophyll content. Under glasshouse conditions, the number of pixels obtained from nadir view (view from the top) was positively correlated to shoot biomass, and total leaf surface, while the TGI was negatively correlated to the SPAD values and nitrogen (N) content of diagnostic leaves. Pictures taken from nadir view in the field showed an increase in soil surface cover and a decrease in TGI with time. TGI was negatively correlated to SPAD values measured on diagnostic leaves but was not correlated to leaf N content. In conclusion, these phenotyping techniques deliver relevant results but need to be further developed and validated for application in yam.

Water limitation is one of the major factors reducing crop productivity worldwide. In order to develop efficient breeding strategies to improve drought tolerance, accurate methods to identify when a plant reduces growth as a consequence of water deficit have yet to be established. In perennial ryegrass (Lolium perenne L.), an important forage grass of the Poaceae family, leaf elongation is a key factor determining plant growth and hence forage yield. Although leaf elongation has been shown to be temperature-dependent under non-stress conditions, the impact of water limitation on leaf elongation in perennial ryegrass is poorly understood. We describe a method for quantifying tolerance to water deficit based on leaf elongation in relation to temperature and soil moisture in perennial ryegrass. With decreasing soil moisture, three growth response phases were identified: first, a “normal” phase where growth is mainly determined by temperature, second a “slow” phase where leaf elongation decreases proportionally to soil water potential and third an “arrest” phase where leaf growth terminates. A custom R function was able to quantify the points which demarcate these phases and can be used to describe the response of plants to water deficit. Applied to different perennial ryegrass genotypes, this function revealed significant genotypic variation in the response of leaf growth to temperature and soil moisture. Dynamic phenotyping of leaf elongation can be used as a tool to accurately quantify tolerance to water deficit in perennial ryegrass and to improve this trait by breeding. Moreover, the tools presented here are applicable to study the plant response to other stresses in species with linear, graminoid leaf morphology.

Producing quantitative and reliable measures of crop disease is essential for resistance breeding, but is challenging and time consuming using traditional phenotyping methods. Hyperspectral remote sensing has shown potential for the detection of plant diseases, but its utility for phenotyping large and diverse populations of plants under field conditions requires further evaluation. In this study, we collected canopy hyperspectral data from 335 wheat varieties using a spectroradiometer, and we investigated the use of canopy reflectance for detecting the Septoria tritici blotch (STB) disease and for quantifying the severity of infection. Canopy- and leaf-level infection metrics of STB based on traditional visual assessments and automated analyses of leaf images were used as ground truth data. Results showed (i) that canopy reflectance and the selected spectral indices show promise for quantifying STB infections, and (ii) that the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices. Moreover, partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics. The PLS discriminant analysis (PLSDA) model calibrated based on the spectral data of four reference varieties was able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60). Finally, the PLSDA model predictions allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment. This study demonstrates the great potential of using canopy hyperspectral remote sensing to improve foliar disease assessment and to facilitate plant breeding for disease resistance.

Viscose fibers were impregnated with different concentrations of diammonium hydrogen phosphate (DAHP), carbonized, activated, and tested as high-performance electrode materials for supercapacitors. The yield of these activated carbon fibers (ACFs) could be increased by a factor of 14 by using DAHP compared to ACF without impregnation. These specific activation procedures yielded a high specific surface area of more than 2700 m2·g−1 with a pore size distribution (PSD) suitable for use as a supercapacitor electrode. The electrode materials were implemented in symmetric supercapacitors using TEMA BF4 as electrolyte and cyclic voltammetry measurements showed high specific capacitances of up to 167 F·g−1. Furthermore, the devices showed high energy densities of up to 21.4 W·h·kg−1 and high-power densities of up to 8.7 kW·kg−1. The supercapacitors featured high capacity retention (96%) after 10,000 cycles. These results show that ACFs made of viscose fibers, previously impregnated with DAHP, can be used as high-performance electrodes in supercapacitors for energy storage applications.

The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB (red, green, and blue) inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.