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Denitrification is one of the key processes of the global nitrogen (N) cycle driven by bacteria. It has been widely known for more than 100 years as a process by which the biogeochemical N-cycle is balanced. To study this process, we develop an individual-based model called INDISIM-Denitrification. The model embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM and is designed to simulate in aerobic and anaerobic conditions the cell growth kinetics of denitrifying bacteria. INDISIM-Denitrification simulates a bioreactor that contains a culture medium with succinate as a carbon source, ammonium as nitrogen source and various electron acceptors. To implement INDISIM-Denitrification, the individual-based model INDISIM was used to give sub-models for nutrient uptake, stirring and reproduction cycle. Using a thermodynamic approach, the denitrification pathway, cellular maintenance and individual mass degradation were modeled using microbial metabolic reactions. These equations are the basis of the sub-models for metabolic maintenance, individual mass synthesis and reducing internal cytotoxic products. The model was implemented in the open-access platform NetLogo. INDISIM-Denitrification is validated using a set of experimental data of two denitrifying bacteria in two different experimental conditions. This provides an interactive tool to study the denitrification process carried out by any denitrifying bacterium since INDISIM-Denitrification allows changes in the microbial empirical formula and in the energy-transfer-efficiency used to represent the metabolic pathways involved in the denitrification process. The simulator can be obtained from the authors on request. [ABSTRACT FROM AUTHOR]

We have developed an individual-based model for denitrifying bacteria. The model, called INDISIM-Paracoccus, embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM, and is designed to simulate the bacterial cell population behavior and the product dynamics within the culture. The INDISIM-Paracoccus model assumes a culture medium containing succinate as a carbon source, ammonium as a nitrogen source and various electron acceptors such as oxygen, nitrate, nitrite, nitric oxide and nitrous oxide to simulate in continuous or batch culture the different nutrient-dependent cell growth kinetics of the bacterium Paracoccus denitrificans. The individuals in the model represent microbes and the individual-based model INDISIM gives the behavior-rules that they use for their nutrient uptake and reproduction cycle. Three previously described metabolic pathways for P. denitrificans were selected and translated into balanced chemical equations using a thermodynamic model. These stoichiometric reactions are an intracellular model for the individual behavior-rules for metabolic maintenance and biomass synthesis and result in the release of different nitrogen oxides to the medium. The model was implemented using the NetLogo platform and it provides an interactive tool to investigate the different steps of denitrification carried out by a denitrifying bacterium. The simulator can be obtained from the authors on request. [ABSTRACT FROM AUTHOR]

Soil organic matter dynamics are essential for terrestrial ecosystem functions as they affect biogeochemical cycles and, thus, the provision of plant nutrients or the release of greenhouse gases to the atmosphere. Most of the involved processes are driven by microorganisms. To investigate and understand these processes, individual-based models allow analyzing complex microbial systems' behavior based on rules and conditions for individual entities within these systems, taking into account local interactions and individual variations. Here, we present a streamlined, user-friendly and open version of the individual-based model INDISIM-SOM, which describes the mineralization of soil carbon and nitrogen. It was implemented in NetLogo, a widely used and easily accessible software platform especially designed for individual-based simulation models. Including powerful means to observe the model behavior and a standardized documentation, this increases INDISIM-SOM's range of potential uses and users, and facilitates the exchange among soil scientists as well as between different modeling approaches. [ABSTRACT FROM AUTHOR]

Highlights: [•] An individual-based model of Saccharomyces cerevisiae is developed. [•] The yeast model is parameterized and calibrated with experimental data. [•] Secondary predictions are used to assess the model adequacy. [•] Ethanol production kinetics were studied in silico. [•] Fermentations started by inocula made up of greater cells show more productivity. [Copyright &y& Elsevier]

The fate of soil carbon and nitrogen compounds in soils in response to climate change is currently the object of significant research. In particular, there is much interest in the development of a new generation of micro-scale models of soil ecosystems processes. Crucial to the elaboration of such models is the ability to describe the growth and metabolism of small numbers of individual microorganisms, distributed in a highly heterogeneous environment. In this context, the key objective of the research described in this article was to further develop an individual-based soil organic matter model, INDISIM-SOM, first proposed a few years ago, and to assess its performance with a broader experimental data set than previously considered. INDISIM-SOM models the dynamics and evolution of carbon and nitrogen associated with organic matter in soils. The model involves a number of state variables and parameters related to soil organic matter and microbial activity, including growth and decay of microbial biomass, temporal evolutions of easily hydrolysable N, mineral N in ammonium and nitrate, CO2 and O2. The present article concentrates on the biotic components of the model. Simulation results demonstrate that the model can be calibrated to provide good fit to experimental data from laboratory incubation experiments performed on three different types of Mediterranean soils. In addition, analysis of the sensitivity toward its biotic parameters shows that the model is far more sensitive to some parameters, i.e., the microbial maintenance energy and the probability of random microbial death, than to others. These results suggest that, in the future, research should focus on securing better measurements of these parameters, on environmental determinants of the switch from active to dormant states, and on the causes of random cell death in soil ecosystems. [Copyright &y& Elsevier]

Varietal control to avoid unwanted varietal mixtures is an important objective for the nursery plant industry. In this study, we have developed and analyzed the capabilities of a computer vision system based on deep learning for the control of plant varieties in the nursery plant industry and for evaluating its capabilities. For this purpose, three datasets of nursery plant images were compared. The datasets came from two varieties of almond trees (Prunus dulcis) named Soleta and Pentacebas. Each dataset contained images with three different scales: whole plant, leaf, and venation. The Gradient‐weighted Class Activation Mapping (Grad‐CAM) technique was used to unveil the most important features to discriminate between both varieties. The three datasets provided classification accuracies above 97% in the test set, being the leaf dataset, with a 98.8% accuracy, the one providing the best results. Concerning the most important features of the plants, the Grad‐CAM showed that they are located in the center of the leaf, that is, the venation. In conclusion, we have shown that computer vision is a promising technique for the control of plant varietal mixtures. [ABSTRACT FROM AUTHOR]

The emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used. The most important variables to discriminate between these varieties were studied through of three scientifically accepted indicators (Variable importance in projection¸ selectivity ratio and vector of the regression coefficients). The results showed that the 7000 to 4000 cm−1 range contains the most useful variables, which allowed to decrease the complexity of the data set. Concerning to the classification models, a high percentage of correct classifications (90–100%) was obtained, where dried-powdered leaves showed better results than fresh leaves. However, the classification rate of both kinds of leaves evidences the capacity of the near-infrared spectroscopy to discriminate Prunus dulcis varieties. We demonstrate with these results the capability of the NIRS technology as a quality control tool in nursery plant industry. [ABSTRACT FROM AUTHOR]

Computer vision coupled to deep learning is a promising technique with multiple applications in the industry. In this work, the potential of this technique has been assessed in the classification of two varieties of almond trees (Prunus dulcis), Soleta and Pentacebas. For that, a convolutional neural network named VGG16 was used. The most appropriate configuration for model training was studied, which included the comparison between two different filling modes (reflect and nearest) in the data augmentation step, the evaluation of the batch size and the analysis of the image sizes. The robustness of the model was also checked, and information was obtained about how the model extracts the information from the images. The results showed that the reflect fill mode was more effective than the nearest one. The best results were obtained using batches with 30 and 40 images, with an image size of (224 × 224) pixels. The verification of the robustness proved the capability of the technique as a promising tool for plant varietal identification. Computer vision and deep learning are promising techniques with multiples usage in the industry. In this work, it was assessed the potential of this technique to classify two varieties of almond trees (Prunus dulcis), Soleta and Pentacebas. To reach that, it was studied the most suitable model configuration, then the robustness of the technique was evaluated. We demonstrate the capability of the computer vision together deep learning as an interesting tool for varietal identification. [ABSTRACT FROM AUTHOR]

Controlled microbial activity is the core of many industrial processes. Such dynamic microbial processes must be carefully studied to optimize their application. They are usually tackled by means of continuous mathematical modelling at the population level (top-down). An alternative approach is individual-based modelling (IbM) (bottom-up). INDISIM is a discrete and spatially explicit IbM. It sets the rules that govern each microbe and its interaction with its local environment, as well as the significant environmental processes. Then it performs simulations that include a large number of microbes, and the behaviour of the whole system emerges. The rules are changed to reproduce the behaviour of microbes depending on the system to be studied. Two adaptations of INDISIM to study yeast fermentations and multi-species composting are presented in this article (INDISIM-YEAST and INDISIM-COMP), proof of INDISIM's versatility. A few representative results are also shown. [ABSTRACT FROM AUTHOR]

Abstract: This work models the dynamics and evolution of carbon (C) and nitrogen (N) related to organic matter in soils by using individual-based simulations. The simulator INDISIM-SOM controls a group of microbial cells at each time step, using a set of time-dependent variables for each microorganism. The space is divided into square cells. In each spatial cell, the amounts of different types of organic compounds are controlled. These are identified as polymerized organic C and N, labile organic C and N, mineral compounds like , , and . The model takes into account the activity of two prototypes of microbial cells: ammonifier microorganisms and nitrifier bacteria. Different metabolic pathways and sources of C and N they can use are identified. Some state variables and parameters related to soil organic matter and microbial activity: growth and decay of microbial biomass, and temporal evolutions of mineralized intermediate N, mineral N in ammonium and nitrate, and are studied. The calibration of the simulation model has made use of data from laboratory incubation experiments performed on two different types of Mediterranean soils. Both, conceptual validation during the development of the model and agreement of the simulation results with experimental data of the short-term dynamics of C and N, provide a direct route from the microscopic details of the system to its macroscopic properties. [Copyright &y& Elsevier]

Site-specific management of crops represents an important improvement in terms of efficiency and efficacy of the different labours, and its implementation has experienced a large development in the last decades, especially for field crops. The particular case of the spray application process for what are called "specialty crops" (vineyard, orchard fruits, citrus, olive trees, etc.) represents one of the most controversial and influential actions directly related with economical, technical, and environmental aspects. This study was conducted with the main objective to find possible correlations between data obtained from remote sensing technology and the actual canopy characteristics. The potential correlation will be the starting point to develop a variable rate application technology based on prescription maps previously developed. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to obtain data to build a canopy vigour map of an entire parcel. By applying the specific software DOSAVIÑA®, the canopy map was then transformed into a practical prescription map, which was uploaded into the dedicated software embedded in the sprayer. Adding to this information precise georeferenced placement of the sprayer, the system was able to modify the working parameters (pressure) in real time in order to follow the prescription map. The results indicate that site-specific management for spray application in vineyards result in a 45% reduction of application rate when compared with conventional spray application. This fact leads to a equivalent reduction of the amount of pesticide when concentration is maintained constant, showing once more that new technologies can help to achieve the goal of the European legislative network of safe use of pesticides. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy (NIRS) can be a faster and more economical alternative to traditional methods for screening varietal mixtures of nursery plants during the propagation process to ensure varietal purity and to avoid errors in the dispatch batches. The global objective of this work was to develop and optimize a NIR spectral collection method for construction of robust multivariate discrimination models. Three different varieties of Prunus dulcis (Avijor , Guara , and Pentacebas) of agricultural interest were used for this study. Sources of variation were investigated, including the position of the leaves on the trees, differences among trees of the same variety, and differences at the varietal level. Three types of processed samples were investigated. Fresh leaves, dried leaves, and dried leaves in powder form were included in each analysis. A study of spectral pre-treatment methods was also performed, and multivariate methods were applied to analyze the influence of different factors on classification. These included principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and ANOVA simultaneous component analysis (ASCA). The results indicated that variety was the most important factor for classification. The spectral pre-treatment that provided the best results was a combination of standard normal variate (SNV), Savitzky-Golay first derivative, and mean-centering methods. With regard to the type of processed sample, the highest percentages of correct classifications were obtained with fresh and dried powdered leaves at both the training set and test set validation levels. This study represents the first step towards the consolidation of NIRS as a method to identify Prunus dulcis varieties. Image 1 • NIRS was used for discriminating between three Prunus dulcis varieties. • Several spectral pre-treatment strategies were investigated. • A combination of SNV, SG first derivative, and mean centering methods was optimal. • Tree variety and leaf age were the most important classification factors for PLS-DA. • NIRS is a rapid and economical method for nursery plant classification. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • DOSAVIÑA is a newly developed DSS giving optimal volume rates/PPP doses in vineyards. • A modified leaf wall area method integrates canopy density/width and sprayer type. • Accurate volume rates from the DSS allow good coverage and uniformity over the canopy. • DSS achieves 20% drop in volume rate and pesticide use while reducing contamination. • DOSAVIÑA is available for IOS and Android; a web version is also available. Abstract DOSAVIÑA is a new tool (website and app for smartphones) developed for calculating the optimal volume rates and pesticide doses to apply during spray application processes in vineyards. DOSAVIÑA also calculates and recommends the optimal working parameters for working pressure, forward speed, and number and types of nozzles. DOSAVIÑA was developed by the Unit of Agricultural Machinery at the Universitat Politècnica de Catalunya, and is available for iOS and Android devices. It is also available on the DOSAVIÑA website (https://dosavina.upc.edu). The developed tool can be used also for the calibration of spray applications on fruit trees (as well as on citrus orchards, olive trees, almond trees, and many other vertical crops) once the volume rate has been established. The system, which is based on a modified version of the leaf wall area (LWA) method, calculates the optimal volume rate for vineyards by considering the effects of leaf density, canopy width, and sprayer type. System testing took biological efficacy into consideration and measured the main factors used for characterizing spray processes, coverage, and distribution over the entire canopy. Results showed that water and pesticide use could be reduced by more than 20% while still meeting economic, environmental, and food quality requirements. The design of the tool is aligned with European requirements concerning pesticide use, as established in the European Directive for a Sustainable Use of Pesticides. [ABSTRACT FROM AUTHOR]

Data from electric particle analysis, light diffraction and flow cytometry analysis provide information on changes in cell morphology. Here, we report analyses of Saccharomyces cerevisiae populations growing in a batch culture using these techniques. The size distributions were determined by electric particle analysis and by light diffraction in order to compare their outcomes. Flow cytometry parameters forward (related to cell size) and side (related to cell granularity) scatter were also determined to complement this information. These distributions of yeast properties were analysed statistically and by a complexity index. The cell size of Saccharomyces at the lag phase was smaller than that at the beginning of the exponential phase, whereas during the stationary phase, the cell size converged with the values observed during the lag phase. These experimental techniques, when used together, allow us to distinguish among and characterize the cell size, cell granularity and the structure of the yeast population through the three growth phases. Flow cytometry patterns are better than light diffraction and electric particle analysis in showing the existence of subpopulations during the different phases, especially during the stationary phase. The use of a complexity index in this context helped to differentiate these phases and confirmed the yeast cell heterogeneity. [ABSTRACT FROM AUTHOR]