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Summary: In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield‐determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear‐counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017–2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro‐plot images. The relationship between the image‐based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in‐situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in‐situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed. Significance Statement: Ear density (ears m–2) is one of the main agronomical yield components of wheat. This study represents a novel contribution to the field of RGB image processing for plant phenotyping using unmanned aerial vehicle (UAV) platforms. By combining high‐resolution RGB imagery with an automatic ear classification and counting system, we have shown that it is possible to assess ear density with high precision from an aerial platform. This is the first published study successfully deploying this approach. [ABSTRACT FROM AUTHOR]

Background: Genetic studies and breeding of agricultural crops frequently involve phenotypic characterization of large collections of genotypes grown in field conditions. These evaluations are typically based on visual observations and manual (destructive) measurements. Robust image capture and analysis procedures that allow phenotyping large collections of genotypes in time series during developmental phases represent a clear advantage as they allow non-destructive monitoring of plant growth and performance. A L. perenne germplasm panel including wild accessions, breeding material and commercial varieties has been used to develop a low-cost, high-throughput phenotyping tool for determining plant growth based on images of individual plants during two consecutive growing seasons. Further we have determined the correlation between image analysis-based estimates of the plant's base area and the capacity to regrow after cutting, with manual counts of tiller number and measurements of leaf growth 2 weeks after cutting, respectively. When working with field-grown plants, image acquisition and image segmentation are particularly challenging as outdoor light conditions vary throughout the day and the season, and variable soil colours hamper the delineation of the object of interest in the image. Therefore we have used several segmentation methods including colour-, texture- and edge-based approaches, and factors derived after a fast Fourier transformation. The performance of the procedure developed has been analysed in terms of effectiveness across different environmental conditions and time points in the season. Results: The procedure developed was able to analyse correctly 77.2 % of the 24,048 top view images processed. High correlations were found between plant's base area (image analysis-based) and tiller number (manual measurement) and between regrowth after cutting (image analysis-based) and leaf growth 2 weeks after cutting (manual measurement), with r values up to 0.792 and 0.824, respectively. Nevertheless, these relations depend on the origin of the plant material (forage breeding lines, current forage varieties, current turf varieties, and wild accessions) and the period in the season. Conclusions: The image-derived parameters presented here deliver reliable, objective data, complementary to the breeders' scores, and are useful for genetic studies. Furthermore, large variation was shown among genotypes for the parameters investigated. [ABSTRACT FROM AUTHOR]

In azalea breeding, flower colour is the most essential selection criterion, but leaf morphology also influences attractiveness of the plant. Despite extensive study of the inheritance of flower colour, no explanation has yet been found for the pink phenotype. We have used image analysis to quantify flower colour and leaf morphology. Flower colour was quantified in a whole population. Pink flower colour has been confirmed to be the result of a gene-dosage effect; two major QTLs were found for flower colour as well as some minor QTLs that seem to be related to pink coloration. Leaf morphology (colour and shape) was scored in four unrelated populations by means of image analysis. The image analysis generated continuous, highly informative data for QTL mapping. Both classical parameters and symmetrical elliptic Fourier descriptors successfully described leaf morphology. Image analysis resulted in large data sets that had to be combined in principal components. Only a limited number of QTLs were found for leaf colour, but we could discern some major QTLs for leaf shape and size that were consistent over the different mapping populations. These QTLs are the most interesting candidates for future analysis of the selected traits. The multi-population approach certainly proved to be valuable for QTL mapping of complex traits. For leaf morphology, however, more research is needed to identify the most valuable QTLs for future use in MAS. [ABSTRACT FROM AUTHOR]

In the framework of variety testing, evaluation of the flower of Begonia x tuberhybrida Voss. is done by experts. The colours of the flowers are compared with the RHS colour chart. For each flower colour a code is given. This method has several disadvantages (data cannot be statistically analyzed with standard ANOVA techniques, bias by the expert is possible, difficult measurement depending on the light environment, ...) that can be overcome with image analysis. In this study several parameters were tested to distinguish the different varieties. Varieties were split in different colour groups (white, red and yellow–orange) and parameters were analyzed. Parameters based on the green colour plain of the RGB colour space resulted in the largest discriminating power. Parameters based on the HSV colour space performed less well. [ABSTRACT FROM AUTHOR]

Background and Aims Turgor pressure within a plant cell represents the key to the mechanistical descriptiion of plant growth, combining the effects of both water and carbon availability. The high level of spatio-temporal variation and diurnal dynamics in turgor pressure within a single plant make it a challenge to model these on the fine spatial scale required for functional–structural plant models (FSPMs). A conceptual model for turgor-driven growth in FSPMs has been established previously, but its practical use has not yet been explored. Methods A turgor-driven growth model was incorporated in a newly established FSPM for soybean. The FSPM simulates dynamics in photosynthesis, transpiration and turgor pressure in direct relation to plant growth. Comparisons of simulations with field data were used to evaluate the potential and shortcomings of the modelling approach. Key Results Model simulations revealed the need to include an initial seed carbon contribution, a more realistic sink function, an estimation of respiration, and the distinction between osmotic and structural sugars, in order to achieve a realistic model of plant growth. However, differences between simulations and observations remained in individual organ growth patterns and under different environmental conditions. This exposed the need to further investigate the assumptions of developmental and environmental (in)sensitivity of the parameters, which represent physiological and biophysical organ properties in the model, in future research. Conclusions The model in its current form is primarily a diagnostic tool, to better understand and model the behaviour of water relations on the scale of individual plant organs throughout the plant life cycle. Potential future applications include its use as a phenotyping tool to capture differences in plant performance between genotypes and growing environments in terms of specific plant characteristics. Additionally, focused experiments can be used to further improve the model mechanisms to lead to better predictive FSPMs, including scenarios of water deficit. [ABSTRACT FROM AUTHOR]

Background and Aims Currently, functional–structural plant models (FSPMs) mostly resort to static descriptions of leaf spectral characteristics, which disregard the influence of leaf physiological changes over time. In many crop species, including soybean, these time-dependent physiological changes are of particular importance as leaf chlorophyll content changes with leaf age and vegetative nitrogen is remobilized to the developing fruit during pod filling. Methods PROSPECT, a model developed to estimate leaf biochemical composition from remote sensing data, is well suited to allow a dynamic approximation of leaf spectral characteristics in terms of leaf composition. In this study, measurements of the chlorophyll content index (CCI) were linked to leaf spectral characteristics within the 400–800 nm range by integrating the PROSPECT model into a soybean FSPM alongside a wavelength-specific light model. Key Results Straightforward links between the CCI and the parameters of the PROSPECT model allowed us to estimate leaf spectral characteristics with high accuracy using only the CCI as an input. After integration with an FSPM, this allowed digital reconstruction of leaf spectral characteristics on the scale of both individual leaves and the whole canopy. As a result, accurate simulations of light conditions within the canopy were obtained. Conclusions The proposed approach resulted in a very accurate representation of leaf spectral properties, based on fast and simple measurements of the CCI. Integration of accurate leaf spectral characteristics into a soybean FSPM leads to a better, dynamic understanding of the actual perceived light within the canopy in terms of both light quantity and quality. [ABSTRACT FROM AUTHOR]

In the plant sciences, results of laboratory studies often do not translate well to the field. To help close this lab-field gap, we developed a strategy for studying the wiring of plant traits directly in the field, based on molecular profiling and phenotyping of individual plants. Here, we use this single-plant omics strategy on winter-type Brassica napus (rapeseed). We investigate to what extent early and late phenotypes of field-grown rapeseed plants can be predicted from their autumnal leaf gene expression, and find that autumnal leaf gene expression not only has substantial predictive power for autumnal leaf phenotypes but also for final yield phenotypes in spring. Many of the top predictor genes are linked to developmental processes known to occur in autumn in winter-type B. napus accessions, such as the juvenile-to-adult and vegetative-to-reproductive phase transitions, indicating that the yield potential of winter-type B. napus is influenced by autumnal development. Our results show that single-plant omics can be used to identify genes and processes influencing crop yield in the field. Author summary: In the face of world population growth and climate change, the development of crops with increased yield and stress resilience is more urgent than ever. A major bottleneck in this process is translating the results of lab experiments to the field, in part because plant growth conditions in a lab are very different from field conditions. Here, we assess the merits of an alternative approach in which data is generated directly in the field, thereby bypassing the translation step. We profiled the gene expression and trait variability of a population of genetically (nearly) identical rapeseed plants grown in the same field, and used machine learning models to link the individual plants' gene expression to their phenotypic traits. We find that the plants' yield traits in spring can be predicted to a considerable extent from gene expression profiled > 5 months earlier in autumn. More importantly, we find that the top predictors in these models function in processes known to affect autumnal plant growth and development. This shows that our single-plant omics approach can be used to identify genes and processes influencing crop yield in the field. [ABSTRACT FROM AUTHOR]

This study explores the potential of a novel hyperspectral snapshot mosaic camera for weed and maize classification. The image processing, feature engineering and machine learning techniques were discussed when developing an optimal classification model for the three kinds of weeds and maize. A total set of 185 spectral features including reflectance and vegetation index features was constructed. Subsequently, the principal component analysis was used to reduce the redundancy of the constructed features, and the first 5 principal components, explaining over 95% variance ratio, were kept for further analysis. Furthermore, random forests as one of machine learning techniques were built for developing the classifier with three different combinations of features. Accuracy-oriented feature reduction was performed when choosing the optimal number of features for building the classification model. Moreover, hyperparameter tuning was explored for the optimal selection of random forest model. The results showed that the optimal random forest model with 30 important spectral features can achieve a mean correct classification rate of 1.0, 0.789, 0.691 and 0.752 for Zea mays , Convolvulus arvensis , Rumex and Cirsium arvense , respectively. The McNemar test showed an overall better performance of the optimal random forest model at the 0.05 significance level compared to the k-nearest neighbours (KNN) model. [ABSTRACT FROM AUTHOR]

Background and Aims In many scenarios the availability of assimilated carbon is not the constraining factor of plant growth. Rather, organ growth appears driven by sink activity in which water availability plays a determinant role. Current functional--structural plant models (FSPMs) mainly focus on plant-carbon relations and largely disregard the importance of plant water status in organogenesis. Consequently, incorporating a turgor-driven growth concept, coupling carbon and water dynamics in an FSPM, presents a significant improvement towards capturing plant development in a more mechanistic manner. Methods An existing process-based water flow and storage model served as a basis for implementing water control in FSPMs. Its concepts were adjusted to the scale of individual plant organs and interwoven with the basic principles of modelling carbon dynamics to allow evaluation of turgor pressure across the entire plant. This was then linked to plant organ growth by applying the principles of the widely used Lockhart equation. Key results This model successfully integrates a mechanistic understanding of plant water transport dynamics coupled with simple carbon dynamics within a dynamically developing plant architecture. It allows evaluation of turgor pressure on the scale of plant organs, resulting in clear diel and long-term patterns, directly linked to plant organ growth. Conclusions A conceptual sap flow and turgor-driven growth model was introduced for functional--structural plant modelling. It is applicable to any plant architecture and allows visual exploration of the diel patterns of organ water content and growth. Integrated in existing FSPMs, this new concept fosters an array of possibilities for FSPMs, as it presents a different formulation of growth in terms of local processes, influenced by local and external conditions. [ABSTRACT FROM AUTHOR]

* Background and Aims The high productivity of Miscanthus x giganteus has been at least partly ascribed to its high chilling tolerance compared with related C4 crops, allowing for a longer productive growing season in temperate climates. However, the chilling tolerance of M. x giganteus has been predominantly studied under controlled environmental conditions. The understanding of the underlying mechanisms contributing to chilling tolerance in the field and their variation in different miscanthus genotypes is largely unexplored. * Methods Five miscanthus genotypes with different sensitivities to chilling were grown in the field and scored for a comprehensive set of physiological traits throughout the spring season. Chlorophyll fluorescence was measured as an indication of photosynthesis, and leaf samples were analysed for biochemical traits related to photosynthetic activity (chlorophyll content and pyruvate, Pi dikinase activity), redox homeostasis (malondialdehyde, glutathione and ascorbate contents, and catalase activity) and water-soluble carbohydrate content. * Key Results Chilling-tolerant genotypes were characterized by higher levels of malondialdehyde, raffinose and sucrose, and higher catalase activity, while the chilling-sensitive genotypes were characterized by higher concentrations of glucose and fructose, and higher pyruvate, Pi dikinase activity later in the growing season. On the early sampling dates, the biochemical responses of M. x giganteus were similar to those of the chilling-tolerant genotypes, but later in the season they became more similar to those of the chilling-sensitive genotypes. * Conclusions The overall physiological response of chilling-tolerant genotypes was distinguishable from that of chilling-sensitive genotypes, while M. x giganteus was intermediate between the two. There appears to be a trade-off between high and efficient photosynthesis and chilling stress tolerance. Miscanthus x giganteus is able to overcome this trade-off and, while it is more similar to the chilling-sensitive genotypes in early spring, its photosynthetic capacity is similar to that of the chilling-tolerant genotypes later on. [ABSTRACT FROM AUTHOR]

Background & aims: Roots are of paramount importance in protecting grassland in numerous ecosystem services e.g. soil organic matter build-up. However, studies that quantified root biomass in grasslands predominantly focused on areas managed less intensively than the management that is common to most North-West European grassland-based farms. To fill this knowledge gap, we compared, root and stubble biomass, the distribution in the soil and root diameter classes of five common European forage grass species grown under intensive management. Methods: On a 3 year old trial comparing yield of five cool season forage grass species at two N fertilization levels (190 kg N ha yr or 300 kg N ha yr) we sampled root and stubble biomass until a depth of 90 cm deep. Results: Tall fescue ( Festuca arundinacea) displayed the highest root and stubble biomass and had the highest mean root diameters of all studied grass species. The total dry biomass below cutting height (stubble + roots up to a depth of 90 cm below the soil surface) varied between 18 and 19 tonnes ha for tall fescue and 10 and 11 tonnes ha for Festulolium at 190 kg N ha yr and 300 kg N ha yr, respectively. Conclusions: Our findings emphasize that in intensively managed grassland, root and stubble biomass under a 3 years old sward can be as high as 19 t DM ha. Owing to the high forage and root biomass of tall fescue, this species has a high potential in maintaining several ecosystem services. [ABSTRACT FROM AUTHOR]

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have proposed a novel method with a new deep learning-based model and the enhanced data augmentation pipeline to train field images alone and subsequently predict both field images and UAV images for weed segmentation and mapping. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90 % when the spraying resolution is at 1.78 × 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results. To achieve this performance, it is crucial to perform preprocessing techniques that reduce dataset differences between two distinct domains. [ABSTRACT FROM AUTHOR]

Background and aims: Root phenotyping and breeding for desirable root traits have been limited in small seed forage species. The objectives of this study were to analyse the degree of within-species variation regarding initial root allocation in two forage legumes and to assess the covariation between root allocation patterns and root and shoot morphogenetic traits involved in carbon source-sink relationships.Experiments were carried out at a phenotyping platform to grow alfalfa and red clover plants of five contrasting cultivars under nutrient conditions non-limiting for growth. An automatic image analysis pipeline was used to characterise dynamic root-shoot allocation over 50 days during plant establishment. Measurements were also performed to characterise plant traits related to potential shoot and root development.In both species, genetic variability was found regarding the allocation of biomass to roots and root morphological traits controlling root elongation and branching. The degree of variability was high for most root traits, within a range similar to that found for the shoot traits usually targeted by breeders. A clear dependency of initial biomass allocation to roots on the shoot traits controlling plant leaf area, and favouring carbon acquisition, was identified. Analysis of covariation between root traits revealed they were largely independent, suggesting considerable potential for their recombination to achieve improved root phenotypes capturing more soil resources at a given biomass.This study demonstrates the feasibility of direct breeding for root traits in legumes and could help to identify trait combinations that promote their rapid establishment and competitive ability.Methods: Root phenotyping and breeding for desirable root traits have been limited in small seed forage species. The objectives of this study were to analyse the degree of within-species variation regarding initial root allocation in two forage legumes and to assess the covariation between root allocation patterns and root and shoot morphogenetic traits involved in carbon source-sink relationships.Experiments were carried out at a phenotyping platform to grow alfalfa and red clover plants of five contrasting cultivars under nutrient conditions non-limiting for growth. An automatic image analysis pipeline was used to characterise dynamic root-shoot allocation over 50 days during plant establishment. Measurements were also performed to characterise plant traits related to potential shoot and root development.In both species, genetic variability was found regarding the allocation of biomass to roots and root morphological traits controlling root elongation and branching. The degree of variability was high for most root traits, within a range similar to that found for the shoot traits usually targeted by breeders. A clear dependency of initial biomass allocation to roots on the shoot traits controlling plant leaf area, and favouring carbon acquisition, was identified. Analysis of covariation between root traits revealed they were largely independent, suggesting considerable potential for their recombination to achieve improved root phenotypes capturing more soil resources at a given biomass.This study demonstrates the feasibility of direct breeding for root traits in legumes and could help to identify trait combinations that promote their rapid establishment and competitive ability.Results: Root phenotyping and breeding for desirable root traits have been limited in small seed forage species. The objectives of this study were to analyse the degree of within-species variation regarding initial root allocation in two forage legumes and to assess the covariation between root allocation patterns and root and shoot morphogenetic traits involved in carbon source-sink relationships.Experiments were carried out at a phenotyping platform to grow alfalfa and red clover plants of five contrasting cultivars under nutrient conditions non-limiting for growth. An automatic image analysis pipeline was used to characterise dynamic root-shoot allocation over 50 days during plant establishment. Measurements were also performed to characterise plant traits related to potential shoot and root development.In both species, genetic variability was found regarding the allocation of biomass to roots and root morphological traits controlling root elongation and branching. The degree of variability was high for most root traits, within a range similar to that found for the shoot traits usually targeted by breeders. A clear dependency of initial biomass allocation to roots on the shoot traits controlling plant leaf area, and favouring carbon acquisition, was identified. Analysis of covariation between root traits revealed they were largely independent, suggesting considerable potential for their recombination to achieve improved root phenotypes capturing more soil resources at a given biomass.This study demonstrates the feasibility of direct breeding for root traits in legumes and could help to identify trait combinations that promote their rapid establishment and competitive ability.Conclusion: Root phenotyping and breeding for desirable root traits have been limited in small seed forage species. The objectives of this study were to analyse the degree of within-species variation regarding initial root allocation in two forage legumes and to assess the covariation between root allocation patterns and root and shoot morphogenetic traits involved in carbon source-sink relationships.Experiments were carried out at a phenotyping platform to grow alfalfa and red clover plants of five contrasting cultivars under nutrient conditions non-limiting for growth. An automatic image analysis pipeline was used to characterise dynamic root-shoot allocation over 50 days during plant establishment. Measurements were also performed to characterise plant traits related to potential shoot and root development.In both species, genetic variability was found regarding the allocation of biomass to roots and root morphological traits controlling root elongation and branching. The degree of variability was high for most root traits, within a range similar to that found for the shoot traits usually targeted by breeders. A clear dependency of initial biomass allocation to roots on the shoot traits controlling plant leaf area, and favouring carbon acquisition, was identified. Analysis of covariation between root traits revealed they were largely independent, suggesting considerable potential for their recombination to achieve improved root phenotypes capturing more soil resources at a given biomass.This study demonstrates the feasibility of direct breeding for root traits in legumes and could help to identify trait combinations that promote their rapid establishment and competitive ability. [ABSTRACT FROM AUTHOR]

Summary: A plant–microbial bioassay, based on the aquatic macrophyte Lemna minor L. (duckweed), was used to monitor biodegradation of nano- and micromolar concentrations of the phenylurea herbicide linuron. After 7 days of exposure to linuron, log-logistic-based dose–response analysis revealed significant growth inhibition on the total frond area of L. minor when linuron concentrations ≥ 80 nM were added to the bioassay. A plant-protective effect was obtained for all concentrations > 80 nM by inoculation with either a bacterial consortium or Variovorax paradoxus WDL1, which is probably the main actor in this consortium. The outcome of the plant–microbe–toxicant interaction was also assessed using pulse amplitude-modulated chlorophyll a fluorescence and chlorophyll a fluorescence imaging. Linuron toxicity to L. minor became apparent as a significant decrease in the effective quantum yield (Δ F/Fm′ ) within 90 min after exposure of the plants to linuron concentrations ≥ 160 nM. Inoculation of the bioassay with the linuron-degrading bacteria neutralized the effect on the effective quantum yield at concentrations ≥ 160 nM, indicating microbial degradation of these concentrations. The chlorophyll a fluorescence-based Lemna bioassay described here offers a sensitive, fast and cost-effective approach to study the potential of biodegrading microorganisms to break down minute concentrations of photosynthesis-inhibiting xenobiotics. [ABSTRACT FROM AUTHOR]

• Study of eco-physiological plant response dynamics in stress-free conditions. • We acquired a hyperspectral dataset with high spatial and temporal resolution. • Leaf temperature and vapour pressure deficit were estimated with NMSE < 0.23. • PAR and air temperature were estimated with NMSE < 0.06. • Lack of clear treatment limited the model performance of some variables. Plants' dynamic eco-physiological responses are vital to their productivity in continuously fluctuating conditions, such as those in agricultural fields. However, it is currently still very difficult to capture these responses at the field scale for phenotyping purposes. Advanced hyperspectral imaging tools are increasingly used in phenotyping, and have been applied to detect changes in plants in response to a specific treatment, phenological state or monitor its growth and development. Phenotyping has to evolve towards capturing dynamic behaviour under more subtle fluctuations in environmental conditions, without the presence of clear treatments or stresses. Therefore, we investigated the potential of hyperspectral imaging to capture dynamic behaviour of plants in stress-free conditions at a temporal resolution of seconds. Two growth chamber experiments were set up, in which strawberry plants and four different background materials, serving as controls, were monitored by a snapshot hyperspectral camera in variable conditions of light, temperature and relative humidity. The sampling period was set to three seconds, triggering image acquisition and gas exchange measurements. Different background materials were used to assess the influence of the environment and the camera in both experiments. To separate the plant and background data, static masks were determined. Two datasets were created, which encompass both experiments. One dataset was constructed after averaging over the entire mask to acquire one value per spectral band. These values were then used to calculate a set of vegetation indices. The other dataset used spatial subsampling to retain spatial information. From both datasets, linear models were constructed using ridge regression, which estimated the measured eco-physiological and environmental data. Leaf temperature and vapour pressure deficit based on leaf temperature are the two main eco-physiological characteristics that could be predicted successfully. Stomatal conductance, photosynthesis and transpiration rate show less promising results. We suspect that limited variation, and low spectral resolution and range are the main causes of the inability of the models to extract meaningful predictions. Furthermore, the models that were only trained on background data also showed good predictive performance. This is probably because the main drivers for good performing eco-physiological variables are temperature and incident light intensity. Environmental characteristics that have good performance are photosynthetically active radiation and air temperature. Current hyperspectral sensing technologies are not yet able to uncover most plant dynamic eco-physiological responses when plants are cultivated in stress-free conditions. [ABSTRACT FROM AUTHOR]

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations. [ABSTRACT FROM AUTHOR]

In perennial ryegrass breeding programmes, dry‐matter yield (DMY) of individual plots is monitored destructively at the different cuts or derived from non‐destructive canopy height measurements using devices like rising plate meters (RPM). These approaches both have constraints. Destructive sampling implies low temporal resolution, restraining the study of dry‐matter accumulation rates, while RPM measurements are influenced by the canopy structure and limit intra‐field variability identification. We present a phenotyping methodology, based on the use of an affordable RGB camera mounted on an unmanned aerial vehicle (UAV), to monitor the spatial and temporal evolution of canopy height and to estimate DMY. Weekly flights were carried out from April to October above a field comprising a diverse set of accessions. To test the capacity of UAV imagery to estimate canopy height, 8 ground control points and 28 artificial height references were placed at different locations. Accurate flights with an RMSE as low as 0.94 cm were achieved. In addition, canopy height was recorded using an RPM and destructive biomass samples were collected. Different models (linear, multiple linear, principal components, partial least squares regression and random forest) were used to predict DMY, and their performance was evaluated. The best estimations were obtained by combining variables including canopy height, vegetation indices and environmental data in a multiple linear regression (R2 = .81). All models built using UAV data obtained a lower RMSE than the one using RPM data. The approach presented is a possibility for breeders to incorporate new information in their selection process. [ABSTRACT FROM AUTHOR]

As the larger part of the residual soil nitrate in late autumn leaches out during the wet winter months, some regions monitor these residues to convince farmers to adopt improved fertilisation practices in order to reduce nitrate pollution. However, the reduction of the nitrate concentration in the surface and groundwater remains limited, especially in regions with intensive vegetable production. Soil-based advisory systems are recommended to reduce nitrate leaching, but it is unclear if this is sufficient to meet the EU nitrate directives. The objectives of this study were to assess localized fertilizer placement, soil-based fertilization, soil-based fertilizer recommendations and nitrate monitoring policies using a simulation with historical weather data. For this purpose, the ECOFERT model was proposed, a pseudo-2D expansion of the WAVE model to simulate the heterogeneity of vegetable root development. The model was calibrated and validated for a cauliflower-leek rotation on three-year experimental data. The results show that a soil-based advisory system reduces nitrate leaching and residual soil nitrate while maintaining similar biomass production levels compared to a fixed high fertilisation rate. However, in most simulated cases, the fertilisation advice was still too high to meet the legal limits of 50 mg NO 3 l-1 in the percolated water or 85 kg N ha-1 residual soil nitrate in autumn. This was attributed to considerable mineralisation and crop N-uptake variability caused by weather variation compared to fixed values. The simulations identified the potential of localised N-application and soil-based advisory systems but also showed some weaknesses. This will allow researchers to improve N-advisory systems to reduce nitrate pollution. • The pseudo-2D ECOFERT model accurately simulates open-field vegetable cultivation. • Soil-based recommendation and row application decreased nitrate leaching by 18.9%. • Soil-based recommendation could not reduce leachate concentration below 50 mg l-1. • Residual soil nitrate is a good indicator for nitrate leaching in the following year. [ABSTRACT FROM AUTHOR]

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phenotyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized. [ABSTRACT FROM AUTHOR]

The developments in the use of unmanned aerial vehicles (UAVs) and advanced imaging sensors provide new opportunities for ultra-high resolution (e.g., less than a 10 cm ground sampling distance (GSD)) crop field monitoring and mapping in precision agriculture applications. In this study, we developed a strategy for inter- and intra-row weed detection in early season maize fields from aerial visual imagery. More specifically, the Hough transform algorithm (HT) was applied to the orthomosaicked images for inter-row weed detection. A semi-automatic Object-Based Image Analysis (OBIA) procedure was developed with Random Forests (RF) combined with feature selection techniques to classify soil, weeds and maize. Furthermore, the two binary weed masks generated from HT and OBIA were fused for accurate binary weed image. The developed RF classifier was evaluated by 5-fold cross validation, and it obtained an overall accuracy of 0.945, and Kappa value of 0.912. Finally, the relationship of detected weeds and their ground truth densities was quantified by a fitted linear model with a coefficient of determination of 0.895 and a root mean square error of 0.026. Besides, the importance of input features was evaluated, and it was found that the ratio of vegetation length and width was the most significant feature for the classification model. Overall, our approach can yield a satisfactory weed map, and we expect that the obtained accurate and timely weed map from UAV imagery will be applicable to realize site-specific weed management (SSWM) in early season crop fields for reducing spraying non-selective herbicides and costs. [ABSTRACT FROM AUTHOR]

The biomass crop M. x giganteus is susceptible to frost stress. Frost can kill rhizomes during winter or damage new shoots in early spring. To develop new miscanthus varieties with improved rhizome and shoot frost tolerance, a large germplasm collection must first be screened for these traits. We therefore determined the temperature at which 50% of the rhizomes are killed (LT 50 ) in 95 miscanthus genotypes including M. sinensis, M. sacchariflorus, M. x giganteus and interspecific hybrid genotypes. The LT 50 in the collection ranged between −0.4 and −5.9 °C, while the average LT 50 for M. x giganteus was −2.6 ± 0.3 °C. On average LT 50 was −3.5 ± 0.1 °C in M. sinensis , −2.6 ± 0.3 °C in M. sacchariflorus and −3.9 ± 0.2 °C in the M. sinensis x sacchariflorus hybrids. Rhizome frost tolerance was correlated to the timing of flowering and senescence but not to rhizome moisture content. Wide variation in shoot damage was observed in a field trial after a cold spell in early spring. Determination of apex height indicated that the shoot apex was probably still below ground when frost occurred, explaining the rapid recovery of damaged shoots after the frost event. This study is the largest screening of rhizome and shoot frost tolerance in miscanthus reported to date and demonstrates the availability of frost tolerant genotypes in miscanthus breeding material, potentially supporting the development of new frost tolerant varieties. [ABSTRACT FROM AUTHOR]

[Display omitted] • Topsoil compaction affected topsoil water content but not maize growth. • The detriment of subsoil physical properties was worse in the headland zone. • Subsoil compaction restricted root growth and increased nitrogen leaching risks. • Subsoiling promoted maize growth and its effects can be detectable after ∼ 2 years. Mechanical soil compaction increasingly occurs in arable land but the state and effects of historical compaction from regular operations are still poorly documented. Therefore, a typical field was selected and the effects of both newly formed topsoil compaction and historical developed subsoil compaction (below 30 cm) on soil physical and hydraulic properties, soil water movement, soil mineral nitrogen leaching potential and crop parameters were evaluated on a Luvisol under a winter rye (Secale cereale L.) and summer maize (Zea mays L.) rotation system. Three subsoil compaction levels were included, i.e., a i) highly compacted headland zone, and a moderately compacted in-field zone ii) without and iii) with deep tillage by subsoiling in the winter of 2016. Topsoil compaction on the other hand was induced by one machine pass with a rotary cultivator in addition to the regular machine passes during field preparation for the summer maize season of 2017, and compared with a control without extra compaction. For subsoil compaction treatments, both visual evaluation and lab or field based measurements indicated that soil physical and hydraulic properties were deteriorated in the headland compared with in-field zones. As a result, both winter and summer crop above-ground biomass were decreased in the highly compacted headland zone because of the restricted root growth, which further increased the risk of soil mineral nitrogen leaching. The alleviation effect of deep tillage remained after ∼ 2 years, which significantly increased maize height in the extremely dry and hot summer of 2018. For topsoil compaction, one traffic pass with a rotary cultivator increased topsoil penetration resistance and water content but summer maize above-ground biomass was not affected. Overall, prevention and alleviation of subsoil compaction are essential in increasing the resistance and resilience to the continuing deteriorating climatic conditions. [ABSTRACT FROM AUTHOR]

Red clover ( Trifolium pratense L.) is a valuable protein-rich forage crop, but poor seed yields are often a setback, especially in tetraploid varieties. Published work on factors underlying seed yield report contradictory or (sometimes) inconclusive results. Here we report on the effect of 10 traits related to seed yield and flowering on seed yield in 600 genotypes from 15 diploid and 15 tetraploid cultivars from diverse origins. Multiple linear regression (MLR) models indicated that the variation in seed number per plant was well explained by (1) the number of ripe flower heads per plant and (2) the seed number per ripe flower head in diploids (R = 0.953) and tetraploids (R = 0.919). Flower color was not significantly related to seed yield, and corolla tube dimensions were not (or only borderline) significantly related to seed yield. The applicability of our models to predict seed yield was validated on the same set of plants in the following harvest year, and on a totally different dataset available from Vleugels et al. (Plant Breed 134:56-61, ). In the 600-plant dataset and the separate Vleugels et al. dataset, the diploid model explained 94.8 and 53.2 %, respectively, and the tetraploid model explained 88.1 and 64.9 %, respectively, of the variation in seed yield. Our models can therefore predict seed yield with good to fair accuracy in red clover plants grown under various conditions. Breeders can increase seed yield by selecting plants that have numerous ripe flower heads and a high seed number per head. Flower color and corolla tube dimensions can be excluded as breeding targets. [ABSTRACT FROM AUTHOR]

In grasses, leaf growth is often monitored to gain insights in growth processes, biomass accumulation, regrowth after cutting, etc. To study the growth dynamics of the grass leaf, its length is measured at regular time intervals to derive the leaf elongation rate (LER) profile over time. From the LER profile, parameters such as maximal LER and leaf elongation duration (LED), which are essential for detecting inter-genotype growth differences and/or quantifying plant growth responses to changing environmental conditions, can be determined. As growth is influenced by the circadian clock and, especially in grasses, changes in environmental conditions such as temperature and evaporative demand, the LER profiles show considerable experimental variation and thus often do not follow a smooth curve. Hence it is difficult to quantify the duration and timing of growth. For these reasons, the measured data points should be fitted using a suitable mathematical function, such as the beta sigmoid function for leaf elongation. In the context of high-throughput phenotyping, we implemented the fitting of leaf growth measurements into a user-friendly Microsoft Excel-based macro, a tool called LEAF-E. LEAF-E allows to perform non-linear regression modeling of leaf length measurements suitable for robust and automated extraction of leaf growth parameters such as LER and LED from large datasets. LEAF-E is particularly useful to quantify the timing of leaf growth, which forms an important added value for detecting differences in leaf growth development. We illustrate the broad application range of LEAF-E using published and unpublished data sets of maize, Miscanthus spp. and Brachypodium distachyon, generated in independent experiments and for different purposes. In addition, we show that LEAF-E could also be used to fit datasets of other growth-related processes that follow the sigmoidal profile, such as cell length measurements along the leaf axis. Given its user-friendliness, ability to quantify duration and timing of leaf growth and broad application range, LEAF-E is a tool that could be routinely used to study growth processes following the sigmoidal profile. [ABSTRACT FROM AUTHOR]

The challenges of securing future food security will require deployment of innovative technologies to accelerate crop production. Plant phenotyping methods have advanced significantly, spanning low-cost hand-held devices to large-scale satellite imaging. Field-based phenotyping aims to capture plant response to the environment, generating data that can be used to inform breeding and selection requirements. This in turn requires access to multiple representative locations and capacities for collecting useful information. In this paper we identify the current challenges in access to field phenotyping in multiple locations in Europe based on stakeholder feedback. We present a map of current infrastructure and propose opportunities for greater integration of existing facilities for meeting different user requirements. We also review the currently available technology and data requirements for effective multi-location field phenotyping and provide recommendations for ensuring future access and co-ordination. Taken together we provide an overview of the current status of European field phenotyping capabilities and provides a roadmap for their future use to support crop improvement. This provides a wider framework for the analysis and planning of field phenotyping activities for crop improvement worldwide. • Field phenotyping is a common challenge within the area of crop phenotyping. • Capturing infrastructure availability and accessibility supports co-ordination. • Multi-site crop examples provide a framework for future recommendations. • New technologies, common access and data policies will ensure long-term value. • Co-ordinated field phenotyping will support future crop research and breeding. [ABSTRACT FROM AUTHOR]

• Global sensitivity analysis highlighted most influential parameters. • A guiding framework for data collection for process-based crop models was developed. • Calibration data for AquaCrop should include multiple years and soil types. • Soil moisture sensors for continuous monitoring are preferred. • Crop observations every two weeks are advised. The collection of high-quality calibration data is essential for the estimation of parameter values and reliability of crop models. However, few tools are available to quantify the minimum number of observations needed for parameter estimation. We therefore here applied practical identifiability analysis, based on global sensitivity analysis, to design measurement campaigns on farmers' fields. We applied the method for parameterization of the AquaCrop model for mid-early potatoes in Belgium. We generated several virtual observational datasets, considering multiple weather and soil conditions, and measurement frequencies and variables. This analysis resulted in experimental designs where measurement campaigns should be conducted over at least two growing seasons and in different soil types, using soil moisture sensors combined with field observations every two weeks. This method showed to be a useful planning tool for the collection of sufficient data for the calibration of process-based crop models. [ABSTRACT FROM AUTHOR]

The capability of Phalaenopsis to acclimate its photosynthetic capacity and metabolic activity to cool night temperature conditions is crucial for improving orchid production in terms of efficient greenhouse heating. The extent to which Phalaenopsis possesses acclimation potential and the mechanistic background of the metabolic processes involved, have, however, not been studied before. Plants were subjected to a direct and gradual shift from a day to night temperature regime of 28/28-28/16°C, the cold stress and cold acclimation treatment, respectively. In comparison with the cold stress treatment, the cold acclimation treatment led to a higher malate accumulation and a reduction in leaf net CO uptake. Consistently, the contribution of respiratory CO recycling to nocturnal malate synthesis was calculated to be 23.5 and 47.0% for the cold stress and cold acclimation treatment, respectively. Moreover, the lower levels of starch measured in the cold acclimated leaves confirmed the suggested enhanced respiratory CO recycling, implying that Phalaenopsis CAM operation evolved towards CAM idling. It is, however, plausible that this adjustment was not an effect of the low night temperature per se but a consequence of cool-root induced drought stress. Apart from that, at the start of the photoperiod, membrane stability showed a depression which was directly counteracted by an increased generation of glucose, fructose and sucrose. From these observations, it can be concluded that the observed plasticity in CAM operation and metabolic flexibility may be recognized as important steps in the low night temperature acclimation of Phalaenopsis. [ABSTRACT FROM AUTHOR]

Abstract: Rhizodeposit-carbon provides a major energy source for microbial growth in the rhizosphere of grassland soils. However, little is known about the microbial communities that mediate the rhizosphere carbon dynamics, especially how their activity is influenced by changes in soil management. We combined a 13CO2 pulse-labeling experiment with phospholipid fatty acid (PLFA) analysis in differently managed Belgian grasslands to identify the active rhizodeposit-C assimilating microbial communities in these grasslands and to evaluate their response to management practices. Experimental treatments consisted of three mineral N fertilization levels (0, 225 and 450kgNha−1 y−1) and two mowing frequencies (3 and 5 times y−1). Phospholipid fatty acids were extracted from surface (0–5cm) bulk (BU) and root-adhering (RA) soil samples prior to and 24h after pulse-labeling and were analyzed by gas chromatography–combustion-isotope ratio mass spectrometry (GC–c-IRMS). Soil habitats significantly differed in microbial community structure (as revealed by multivariate analysis of mol% biomarker PLFAs) as well as in gram-positive bacterial rhizodeposit-C uptake (as revealed by greater 13C-PLFA enrichment following pulse-labeling in RA compared to BU soil in the 450N/5M treatment). Mowing frequency did not significantly alter the relative abundance (mol%) or activity (13C enrichment) of microbial communities. In the non-fertilized treatment, the greatest 13C enrichment was seen in all fungal biomarker PLFAs (C16:1ω5, C18:1ω9, C18:2ω6,9 and C18:3ω3,6,9), which demonstrates a prominent contribution of fungi in the processing of new photosynthate-C in non-fertilized grassland soils. In all treatments, the lowest 13C enrichment was found in gram-positive bacterial and actinomycetes biomarker PLFAs. Fungal biomarker PLFAs had significantly lower 13C enrichment in the fertilized compared to non-fertilized treatments in BU soil (C16:1ω5, C18:1ω9) as well as RA soil (all fungal biomarkers). While these observations clearly indicated a negative effect of N fertilization on fungal assimilation of plant-derived C, the effect of N fertilization on fungal abundance could only be detected for the arbuscular mycorrhizal fungal (AMF) PLFA (C16:1ω5). On the other hand, increases in the relative abundance of gram-positive bacterial PLFAs with N fertilization were found without concomitant increases in 13C enrichment following pulse-labeling. We conclude that in situ 13C pulse-labeling of PLFAs is an effective tool to detect functional changes of those microbial communities that are dominantly involved in the immediate processing of new rhizosphere-C. [Copyright &y& Elsevier]

High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications. [ABSTRACT FROM AUTHOR]

Abstract Evaluations of soil organic carbon (SOC) stocks are often based on assigning a carbon density to each one of a number of ecosystems or soil classes considered, using data from soil profiles within these categories. A better approach, in which the use of classification methods by which extrapolation of SOC data to larger areas is avoided, can only be used if enough data are available at a sufficiently small scale. Over 190 000 SOC measurements (0–24 cm) have been made in the Flemish cropland (the Northern part of Belgium) in the 1989–2000 period. These SOC data were grouped into 3-year periods and as means plus standard deviation per (part of) community (polygons). This large dataset was used to calculate SOC stocks and their evolution with time, without data extrapolation. Using a detailed soil map, larger spatial groups of polygons were created based on soil texture and spatial location. Linear regression analysis showed that in the entire study area, SOC stocks had decreased or at best had remained stable. In total, a yearly decrease of 354 kton OC yr-1 was calculated, which corresponds with a net CO2 emission of 1238 kton CO2 yr-1 . Specific regions with a high carbon sequestration potential were identified, based on SOC losses during the 1989–2000 period and the mean 1999 SOC content, compared to the average SOC content of soils in Flanders with a similar soil texture. When restoring the SOC stocks to their 1990 level, we estimated the carbon sequestration potential of the Flemish cropland soils to be some 300 kton CO2 yr-1 at best, which corresponds to a 40-year restoration period. In conclusion, we can say that in regions where agricultural production is very intense, carbon sequestration in the cropland may make only a very modest contribution to a country's effort to reduce greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer. [ABSTRACT FROM AUTHOR]

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l'éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant. [ABSTRACT FROM AUTHOR]

• Limited nitrogen availability after roller-crimping reduced cabbage yields. • Late termination of cover crops reduced nitrogen availability for cabbage. • Sufficient fertilisation maintained cabbage yield under roller-crimping. • Termination time and method of cover crops did not affect nitrogen leaching risk. • Roller-crimping changed resource allocation between shoot and root growth of cabbage. Agro-ecological service crops (ASCs), also known as cover crops, green manures or catch crops, can improve organic vegetable production in terms of weed suppression, nitrogen (N) recycling, or addition of N through symbiotic N 2 fixation by legumes. Traditionally, ASCs are terminated through full incorporation into the soil (FI), but alternative termination without tillage is available by roller-crimping (RC). The applicability of RC to Northern and Western European climates has only been studied to a limited extend. The objective of this study was therefore to investigate the N dynamics involved in FI and RC of legume and cereal ASCs in organic cabbage production across Northern and Western European climates. Field experiments were conducted at one location in Estonia and in Denmark, and at three locations in Belgium during two cropping cycles (2015/2016 and 2016/2017) to assess the effect of ASC termination method in spring (FI and RC) and ASC species (pea, pea/cereal mixtures and cereals), compared with a bare soil control (BS), on soil mineral N content, N accumulation, yield, and root growth of a following crop of cabbage. Agro-ecological service crops, and in particular cereals, reduced N availability for succeeding cabbage compared with BS by 37–73%. Similar marketable cabbage yield was obtained under FI and BS in two out of four cases in Belgium, due to higher availability of soil mineral N when terminated earlier (April/May) than RC (May/June). However, FI reduced marketable yields by 36–98 % in the other cases in Belgium and in two out of four cases in Estonia and Denmark, where FI was conducted in May/June. Overall, cabbage marketable yields were reduced by 68–100% under RC compared to BS and FI in seven out of nine cases mainly due to reduced soil mineral N availability. Soil mineral N availability was lower under RC likely because of a slower mineralisation of ASCs and soil organic matter. Besides, the termination of ASCs close to the time of cabbage planting under RC increased pre-emptive competition on N. Cabbage marketable yield could be maintained under RC at fertilisation by 236 kg N ha−1 following pea in Denmark. Cabbage root growth was generally increased with higher above ground plant biomass across treatments in Estonia and Denmark, while the relationship was reverse under RC in East Flanders, indicating changes in resource allocation. Soil mineral N at harvest did not differ among treatments in deeper depth, showing no higher risk of N leaching from FI or RC. In conclusion, RC showed major yield reductions in the first and second year after conversion from spring tillage. Therefore, further investigations should verify improved yields under longer-term RC practice, e.g. due to build-up of soil organic matter, before implementation can be recommended in organic vegetable production in Northern and Western Europe. [ABSTRACT FROM AUTHOR]

Close remote sensing approaches can be used for high throughput on-field phenotyping in the context of plant breeding and biological research. Data on canopy cover (CC) and canopy height (CH) and their temporal changes throughout the growing season can yield information about crop growth and performance. In the present study, sigmoid models were fitted to multi-temporal CC and CH data obtained using RGB imagery captured with a drone for a broad set of soybean genotypes. The Gompertz and Beta functions were used to fit CC and CH data, respectively. Overall, 90.4% fits for CC and 99.4% fits for CH reached an adjusted R2 > 0.70, demonstrating good performance of the models chosen. Using these growth curves, parameters including maximum absolute growth rate, early vigor, maximum height, and senescence were calculated for a collection of soybean genotypes. This information was also used to estimate seed yield and maturity (R8 stage) (adjusted R2 = 0.51 and 0.82). Combinations of parameter values were tested to identify genotypes with interesting traits. An integrative approach of fitting a curve to a multi-temporal dataset resulted in biologically interpretable parameters that were informative for relevant traits. [ABSTRACT FROM AUTHOR]

• A proximal sensing platform was built and evaluated for in-field hyperspectral imaging. • 750 nm, 550 nm and 680 nm were most discriminating for Alternaria solani detection. • Pixel based accuracy was 0.92, object based precision was 0.22 and recall 0.84. Automatic detection of early blight caused by Alternaria solani could promote a drastic reduction in the consumption of plant protection agents and the related production losses. A proximal sensing platform was constructed and calibrated for acquiring high resolution hyperspectral images in the field, and used to accurately map Alternaria lesions. High resolution canopy reflectance images were obtained for 32 potato plants that had been infected with A. solani and 32 healthy reference plants. Spectral classifiers like partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) based on PCA scores were tested to discriminate affected and non-affected pixels. Both spectral classifiers performed well at pixel level with accuracies above 0.92. The NIR region (750 nm) was identified as the most discriminative part of the spectrum for detecting the lesions. As the disease pressure is typically expressed as the number of lesions per area, the accuracy was also evaluated at this level. This indicated a considerable number of false detections at the edges of the leaves and the leaf axils. Therefore, a decision tree was designed based on expert knowledge about the shape of Alternaria lesions, and used to post-process the classified images. This reduced the number of false detections, increasing the precision from 0.17 to 0.22 at the expense of a reduction in recall from 0.88 to 0.84. This leaves considerable room for improvement in the classification accuracy at the object level. We learned that (1) few, broad wavelengths are sufficient and (2) spatial context is essential for the detection of lesions caused by Alternaria infection. The application of more powerful object classification techniques such as convolutional neural networks to enhance the model performance by efficiently encapsulating the spatial context in the classifier might further improve the detection performance. This could pave the way to UAV or tractor based Alternaria mapping. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (Lolium perenne) is a perennial crop used in temperate regions as forage. In L. perenne breeding programs, persistency is an important trait. Poor persistency results in sward degradation and associated yield and nutritive value losses. Breeders assess persistency of accessions using visual scoring in field plots during the 2nd or 3rd growing season. This evaluation system is easy and cheap but is not free from human bias. In this study, the correlation between the scoring done by different breeders was only 0.243. As an alternative we have developed a methodology to assess persistency of L. perenne breeding materials based on vegetation indices (VIs) derived from Unmanned Aerial Vehicle (UAV) imagery. The VIs Excess green (ExG2), Green Leaf Index and Normalized green intensity (GCC) were found to provide consistent results for flights carried out under different light conditions and were validated by ground reference information. The correlation between the VIs and the percentage of ground cover extracted from on-ground imagery was 0.885. To test the implementation of the method we compared the ExG2 value based approach to selection with a visual score based selection methodology as applied by two breeders. The breeding decisions of Breeder A agreed well with decisions based on ExG2 values (74.6%), but those of Breeder B displayed a lower agreement (54.0%). In contrast, agreement between decisions based on different flights was very high (91.6%). The methodology was validated for general applicability. In summary, the results demonstrate that basing persistency selection in L. perenne breeding programs on ExG2 values from UAV imagery is likely to be more objective in comparison to the currently-used visual scoring method. [ABSTRACT FROM AUTHOR]