Practical RGB-to-XYZ Color Transformation Matrix Estimation under Different Lighting Conditions for Graffiti Documentation.

Color data are often required for cultural heritage documentation. These data are typically acquired via standard digital cameras since they facilitate a quick and cost-effective way to extract RGB values from photos. However, cameras' absolute sensor responses are device-dependent and thus not colorimetric. One way to still achieve relatively accurate color data is via camera characterization, a procedure which computes a bespoke RGB-to-XYZ matrix to transform camera-dependent RGB values into the device-independent CIE XYZ color space. This article applies and assesses camera characterization techniques in heritage documentation, particularly graffiti photographed in the academic project INDIGO. To this end, this paper presents COOLPI (COlor Operations Library for Processing Images), a novel Python-based toolbox for colorimetric and spectral work, including white-point-preserving camera characterization from photos captured under diverse, real-world lighting conditions. The results highlight the colorimetric accuracy achievable through COOLPI's color-processing pipelines, affirming their suitability for heritage documentation.

Smartvessel: A New Extinguisher Prototype Based on New Materials and IoT Sensors.

Smartvessel is an innovative fire extinguisher prototype supported by new materials and IoT technology that seeks to improve the functionality and efficiency of conventional fire extinguishers. Storage containers for gases and liquids are essential for industrial activity as they enable higher energy density. The main contributions of this new prototype are (i) innovation in the use of new materials that provide lighter and more resistant extinguishers, both mechanically and against corrosion in aggressive environments. For this purpose, these characteristics are directly compared in vessels made of steel, aramid fiber and carbon fiber with the filament winding technique. (ii) The integration of sensors that allow its monitoring and provide the possibility of predictive maintenance. The prototype is tested and validated on a ship, where accessibility is complicated and critical. For this purpose, different data transmission parameters are defined, verifying that no data are lost. Finally, a noise study of these measurements is carried out to verify the quality of each data. Acceptable coverage values are achieved with very low read noise, on average less than 1%, and a weight reduction of 30% is obtained.

Comprehensive Generation of Historical Construction CAD Models from Data Provided by a Wearable Mobile Mapping System: A Case Study of the Church of Adanero (Ávila, Spain).

This paper presents the results of a complex three-dimensional reconstruction of the church of Nuestra Señora de la Asunción (Ávila, Spain) as an example of a successful process of verticalization from point clouds to a comprehensive computer-aided design (CAD) model. The reconstruction was carried out using the novel and advanced wearable mobile mapping system ZEB-REVO in combination with a lifting pole, in order to cover the whole geometry of the temple and, also, to model the different constructive elements. To this end, a set of good practices was followed, which allowed for passing from reality to the CAD model, such as the use of closed loops or even the use of different parametric and non-parametric strategies to capture the real geometry of the elements. As a result, this paper outlines the main guidelines for passing from point clouds to comprehensive CAD models, the former being useful for the application of smart preventive conservation processes, heritage building information models or even advanced numerical simulations.

Inflammation in Pulmonary Hypertension and Edema Induced by Hypobaric Hypoxia Exposure.

Exposure to high altitudes generates a decrease in the partial pressure of oxygen, triggering a hypobaric hypoxic condition. This condition produces pathophysiologic alterations in an organism. In the lung, one of the principal responses to hypoxia is the development of hypoxic pulmonary vasoconstriction (HPV), which improves gas exchange. However, when HPV is exacerbated, it induces high-altitude pulmonary hypertension (HAPH). Another important illness in hypobaric hypoxia is high-altitude pulmonary edema (HAPE), which occurs under acute exposure. Several studies have shown that inflammatory processes are activated in high-altitude illnesses, highlighting the importance of the crosstalk between hypoxia and inflammation. The aim of this review is to determine the inflammatory pathways involved in hypobaric hypoxia, to investigate the key role of inflammation in lung pathologies, such as HAPH and HAPE, and to summarize different anti-inflammatory treatment approaches for these high-altitude illnesses. In conclusion, both HAPE and HAPH show an increase in inflammatory cell infiltration (macrophages and neutrophils), cytokine levels (IL-6, TNF-α and IL-1ß), chemokine levels (MCP-1), and cell adhesion molecule levels (ICAM-1 and VCAM-1), and anti-inflammatory treatments (decreasing all inflammatory components mentioned above) seem to be promising mitigation strategies for treating lung pathologies associated with high-altitude exposure.

Towards a Combined Use of Geophysics and Remote Sensing Techniques for the Characterization of a Singular Building: "El Torreón" (the Tower) at Ulaca Oppidum (Solosancho, Ávila, Spain).

This research focuses on the study of the ruins of a large building known as "El Torreón" (the Tower), belonging to the Ulaca oppidum (Solosancho, Province of Ávila, Spain). Different remote sensing and geophysical approaches have been used to fulfil this objective, providing a better understanding of the building's functionality in this town, which belongs to the Late Iron Age (ca. 300-50 BCE). In this sense, the outer limits of the ruins have been identified using photogrammetry and convergent drone flights. An additional drone flight was conducted in the surrounding area to find additional data that could be used for more global interpretations. Magnetometry was used to analyze the underground bedrock structure and ground penetrating radar (GPR) was employed to evaluate the internal layout of the ruins. The combination of these digital methodologies (surface and underground) has provided a new perspective for the improved interpretation of "El Torreón" and its characteristics. Research of this type presents additional guidelines for better understanding of the role of this structure with regards to other buildings in the Ulaca oppidum. The results of these studies will additionally allow archaeologists to better plan future interventions while presenting new data that can be used for the interpretation of this archaeological complex on a larger scale.

A New Roadway Eventual Obstacle Detection System Based on Computer Vision.

A new roadway eventual obstacle detection system based on computer vision is described and evaluated. This system uses low-cost hardware and open-source software to detect and classify moving elements in roads using infra-red and colour video images as input data. This solution represents an important advancement to prevent road accidents due to eventual obstacles which have considerably increased in the past decades, mainly with wildlife. The experimental evaluation of the system demonstrated that the proposed solution detects and classifies correctly different types of moving obstacles on roads, working robustly under different weather and illumination conditions.

Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods.

The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an ad-hoc hybrid method that integrates finite element simulation and machine learning models using different predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.

Ambulatory arterial stiffness index in diabetic patients.

Background The ambulatory arterial stiffness index (AASI), derived from 24 h ambulatory blood pressure monitoring (ABPM) can be a good indicator of arterial stiffness. Aim To assess the correlation between AASI and brachial-ankle pulse wave velocity (baPWV), ankle-brachial index (ABI) and cardio-ankle vascular index (CAVI) in patients with type 2 diabetes mellitus without hypertension. Material and Methods Cross sectional study in 28 diabetic patients aged 49 ± 7 years (40% women). AASI was calculated as 1 minus the regression slope of diastolic on systolic blood pressure, using ABPM data. ABPM was measured in the arm using an oscillometric device. ABI was calculated as the ratio between ankle and brachial systolic blood pressure. CAVI was derived from pulse wave velocity using the Vasera VS-1000 device. Correlations were calculated using a bivariate Spearman correlation. Results The mean values for AASI, ABI, baPWV and CAVI were 0.39 ± 0.14, 1.14 ± 0.09, 15.15 ± 2.71 m/s and 7.60 ± 1.90, respectively. There was a significant negative correlation between AASI and ABI (r = -0.491, p < 0.01). Conclusions In these diabetic patients, there was an association between AASI, an arterial stiffness marker and ABI, an indicator for the presence of atherosclerosis.

Behavior Analysis of Novel Wearable Indoor Mapping System Based on 3D-SLAM.

This paper presents a Wearable Prototype for indoor mapping developed by the University of Vigo. The system is based on a Velodyne LiDAR, acquiring points with 16 rays for a simplistic or low-density 3D representation of reality. With this, a Simultaneous Localization and Mapping (3D-SLAM) method is developed for the mapping and generation of 3D point clouds of scenarios deprived from GNSS signal. The quality of the system presented is validated through the comparison with a commercial indoor mapping system, Zeb-Revo, from the company GeoSLAM and with a terrestrial LiDAR, Faro Focus3D X330. The first is considered as a relative reference with other mobile systems and is chosen due to its use of the same principle for mapping: SLAM techniques based on Robot Operating System (ROS), while the second is taken as ground-truth for the determination of the final accuracy of the system regarding reality. Results show that the accuracy of the system is mainly determined by the accuracy of the sensor, with little increment in the error introduced by the mapping algorithm.

Assessment of statistical agreement of three techniques for the study of cut marks: 3D digital microscope, laser scanning confocal microscopy and micro-photogrammetry.

In the last few years, the study of cut marks on bone surfaces has become fundamental for the interpretation of prehistoric butchery practices. Due to the difficulties in the correct identification of cut marks, many criteria for their description and classification have been suggested. Different techniques, such as three-dimensional digital microscope (3D DM), laser scanning confocal microscopy (LSCM) and micro-photogrammetry (M-PG) have been recently applied to the study of cut marks. Although the 3D DM and LSCM microscopic techniques are the most commonly used for the 3D identification of cut marks, M-PG has also proved to be very efficient and a low-cost method. M-PG is a noninvasive technique that allows the study of the cortical surface without any previous preparation of the samples, and that generates high-resolution models. Despite the current application of microscopic and micro-photogrammetric techniques to taphonomy, their reliability has never been tested. In this paper, we compare 3D DM, LSCM and M-PG in order to assess their resolution and results. In this study, we analyse 26 experimental cut marks generated with a metal knife. The quantitative and qualitative information registered is analysed by means of standard multivariate statistics and geometric morphometrics to assess the similarities and differences obtained with the different methodologies.

Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images.

Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

Confronting passive and active sensors with non-Gaussian statistics.

This paper has two motivations: firstly, to compare the Digital Surface Models (DSM) derived by passive (digital camera) and by active (terrestrial laser scanner) remote sensing systems when applied to specific architectural objects, and secondly, to test how well the Gaussian classic statistics, with its Least Squares principle, adapts to data sets where asymmetrical gross errors may appear and whether this approach should be changed for a non-parametric one. The field of geomatic technology automation is immersed in a high demanding competition in which any innovation by one of the contenders immediately challenges the opponents to propose a better improvement. Nowadays, we seem to be witnessing an improvement of terrestrial photogrammetry and its integration with computer vision to overcome the performance limitations of laser scanning methods. Through this contribution some of the issues of this "technological race" are examined from the point of view of photogrammetry. A new software is introduced and an experimental test is designed, performed and assessed to try to cast some light on this thrilling match. For the case considered in this study, the results show good agreement between both sensors, despite considerable asymmetry. This asymmetry suggests that the standard Normal parameters are not adequate to assess this type of data, especially when accuracy is of importance. In this case, standard deviation fails to provide a good estimation of the results, whereas the results obtained for the Median Absolute Deviation and for the Biweight Midvariance are more appropriate measures.

Astaxanthin Supplementation Effects in Right Ventricle of Rats Exposed to Chronic Intermittent Hypobaric Hypoxia.

In Chile, individuals are commonly exposed to high altitude due to the work shift system, involving days of exposure to high altitude followed by days at sea level over the long term, which can result in chronic intermittent hypobaric hypoxia (CIHH). CIHH can cause high-altitude pulmonary hypertension (HAPH), the principal manifestation of which is right ventricular hypertrophy (RVH), in some cases leading to heart failure and eventually death. Studies have shown the contribution of oxidative stress and inflammation to RVH development. Recently, it was determined that the pigment astaxanthin has high antioxidant capacity and strong anti-inflammatory and cardioprotective effects. Therefore, the aim of this study was to determine the effects of astaxanthin on RVH development in rats subjected to CIHH. METHODS: Thirty two male Wistar rats were randomly assigned to the following groups (n = 8 per group): the normoxia with vehicle (NX), normoxia with astaxanthin (NX + AS), chronic intermittent hypobaric hypoxia with vehicle (CIHH), and chronic intermittent hypobaric hypoxia with astaxanthin (CIHH + AS) groups. CIHH was simulated by 2 days in a hypobaric chamber followed by 2 days at sea level for 29 days. RESULTS: Exposure to CIHH induced RVH and increased lipid peroxidation (MDA), Nox2 expression, and SOD activity, however, it decreased pro-IL-1ß expression. Astaxanthin restored oxidative stress markers (Nox2 and MDA), increased GPx activity, and decreased RVH compared to CIHH. CONCLUSION: Astaxanthin alleviates RVH and reduces Nox2 and MDA levels while increasing GPx activity in rats subjected to CIHH. These findings provide new insights of astaxanthin as a new nutraceutical against high-altitude effects.

Using computational learning for non-melanoma skin cancer and actinic keratosis near-infrared hyperspectral signature classification.

BACKGROUND: The early detection of Non-Melanoma Skin Cancer (NMSC) is essential to ensure patients receive the most effective treatment. Diagnostic screening tools for NMSC are crucial due to high confusion rates with other types of skin lesions, such as Actinic Keratosis. Nevertheless, current means of diagnosing and screening patients rely on either visual criteria, that are often conditioned by subjectivity and experience, or highly invasive, slow, and costly methods, such as histological diagnoses. From this, the objectives of the present study are to test if classification accuracies improve in the Near-Infrared region of the electromagnetic spectrum, as opposed to previous research in shorter wavelengths. METHODS: This study utilizes near-infrared hyperspectral imaging, within the range of 900.6 and 1454.8 nm. Images were captured for a total of 125 patients, including 66 patients with Basal Cell Carcinoma, 42 with cutaneous Squamous Cell Carcinoma, and 17 with Actinic Keratosis, to differentiate between healthy and unhealthy skin lesions. A combination of hybrid convolutional neural networks (for feature extraction) and support vector machine algorithms (as a final activation layer) was employed for analysis. In addition, we test whether transfer learning is feasible from networks trained on shorter wavelengths of the electromagnetic spectrum. RESULTS: The implemented method achieved a general accuracy of over 80 %, with some tasks reaching over 90 %. F1 scores were also found to generally be over the optimal threshold of 0.8. The best results were obtained when detecting Actinic Keratosis, however differentiation between the two types of malignant lesions was often noted to be more difficult. These results demonstrate the potential of near-infrared hyperspectral imaging combined with advanced machine learning techniques in distinguishing NMSC from other skin lesions. Transfer learning was unsuccessful in improving the training of these algorithms. CONCLUSIONS: We have shown that the Near-Infrared region of the electromagnetic spectrum is highly useful for the identification and study of non-melanoma type skin lesions. While the results are promising, further research is required to develop more robust algorithms that can minimize the impact of noise in these datasets before clinical application is feasible.

Enhancing Thermal Efficiency in Water Storage Tanks Using Pigmented Recycled Concrete.

The present work investigated the manufacture of elements such as water tanks from recycled concrete for applications where industries require water heating. This proposal leverages precast rejects for recycled concrete and incorporates colouring pigments. It is expected to contribute to the circularity of construction materials (due to the total replacement of natural aggregates by recycled aggregates) as well as to energy and emissions savings, which are attributed to improved thermal performance driven by the thermal behaviour that the coloration pigment gives to the manufactured concrete elements. To assess the efficacy of the proposed solution, on the one hand, mechanical tests were carried out in tensile, compression and modulus of elasticity, which showed a suitable concrete dosage for HA-30 structural concrete. Simultaneously, in search for a material that would increase the internal temperature of the tanks, thermal tests were carried out in a controlled laboratory environment on samples with different percentages of pigment, and an optimum concentration of 1% was obtained. It was also found that the thermal conductivity remained almost unaffected. Finally, two water tank prototypes were manufactured and tested under real environmental conditions: one with the optimised pigment concentration solution and other (the reference tank) without pigment. The results revealed that the colourised tank with the optimal concentration resulted in an average water temperature increase of 2 °C with respect to the reference tank. Finally, the economic and environmental benefits of this temperature increase were studied for industrial processes requiring water heating with a potential saving of 8625 kWh per month.

Surrogate metamodels from digital image correlation for testing high-performance composite vessels.

In this work, a workflow has been developed for the generation of surrogate metamodels to predict and evaluate failure with a confidence above 95 % in initial service conditions of high-performance cylindrical vessels manufactured in composites by Roll Wrapping technology. Currently, there is no specific testing standardization for this type of vessel and to fill this gap probabilistic numerical models were developed, performed by the Finite Element Method, fed with the material characteristics obtained experimentally by 2D digital image correlation from flat specimens. From the initial numerical model, a surrogate metamodel was generated by stochastic approximations. Once the metamodels were obtained by robust engineering, an experimental ring-ring tensile test was developed under service conditions and deformations were measured by high-precision 3D digital image correlation. Parametric and robust tests showed that the results of the metamodel did not show statistically significant differences, with errors in the rupture part of less than 2 % with respect to the results obtained in the test, being proposed as a basis for new test procedures.

Rescue and rehabilitation of maned wolf (Chrysocyon brachyurus) in Paraguay: Case description.

The maned wolf, Chrysocyon brachyurus, is the largest South American canid, with a natural distribution that stretches across Peru, Bolivia, Brazil, Argentina, Paraguay and Uruguay. The present study reports the case of a rescued specimen of maned wolf that underwent a rehabilitation process in Paraguay, starting in October 2020 with its rescue, and finalising in May 2021 with the reintroduction. Herein, we document findings regarding the general management, biometrics, feeding and environmental enrichment; chemical immobilisation and monitoring; haematology, blood biochemistry and specific serology-relevant pathogens; skin examination and bone marrow cytology; orthopaedic, ophthalmological and dental evaluation; abdominal and cardiac ultrasonography; radiology and copro-parasitology. Main findings include the feeding habits of the individual and enrichment opportunities. The animal weighed 7 kg on arrival, with an estimated age of 5 months, and 18 kg on reintroduction, with an estimated age of 1 year. The animal tested negative to serologic tests for Brucella canis, Dirofilaria, canine distemper, Toxoplasmosis and canine parvovirus. Leptospira testing showed antibodies against L. grippotyphosa on both samplings, L. wolffi and L. ictero on the first sampling, and L. pomona on the second sampling. Abdominal organs were examined and measured through ultrasound evaluation and kidneys showed no alterations. Echocardiography showed preserved mitral, tricuspid and aortic valve flows, but turbulent pulmonary valve flow. Copro-parasitology reported the presence of Lagochilascaris sp. and Balantidium sp. All the information gathered aided in diagnosing the health status of the individual, and the response to environmental enrichment helped assess the behaviour, which led to the suggestion of reintroducing the animal. These data constitute the first published health check of a maned wolf in Paraguay, which can contribute to the species' conservation in the country. The protocol presented in this study can serve as a basis for developing an action plan for the maned wolf in Paraguay.

Near-infrared hyperspectral imaging and robust statistics for in vivo non-melanoma skin cancer and actinic keratosis characterisation.

One of the most common forms of cancer in fair skinned populations is Non-Melanoma Skin Cancer (NMSC), which primarily consists of Basal Cell Carcinoma (BCC), and cutaneous Squamous Cell Carcinoma (SCC). Detecting NMSC early can significantly improve treatment outcomes and reduce medical costs. Similarly, Actinic Keratosis (AK) is a common skin condition that, if left untreated, can develop into more serious conditions, such as SCC. Hyperspectral imagery is at the forefront of research to develop non-invasive techniques for the study and characterisation of skin lesions. This study aims to investigate the potential of near-infrared hyperspectral imagery in the study and identification of BCC, SCC and AK samples in comparison with healthy skin. Here we use a pushbroom hyperspectral camera with a spectral range of ≈ 900 to 1600 nm for the study of these lesions. For this purpose, an ad hoc platform was developed to facilitate image acquisition. This study employed robust statistical methods for the identification of an optimal spectral window where the different samples could be differentiated. To examine these datasets, we first tested for the homogeneity of sample distributions. Depending on these results, either traditional or robust descriptive metrics were used. This was then followed by tests concerning the homoscedasticity, and finally multivariate comparisons of sample variance. The analysis revealed that the spectral regions between 900.66-1085.38 nm, 1109.06-1208.53 nm, 1236.95-1322.21 nm, and 1383.79-1454.83 nm showed the highest differences in this regard, with <1% probability of these observations being a Type I statistical error. Our findings demonstrate that hyperspectral imagery in the near-infrared spectrum is a valuable tool for analyzing, diagnosing, and evaluating non-melanoma skin lesions, contributing significantly to skin cancer research.

Recruiting a skeleton crew-Methods for simulating and augmenting paleoanthropological data using Monte Carlo based algorithms.

OBJECTIVES: Data collection is a major hindrance in many types of analyses in human evolutionary studies. This issue is fundamental when considering the scarcity and quality of fossil data. From this perspective, many research projects are impeded by the amount of data available to perform tasks such as classification and predictive modeling. MATERIALS AND METHODS: Here we present the use of Monte Carlo based methods for the simulation of paleoanthropological data. Using two datasets containing cross-sectional biomechanical information and geometric morphometric 3D landmarks, we show how synthetic, yet realistic, data can be simulated to enhance each dataset, and provide new information with which to perform complex tasks with, in particular classification. We additionally present these algorithms in the form of an R library; AugmentationMC. We also use a geometric morphometric dataset to simulate 3D models, and emphasize the power of Machine Teaching, as opposed to Machine Learning. RESULTS: Our results show how Monte Carlo based algorithms, such as the Markov Chain Monte Carlo, are useful for the simulation of morphometric data, providing synthetic yet highly realistic data that has been tested statistically to be equivalent to the original data. We additionally provide a critical overview of bootstrapping techniques, showing how Monte Carlo based methods perform better than bootstrapping as the data simulated is not an exact copy of the original sample. DISCUSSION: While synthetic datasets should never replace large and real datasets, this can be considered an important advance in how paleoanthropological data can be handled.

4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography.

Currently, plant phenomics is considered the key to reducing the genotype-to-phenotype knowledge gap in plant breeding. In this context, breakthrough imaging technologies have demonstrated high accuracy and reliability. The X-ray computed tomography (CT) technology can noninvasively scan roots in 3D; however, it is urgently required to implement high-throughput phenotyping procedures and analyses to increase the amount of data to measure more complex root phenotypic traits. We have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans. Through a cylinder fitting, we automatically extract significant root architectural traits, distribution, and hierarchy. The open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot. In this research, 3D root scans from the black walnut tree were analyzed, a punctual scan for the spatial study and a weekly time-slot series for the temporal one. 4DRoot provides breeders and root biologists an objective and useful tool to quantify carbon sequestration throw trait extraction. In addition, 4DRoot could help plant breeders to improve plants to meet the food, fuel, and fiber demands in the future, in order to increase crop yield while reducing farming inputs.