Monocular visual SLAM, visual odometry, and structure from motion methods applied to 3D reconstruction: A comprehensive survey.

Monocular Simultaneous Localization and Mapping (SLAM), Visual Odometry (VO), and Structure from Motion (SFM) are techniques that have emerged recently to address the problem of reconstructing objects or environments using monocular cameras. Monocular pure visual techniques have become attractive solutions for 3D reconstruction tasks due to their affordability, lightweight, easy deployment, good outdoor performance, and availability in most handheld devices without requiring additional input devices. In this work, we comprehensively overview the SLAM, VO, and SFM solutions for the 3D reconstruction problem that uses a monocular RGB camera as the only source of information to gather basic knowledge of this ill-posed problem and classify the existing techniques following a taxonomy. To achieve this goal, we extended the existing taxonomy to cover all the current classifications in the literature, comprising classic, machine learning, direct, indirect, dense, and sparse methods. We performed a detailed overview of 42 methods, considering 18 classic and 24 machine learning methods according to the ten categories defined in our extended taxonomy, comprehensively systematizing their algorithms and providing their basic formulations. Relevant information about each algorithm was summarized in nine criteria for classic methods and eleven criteria for machine learning methods to provide the reader with decision components to implement, select or design a 3D reconstruction system. Finally, an analysis of the temporal evolution of each category was performed, which determined that the classical-sparse-indirect and classical-dense-indirect categories have been the most accepted solutions to the monocular 3D reconstruction problem over the last 18 years.

CYCLOPS: A cyclists' orientation data acquisition system using RGB camera and inertial measurement units (IMU).

This paper introduces CYCLOPS, an acquisition system developed to capture images and inertial measurement data of moving cyclists from a vehicle. The development of CYCLOPS addresses the need to acquire useful data for training machine learning models capable of predicting the motion intentions of cyclists on urban roads. Considering its application, it is a completely original development. The system consists of two devices. The first device is installed on the bicycle and is based on an electronic acquisition board comprising an inertial measurement unit (IMU), a microcontroller, and a transceiver for sending the cyclist's acceleration and orientation data to a vehicle. The second device is installed on the vehicle and uses the same board architecture to acquire the vehicle's accelerations and orientations, along with an RGB monocular camera. The data is stored in real-time in a laptop's drive for subsequent analysis and manipulation. The hardware architecture is presented in detail, including the designs to install the devices, for IMUs configuration, and software installation on the laptop. All design and software files required to develop the proposed system are available for download at: doi.org/10.17632/3yx5y8b7tm.1, licensed under the Open-source license CC BY 4.0.

Green Extraction of Natural Colorants from Food Residues: Colorimetric Characterization and Nanostructuring for Enhanced Stability.

Food residues are a promising resource for obtaining natural pigments, which may replace artificial dyes in the industry. However, their use still presents challenges due to the lack of suitable sources and the low stability of these natural compounds when exposed to environmental variations. In this scenario, the present study aims to identify different food residues (such as peels, stalks, and leaves) as potential candidates for obtaining natural colorants through eco-friendly extractions, identify the colorimetric profile of natural pigments using the RGB color model, and develop alternatives using nanotechnology (e.g., liposomes, micelles, and polymeric nanoparticles) to increase their stability. The results showed that extractive solution and residue concentration influenced the RGB color profile of the pigments. Furthermore, the external leaves of Brassica oleracea L. var. capitata f. rubra, the peels of Cucurbita maxima, Cucurbita maxima x Cucurbita moschata, and Beta vulgaris L. proved to be excellent resources for obtaining natural pigments. Finally, the use of nanotechnology proved to be a viable alternative for increasing the stability of natural colorants over storage time.

Feasibility of Using a Cheap Colour Sensor to Detect Blends of Vegetable Oils in Avocado Oil.

This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R2 > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17-3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.

A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points.

Scene classification in autonomous navigation is a highly complex task due to variations, such as light conditions and dynamic objects, in the inspected scenes; it is also a challenge for small-factor computers to run modern and highly demanding algorithms. In this contribution, we introduce a novel method for classifying scenes in simultaneous localization and mapping (SLAM) using the boundary object function (BOF) descriptor on RGB-D points. Our method aims to reduce complexity with almost no performance cost. All the BOF-based descriptors from each object in a scene are combined to define the scene class. Instead of traditional image classification methods such as ORB or SIFT, we use the BOF descriptor to classify scenes. Through an RGB-D camera, we capture points and adjust them onto layers than are perpendicular to the camera plane. From each plane, we extract the boundaries of objects such as furniture, ceilings, walls, or doors. The extracted features compose a bag of visual words classified by a support vector machine. The proposed method achieves almost the same accuracy in scene classification as a SIFT-based algorithm and is 2.38× faster. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and robustness for the 7-Scenes and SUNRGBD datasets.

Use of Digital Images as a Low-Cost System to Estimate Surface Optical Parameters in the Ocean.

Ocean color is the result of absorption and scattering, as light interacts with the water and the optically active constituents. The measurement of ocean color changes enables monitoring of these constituents (dissolved or particulate materials). The main objective of this research is to use digital images to estimate the light attenuation coefficient (Kd), the Secchi disk depth (ZSD), and the chlorophyll a (Chla) concentration and to optically classify plots of seawater using the criteria proposed by Jerlov and Forel using digital images captured at the ocean surface. The database used in this study was obtained from seven oceanographic cruises performed in oceanic and coastal areas. Three approaches were developed for each parameter: a general approach that can be applied under any optical condition, one for oceanic conditions, and another for coastal conditions. The results of the coastal approach showed higher correlations between the modeled and validation data, with rp values of 0.80 for Kd, 0.90 for ZSD, 0.85 for Chla, 0.73 for Jerlov, and 0.95 for Forel-Ule. The oceanic approach failed to detect significant changes in a digital photograph. The most precise results were obtained when images were captured at 45° (n = 22; Fr cal=11.02>Fr crit=5.99). Therefore, to ensure precise results, the angle of photography is key. This methodology can be used in citizen science programs to estimate ZSD, Kd, and the Jerlov scale.

High-Throughput Screening to Examine the Dynamic of Stay-Green by an Imaging System.

The development of RGB (red, green, blue) sensors has opened the way for plant phenotyping. This is relevant because plant phenotyping allows us to visualize the product of the interaction between the plant ontogeny, anatomy, physiology, and biochemistry. Better yet, this can be achieved at any stage of plant development, i.e., from seedling to maturity. Here, we describe the use of phenotyping, based on the stay-green trait, of common bean (Phaseolus vulgaris L.) plant, as a model, stressed by water deficit, to elucidate the result of that interaction. Description is based on interpretation of RGB digital images acquired using a phenomic platform and a specific software. These images allow us to obtain a data group related to the color parameters that quantify the changes and alterations in each plant growth and development.

Theoretical Approach for the Luminescent Properties of Ir(III) Complexes to Produce Red-Green-Blue LEC Devices.

With an appropriate mixture of cyclometalating and ancillary ligands, based on simple structures (commercial or easily synthesized), it has been possible to design a family of eight new Ir(III) complexes (1A, 1B, 2B, 2C, 3B, 3C, 3D and 3E) useful as luminescent materials in LEC devices. These complexes involved the use of phenylpyridines or fluorophenylpyridines as cyclometalating ligands and bipyridine or phenanthroline-type structures as ancillary ligands. The emitting properties have been evaluated from a theoretical approach through Density Functional Theory and Time-Dependent Density Functional Theory calculations, determining geometric parameters, frontier orbital energies, absorption and emission energies, injection and transport parameters of holes and electrons, and parameters associated with the radiative and non-radiative decays. With these complexes it was possible to obtain a wide range of emission colours, from deep red to blue (701-440 nm). Considering all the calculated parameters between all the complexes, it was identified that 1B was the best red, 2B was the best green, and 3D was the best blue emitter. Thus, with the mixture of these complexes, a dual host-guest system with 3D-1B and an RGB (red-green-blue) system with 3D-2B-1B are proposed, to produce white LECs.

Comparative Analysis of the NDVI and NGBVI as Indicators of the Protective Effect of Beneficial Bacteria in Conditions of Biotic Stress.

Precision agriculture has the objective of improving agricultural yields and minimizing costs by assisting management with the use of sensors, remote sensing, and information technologies. There are several approaches to improving crop yields where remote sensing has proven to be an important methodology to determine agricultural maps to show surface differences which may be associated with many phenomena. Remote sensing utilizes a wide variety of image sensors that range from common RGB cameras to sophisticated, hyper-spectral image cameras which acquire images from outside the visible electromagnetic spectrum. The NDVI and NGBVI are computer vision vegetation index algorithms that perform operations from color masks such as red, green, and blue from RGB cameras and hyper-spectral masks such as near-infrared (NIR) to highlight surface differences in the image to detect crop anomalies. The aim of the present study was to determine the relationship of NDVI and NGBVI as plant health indicators in tomato plants (Solanum lycopersicum) treated with the beneficial bacteria Bacillus cereus-Amazcala (B. c-A) as a protective agent to cope with Clavibacter michiganensis subsp. michiganensis (Cmm) infections. The results showed that in the presence of B. c-A after infection with Cmm, NDVI and NGBVI can be used as markers of plant weight and the activation of the enzymatic activities related to plant defense induction.

RGB color sensor for colorimetric determinations: Evaluation and quantitative analysis of colored liquid samples.

In this work, a red, green, blue (RGB) color sensor was used for quantitative optical analysis of colored solutions. The capability of the sensor to respond to different colored solutions was critically evaluated to better understand which spectral bands are filtered and processed by each sensor channel. The effective capability of the RGB sensor, defined as its ability to illuminate and detect electromagnetic radiation reflected by the samples, was observed in the range of 415-564, 440-600 and 510-750 nm for blue, green and red channels, respectively. These results can help understand the interaction between the light emitted by the sensor and the signals obtained by the RGB channels for different quantitative determinations. In order to investigate the interaction between the RGB sensor and colored substances, and thereafter achieve quantitative optical analysis, different colored dyes were chosen to evaluate the RGB sensor capability, thus covering a wide range of colors. The analytical performance of the RGB sensor yielded a linear range of 5.0-50.0 µmol L-1 for dye solutions. The accuracy of this sensor was demonstrated by the thiocyanate method for colorimetric determination of iron in soil and supplement samples. Such RGB sensor achieved analytical performance similar to that obtained with the commercial spectrophotometer, without requiring the use of computers for image processing so as to gather RGB values. Additionally, this sensor also contributes to meeting the requirements of Internet of Analytical Things (IoAT) for the quantitative analysis of colored solutions.

A high-throughput, cheap, and green method for determination of ethanol in cachaça and vodka using 96-well-plate images.

A high-throughput method for the determination of ethanol in vodka and cachaça using 96-well-plate digital images was proposed and validated. The standard method consists of beverage distillation, measuring its density using a pycnometer, and converting it into ethanol content. It is simple, but it is time-consuming and susceptive to error. The proposed method exploits the suppression of phenolphthalein ionization by ethanol in an alkaline solution and the fading of the pink solution was converted into ethanol content. It consists in mixing 1 mL of sample with 0.1 mL of an alkaline phenolphthalein solution. 96-well-plate images were acquired using a desktop scanner. Red, green, and blue (RGB) values from the 96 wells were automatically extracted using ReadPlate (ImageJ plugin). Then, RGB values were exported to a spreadsheet that converted these values into analytical signals and calculated the ethanol content in beverages. The ethanol content of cachaças and vodkas was 33-45% (v/v) and it was also the linear range of the proposed method. The method's precision was evaluated using relative standard deviation (RSD). Five cachaças and three vodkas were analyzed. Each beverage was analyzed six times on the same day (intra-day repeatability) and three consecutive days (inter-day repeatability) by three different analysts (inter-analyst repeatability). The intra-day repeatability average was 1.7% (1.2-2.2% range), the intra-day repeatability average was 2.6% (1.9-3.5% range), and the inter-analyst repeatability average was 4% (2.6-6.2% range). Accuracy was evaluated by comparing the proposed method with the standard method using a percent error and a paired t-test. The average percent error was 1.9%, in the paired t-test, the p-value average value was 0.25. The proposed method can analyze 12 samples in 30 min, whereas the standard method spends around 1 h on each sample. Thus, the proposed method provides high-throughput compared with the standard method.

Validation of Angle Estimation Based on Body Tracking Data from RGB-D and RGB Cameras for Biomechanical Assessment.

Motion analysis is an area with several applications for health, sports, and entertainment. The high cost of state-of-the-art equipment in the health field makes it unfeasible to apply this technique in the clinics' routines. In this vein, RGB-D and RGB equipment, which have joint tracking tools, are tested with portable and low-cost solutions to enable computational motion analysis. The recent release of Google MediaPipe, a joint inference tracking technique that uses conventional RGB cameras, can be considered a milestone due to its ability to estimate depth coordinates in planar images. In light of this, this work aims to evaluate the measurement of angular variation from RGB-D and RGB sensor data against the Qualisys Tracking Manager gold standard. A total of 60 recordings were performed for each upper and lower limb movement in two different position configurations concerning the sensors. Google's MediaPipe usage obtained close results compared to Kinect V2 sensor in the inherent aspects of absolute error, RMS, and correlation to the gold standard, presenting lower dispersion values and error metrics, which is more positive. In the comparison with equipment commonly used in physical evaluations, MediaPipe had an error within the error range of short- and long-arm goniometers.

Electronic Eye Based on RGB Analysis for the Identification of Tequilas.

The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image's pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers' performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen's kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.

Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (P l a t F W ) and multi-rotor (P l a t M R ) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using P l a t F W were consistent with those obtained from P l a t M R , showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R 2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R 2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06-0.97). Finally, we also observed high Spearman rank correlations (0.47-0.91) and R 2 (0.63-0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.

Near infrared spectroscopy and smartphone-based imaging as fast alternatives for the evaluation of the bioactive potential of freeze-dried açai.

The development of green analytical techniques for food industry quality control has become an important issue in the context of the fourth industrial revolution. In this sense, near infrared spectroscopy (NIR) and smartphone-based imaging (SBI) were applied to evaluate the bioactive potential of freeze-dried açai pulps. For this purpose, reference results of ninety-six samples were obtained by determining total anthocyanins (TAC), polyphenol content (TPC), and antioxidant capacity (DPPH, ORAC and TEAC) by traditional methods and correlated to NIR spectra and SBI to build predictive models based on partial square least (PLS) regression. In summary, the NIR-PLS models showed better performance for predicting the TAC, TPC and antioxidant capacity of studied samples; considering the parameters of merit, such as coefficient of determination (0.8) and residual prediction deviation (RPD) (2.2) compared to the SBI-PLS models (0.7 and lower 1.5, respectively). The better performance of NIR-PLS could be potentially justified by a higher sensitivity of the NIR equipment than the smartphone images. In conclusion, these results show that the proposed alternative methods are promising tools for the future context of the 4.0 food industry.

Genetic Yield Gains and Changes in Morphophysiological-Related Traits of Winter Wheat in Southern Chilean High-Yielding Environments.

Both the temperate-humid zone and the southern part of the Mediterranean climate region of Chile are characterized by high wheat productivity. Study objectives were to analyze the yield potential, yield progress, and genetic progress of the winter bread wheat (Triticum aestivum L.) cultivars and changes in agronomic and morphophysiological traits during the past 60 years. Thus, two field experiments: (a) yield potential and (b) yield genetic progress trials were conducted in high-yielding environments of central-southern Chile during the 2018/2019 and 2019/2020 seasons. In addition, yield progress was analyzed using yield historical data of a high-yielding environment from 1957 to 2017. Potential yield trials showed that, at the most favorable sites, grain yield reached ∼20.46 Mg ha-1. The prolonged growing and grain filling period, mild temperatures in December-January, ample water availability, and favorable soil conditions explain this high-potential yield. Yield progress analysis indicated that average grain yield increased from 2.70 Mg ha-1 in 1959 to 12.90 Mg ha-1 in 2017, with a 128.8 kg ha-1 per-year increase due to favorable soil and climatic conditions. For genetic progress trials, genetic gain in grain yield from 1965 to 2019 was 70.20 kg ha-1 (0.49%) per year, representing around 55% of the yield progress. Results revealed that the genetic gains in grain yield were related to increases in biomass partitioning toward reproductive organs, without significant increases in Shoot DW production. In addition, reducing trends in the NDVI, the fraction of intercepted PAR, the intercepted PAR (form emergence to heading), and the RGB-derived vegetation indices with the year of cultivar release were detected. These decreases could be due to the erectophile leaf habit, which enhanced photosynthetic activity, and thus grain yield increased. Also, senescence of bottom canopy leaves (starting from booting) could be involved by decreasing the ability of spectral and RGB-derived vegetation indices to capture the characteristics of green biomass after the booting stage. Contrary, a positive correlation was detected for intercepted PAR from heading to maturity, which could be due to a stay-green mechanism, supported by the trend of positive correlations of Chlorophyll content with the year of cultivar release.

A novel digital image method for determination of reducing sugars in aged and non-aged cachaças employing a smartphone.

For the first time, a method is proposed for colorimetric determination of reducing sugars in cachaça employing digital image and a smartphone as detector. The method was based on the reduction of Cu(II) to Cu(I) by sugars and followed by the formation of a colored Cu(I)-Neocuproine complex. A calibration curve was linear from 0.1 to 15 g L-1 for glucose and fructose with limits of detection of 0.012 g L-1 and 0.010 g L-1, respectively. It was observed that the non-aged cachaças, known for having inferior flavors and aromas, had a reducing sugar content three times higher than the aged cachaças, once a common practice among producers is to add sugar to adjust sensory deficits in the final product. Furthermore, the method is simple, does not require complex technical knowledge and it could be used as a tool to check possible fraud, adulteration or non-compliance to the law.

3D-printed and fully portable fluorescent-based platform for sulfide determination in waters combining vapor generation extraction and digital images treatment.

The determination of sulfide anion in a variety of waters (e.g. wastewaters and natural waters) even at low concentration (i.e. in the µM range) is essential due to its high toxicity, corrosivity and unpleasant smelling proprieties. Despite several methodologies are dedicated to aqueous sulfide determination, most of them need sampling/transport steps - which is no adequate to sulfide due to its reactivity and instability - resulting in critical analytical bias. In this study, we present a fully modular and portable 3D-printed platform for in-situ aqueous sulfide determination. The analytical device is based on H2S vapor generation from the sulfide sample solution by addition of H3PO4 followed by collection in a miniaturized cuvette (µCuvette) containing few microliters of Fluorescein Mercury Acetate (FMA), a fluorescent dye. The chemical reaction results in fluorescence quenching of the dye at 530 nm when excited at 470 nm. A light-emitted diode (LED) emitting at 470 nm and powered with 9 V-battery based circuitry was employed to provide stable excitation light source at 20 mA. Digital images from the light emitted by FMA were captured by a smartphone and the Green channel intensity was used as analytical signal. Under optimized conditions, a linear relation (r2 > 0.99) from 0.1 to 5 µM of sulfide was obtained using 10 mL of standard/sample solution. The portable platform was applied to the in-situ monitoring of sulfide in tap water and river water with no loss of analyte, no need for external power supplies or powered pumps. and the analysis results were obtained in 20 min. The proposed device shows advantages in terms of high degree of portability, low-power consumption, easiness to use, minimal use of reagents yet enabling on-site determination of sulfide with high sensitivity. By using the vapor generation approach combined with the modular building blocks concept presented herein for the first time, we anticipate the development of a tailored "plug-and-play" platform enabling the multiplexed determination of volatile substances using absorbance, reflectance or fluorescence measurements with smartphones.

Evaluation of the nutritional status of corn by vegetation indices via aerial images / Avaliação do estado nutricional do milho por índices de vegetação de imagens aéreas

The objective of this study is to determine the vegetation indices (IV) as a means of identifying the nutritional status of corn, with respect to the soil nitrogen and potassium, using the aerial images received through an RGB camera loaded on an unmanned aerial vehicle. The images were obtained for an experiment of the nitrogen levels (0, 60, 120 and 180 kg ha-¹) and potassium levels (0, 50, 100 and 150 kg ha-¹), in the random block design, with a factorial scheme of 4 x 4, having three repetitions. Ten leaves were plucked per plot during the flowering phase to assess the total N (NF) and K+ leaf contents. The Pearsons correlation analysis, as well as the analyses of variance and regression between the IV and the concentrations of N and K2O. NF, K+ and the grain yield, responded only to the soil N levels. A significant correlation was observed for the indices of Red Index, Normalized Difference Index and Visible Atmospherically Resistant Index with the NF, which endorses them as favorable in identifying the nutritional standing of corn, with respect to the N level. Not even a single one of the indices evaluated could detect the nutritional ranking of corn in the context of the potassium level.(AU)

O estudo teve como objetivo avaliar índices de vegetação (IV) para detecção do status nutricional do milho com relação ao nitrogênio e potássio por meio de imagens aéreas obtidas por câmera RGB embarcada em veículo aéreo não tripulado. As imagens foram adquiridas em ensaio de níveis de nitrogênio (0, 60, 120 e 180 kg ha-¹) e potássio (0, 50, 100 e 150 kg ha-¹), em blocos ao acaso, fatorial 4 x 4, com três repetições. Coletaram-se dez folhas por parcela na fase de florescimento para avaliação do teor foliar de N total (NF) e K+. Efetuou-se análise de correlação de Pearson, análise de variância e de regressão entre os IV e os níveis de N e de K2O. NF, K+ e a produtividade de grãos responderam apenas aos níveis de N no solo. Houve correlação significativa para os índices Excess Red Index, Normalized Difference Index e Visible Atmospherically Resistant Index com o NF, que os credencia como promissores na detecção do status nutricional do milho em relação ao N. Nenhum dos índices avaliados foi capaz de detectar o status nutricional do milho com relação ao potássio.(AU)

Producción de leche real vs la calculada a partir de la ENL estimada por el algoritmo de análisis de imágenes red-green-blue de gramíneas / Produção real de leite vs produção de leite calculada a partir da energía líquida de lactação estimada pelo algoritmo para análise de imagens vermelho-verde-azul de gramíneas

Knowledge about the net lactation energy (NLE) contained in the dry matter of grasses is necessary to make decisions about forage and the balance of diets for grazing cattle. Its determination is made in laboratories using wet or dry chemistry methods, which are costly, delayed, and sometimes present sampling- or process-related reliability problems. An algorithm, which analyzes the red-green-blue (RGB) images of grasses taken by drone, has been developed as a technological alternative. This has allowed us estimating the NLE level, reducing costs, and changing the sampling system and analysis method. The objective of the present study was to compare the milk production, which was calculated from the NLE and estimated using the algorithm for analysis of RGB images of grasses (included in the TaurusWebs® software), vs the actual milk production. The study was conducted in 15 dairy farms belonging to the dairy control system of the Colácteos dairy cooperative, which are located in the upper tropical region (Department of Nariño, Colombia). The prairies evaluated were composed of mixtures of Kikuyo (Pennisetum clandestinum), Raigrás (Lolium spp), and False Poa (Holcus lanatus). The result was analyzed using a linear regression model (R²=0.86; R=0.93). In the Student´s t-test, the actual and estimated milk production averages were equal (P>0.05). In conclusion, the NLE calculated using the algorithm satisfactorily explains the study livestock production, and the information generated by the algorithm can be used to calculate the NLE of grasses.(AU)

O conhecimento sobre a energia líquida de lactação (NLE) contida na matéria seca das gramíneas é necessário para a tomada de decisões sobre forragem e o equilíbrio das dietas para pastagem. Sua determinação é feita em laboratórios usando métodos de química úmida ou seca, que são dispendiosos, atrasados e às vezes apresentam problemas de confiabilidade relacionados a amostras ou processos. Um algoritmo, que avalia as imagens vermelho-verde-azul (RGB) de gramíneas tiradas por drone, foi desenvolvido como uma alternativa tecnológica. Isso nos permitiu estimar o nível de NLE, reduzir custos e alterar o sistema de amostragem e o método de análise. O objetivo do presente estudo foi comparar a produção de leite, calculada a partir do NLE e estimada usando o algoritmo para análise de imagens RGB de gramíneas (incluídas no software TaurusWebs®) versus a produção real de leite. O estudo foi realizado em 15 fazendas leiteiras pertencentes ao sistema de controle de laticínios da cooperativa de laticínios Colácteos, localizada na região tropical superior (Departamento de Nariño, Colômbia). As pradarias avaliadas foram compostas por misturas de Kikuyo (Pennisetum clandestinum), Raigrás (Lolium spp) e False Poa (Holcus lanatus). O resultado foi analisado usando um modelo de regressão linear (R² = 0,86; R = 0,93). No teste t de Student, as médias reais e estimadas de produção de leite foram iguais (P> 0,05). Em conclusão, o NLE calculado usando o algoritmo explica satisfatoriamente a produção animal estudada, e as informações geradas pelo algoritmo podem ser usadas para calcular o NLE das gramíneas.(AU)