Search Results (47)

Antimicrobial resistance (AMR) has emerged as one of the most pressing global health challenges of our time. Alarming projections of increasing mortality from resistant infections highlight the urgent need for innovative solutions. While many candidates have shown promise in preliminary studies, they often encounter challenges in terms of efficacy and safety during clinical translation. This review examines cutting-edge approaches to combat AMR, with a focus on engineered antimicrobial peptides, functionalized nanoparticles, and advanced genomic therapies, including Clustered Regularly Interspaced Short Palindromic Repeats-associated proteins (CRISPR-Cas systems) and phage therapy. Recent advancements in these fields are critically analyzed, with a focus on their mechanisms of action, therapeutic potential, and current limitations. Emphasis is given to strategies targeting biofilm disruption and quorum sensing interference, which address key mechanisms of resistance. By synthesizing current knowledge, this work provides researchers with a comprehensive framework for developing next-generation antimicrobials, highlighting the most promising approaches for overcoming AMR through rational drug design and targeted therapies. Ultimately, this review aims to bridge the gap between experimental innovation and clinical application, providing valuable insights for developing effective and resistance-proof antimicrobial agents. Full article

Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. However, the prediction of pineapple quality by hyperspectral data applied to ML is not known. The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. Three inputs were used for each model: leaf reflectance, peel reflectance, and fruit reflectance. The machine learning model SVM, stood out for its best results, demonstrating good generalization capacity and effectiveness in predicting these attributes, reaching accuracy values above 0.7 for Brix and ratio, using fruit reflectance. In terms of the overall efficiency of the input variables, peel and fruit were the most informative, with peel standing out for the estimation of secondary metabolism compounds, while the fruit showed excellent performance in predicting flavor-related attributes, such as acidity, °Brix and ratio, as mentioned previously, above 0.7. These results highlight the potential of using spectral data and machine learning in the non-destructive assessment of pineapple quality, enabling advances in monitoring and selecting fruits with better sensors. Full article

The global transition to a digital era is crucial for society, as most daily activities are driven by digital technologies aimed at enhancing productivity and efficiency in the production of food, fibers, and bioenergy. However, the segregation of digital techniques and equipment in both rural and urban areas poses significant obstacles to technological efforts aimed at combating hunger, ensuring sustainable agriculture, and fostering innovations aligned with the United Nations Sustainable Development Goals (SDGs 02 and 09). Rural regions, which are often less connected to technological advancements, require digital transformation to shift from subsistence farming to market-integrated production. Recent efforts to expand digitalization in these areas have shown promising results. Digital agriculture encompasses terms such as artificial intelligence (AI), the Internet of Things (IoT), big data, and precision agriculture integrating information and communication with geospatial and satellite technologies to manage and visualize natural resources and agricultural production. This digitalization involves both internal and external property management through data analysis related to location, climate, phytosanitary status, and consumption. By utilizing sensors integrated into unmanned aerial vehicles (UAVs) and connected to mobile devices and machinery, farmers can monitor animals, soil, water, and plants, facilitating informed decision-making. An important limitation in studies on nutritional diagnostics is the lack of accuracy validation based on plant responses, particularly in terms of yield. This issue is observed even in conventional leaf tissue analysis methods. The absence of such validation raises concerns about the reliability of digital tools under real field conditions. To ensure the effectiveness of spectral reflectance-based diagnostics, it is essential to conduct additional studies in commercial fields across different regions. These studies are crucial to confirm the accuracy of these methods and to strengthen the development of digital and precision agriculture. Full article

Soybean stands out for being the most economically important oilseed in the world. Remote sensing techniques and precision agriculture are being analyzed through research in different agricultural regions as a technological system aiming at productivity and possible low-cost reduction. Machine learning (ML) methods, together with the advent of demand for remotely piloted aircraft available on the market in the recent decade, have been conducive to remote sensing data processes. The objective of this work was to evaluate the best ML and input configurations in the classification of agronomic variables in different phenological stages. The spectral variables were obtained in three phenological stages of soybean genotypes: V8 (at 45 days after emergence—DAE), R1 (60 DAE), and R5 (80 DAE). A Sensefly eBee fixed-wing RPA equipped with the Parrot Sequoia multispectral sensor coupled to the RGB sensor was used. The Sequoia multispectral sensor with an RGB sensor acquired reflectance at wavelengths of blue (450 nm), green (550 nm), red (660 nm), near-infrared (735 nm), and infrared (790 nm). The following were used to evaluate the agronomic traits: days to maturity, number of branches, productivity, plant height, height of the first pod insertion and diameter of the main stem. The random forest (RF) model showed greater accuracy with data collected in the R5 stage, whose accuracies were close to 56 for the percentage of correct classifications (CC), close to 0.2 for Kappa, and above 0.55 for the F-score. Logistic regression (RL) and support vector machine (SVM) models showed better performance in the early reproductive stage R1, with accuracies above 55 for CC, close to 0.1 for Kappa, and close to 0.4 for the F-score. J48 performed better with data from the V8 stage, with accuracies above 50 for CC and close to 0.4 for the F-score. This reinforces that the use of different specific spectra for each model can enhance accuracy, optimizing the choice of model according to the phenological stage of the plants. Full article

The use of unmanned aerial spraying systems is currently being explored and applied worldwide. The objective of this study was to characterize the droplet population generated by hydraulic nozzles and centrifugal atomization nozzles used in sprayers mounted on remotely piloted aircraft (RPA). Two spray nozzle technologies were tested using a Malvern SprayTech laser particle size meter. The hydraulic nozzle evaluated was model 11001, which generates a wide-use fan spray. The centrifugal atomization nozzle, used in RPA sprayers, was manufactured by Yuenhoang, model DC12V. The experimental design was implemented in a completely randomized scheme, containing variations in the nozzles (hydraulic nozzle and centrifugal atomization nozzle) and application rate (AR) (5, 10, and 15 L ha⁻¹ in the test with the hydraulic nozzle; and 9.2, 12.8, and 15.6 L ha⁻¹ in the test with the centrifugal nozzle), with five replicates per treatment. The hydraulic nozzle test data showed a coefficient of variation of 6.8% VMD for all treatments, with droplet sizes within the fine classification ranging from 132.8 to 163.2 µm. It is noteworthy that the average relative span (span) of the droplet population generated by the hydraulic nozzle was 1.2, i.e., 20% higher than the desired reference value of 1. This value exceeds the general average reported for the centrifugal atomization nozzle, which has a span of 1.1. The relative span of the droplet size distribution for the hydraulic nozzles is greater than that observed with the centrifugal atomization nozzles. Excluding the extreme rotational speeds of the centrifugal atomization nozzle, the percentage of droplets generated with a volume smaller than 100 µm is lower compared to those produced by the hydraulic nozzle. Full article

The use of summarized spectral data in bands obtained by hyperspectral sensors can make it possible to obtain biochemical information about seeds and, thus, relate the results to seed viability and vigor. Thus, the hypothesis of this work is based on the possibility of obtaining information about the physiological quality of seeds through hyperspectral bands and distinguishing seed lots regarding their quality through wavelengths. The objective was then to evaluate the possibility of differentiating soybean genotypes regarding the physiological quality of seeds using spectral data. The experiment was conducted during the 2021/2022 harvest at the Federal University of Mato Grosso do Sul in a randomized block design with four replicates and 10 F3 soybean populations (G1, G8, G12, G15, G19, G21, G24, G27, G31, and G36). After the maturation of each genotype, seeds were harvested from the central rows of each plot, which consisted of five one-meter rows. Seed samples from each experimental unit were placed in a Petri dish to collect spectral data. Readings were performed in the laboratory at a temperature of 26 °C and using two 60 W halogen lamps as the light source, positioned 15 cm between the sensor and the sample. The sensor used was the Ocean Optics (Florida, USA) model STS-VIS-L-50-400-SMA, which captured the reflectance of the seed sample at wavelengths between 450 and 824 nm. After readings from the hyperspectral sensor, the seeds were subjected to tests for water content, germination, first germination count, electrical conductivity, and tetrazolium. The data obtained were subjected to an analysis of variance and the means were compared by the Scott–Knott test at 5% probability, analyzed using R software version 4.2.3 (Auckland, New Zealand). The data on the physiological quality of the seeds of the soybean genotypes were subjected to principal component analysis (PCA) and associated with the K-means algorithm to form groups according to the similarity and distinction between the genetic materials. After the formation of these groups, spectral curve graphs were constructed for each soybean genotype and for the groups that were formed. The physiological quality of the soybean genotypes can be differentiated using hyperspectral bands. The spectral bands, therefore, provide important information about the physiological quality of soybean seeds. Through the use of hyperspectral sensors and the observation of specific bands, it is possible to differentiate genotypes in terms of seed quality, complementing and/or replacing traditional tests in a fast, accurate, and non-destructive way, reducing the time and investment spent on obtaining information on seed viability and vigor. The results found in this study are promising, and further research is needed in future studies with other species and genotypes. The interval between 450 and 649 nm was the main spectrum band that contributed to the differentiation between soybean genotypes of superior and inferior physiological quality. Full article

The objectives of this work are (i) to classify soybean cultivars under different irrigation managements using hyperspectral data, looking for the best machine-learning algorithm for the classification and the input that improves the performance of the models. The experiment was implemented in the 2023/24 harvest in the experimental area of the Federal University of Mato Grosso do Sul, Câmpus Chapadão do Sul, Mato Grosso do Sul, and it was conducted in a strip scheme with seven cultivars subjected to irrigated and rainfed management. Sixty days after crop emergence, three leaves per plot were collected for evaluation by the hyperspectral sensor. The spectral data was then separated into 28 bands to reduce dimensionality. In this way, two databases were generated: one with all the spectral information provided by the sensor (WL) and one with the 28 spectral bands (SB). Each database was subjected to different machine-learning models to ascertain the improved accuracy of the models in distinguishing the different eucalyptus species. The models tested were artificial neural networks (ANN), decision trees (DT), linear regression (LR), M5P algorithm, random forest (RF), and support vector machine (SVM). The results demonstrate the effectiveness of machine-learning models in differentiating soybean management under rainfed and irrigated conditions, highlighting the advantage of hyperspectral data (WL) over selected spectral bands (SB). Models such as the support vector machine (SVM) showed the best levels of accuracy when using the entire available spectrum. On the other hand, artificial neural networks (ANN) performed well with spectral band data, demonstrating their ability to work with smaller data sets without compromising the classification. Full article

Identifying machine learning models that are capable of classifying soybean genotypes according to micronutrient content using only spectral data as input is relevant and useful for plant breeding programs and agricultural producers. Therefore, our objective was to classify soybean genotypes according to leaf micronutrient levels using multispectral images. In the 2019/20 crop year, a field experiment was carried out with 103 F2 soybean populations in the experimental area of the Federal University of Mato Grosso do Sul, in Chapadão do Sul, Brazil. The data were subjected to machine learning analysis using algorithms to classify genotypes according to leaf micronutrient content. The spectral data were divided into three distinct input groups to be tested in the machine learning models: spectral bands (SBs), vegetation indices (VIs), and combining VIs and SBs. The algorithms tested were: J48 Decision Tree (J48), Random Forest (RF), Support Vector Machine (SVM), Perceptron Multilayer Neural Network (ANN), Logistic Regression (LR), and REPTree (DT). All model parameters were set as the default settings in Weka 3.8.5 software. The Random Forest (RF) algorithm outperformed (>90 for CC and >0.9 for Kappa and Fscore) regardless of the input used, demonstrating that it is a robust model with good data generalization capacity. The DT and J48 algorithms performed well when using VIs or VIs+SBs inputs. The SVM algorithm performed well with VIs+SBs as input. Overall, inputs containing information about VIs provided better results for the classification of soybean genotypes. Finally, when deciding which data should serve as input in scenarios of spectral bands, vegetation indices or the combination (VIs+SBs), we suggest that the ease and speed of obtaining information are decisive, and, therefore, a better condition is achieved with band-only inputs. This allows for the identification of genetic materials that use micronutrients more efficiently and the adaptation of management practices. In addition, the decision to be made can be made quickly, without the need for chemical evaluation in the laboratory. Full article

The application of hyperspectral data in machine learning models can contribute to the rapid and accurate determination of caffeine content in coffee beans. This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data for these models that can improve the accuracy of these algorithms. The coffee beans were harvested one year after the seedlings were planted. The fresh beans were taken to the spectroscopy laboratory (Laspec) at the Federal University of Mato Grosso do Sul, Chapadão do Sul campus, for spectral evaluation using a spectroradiometer. For the analysis, the dried coffee beans were ground and sieved for the quantification of caffeine, which was carried out using a liquid chromatograph on the Waters Acquity 1100 series UPLC system, with an automatic sample injector. The spectral data of the beans, as well as the spectral data of the roasted and ground coffee, were analyzed using machine learning (ML) algorithms to predict caffeine content. Four databases were used as input: the spectral information of the bean (CG), the spectral information of the bean with additional clone information (CG+C), the spectral information of the bean after roasting and grinding (CGRG) and the spectral information of the bean after roasting and grinding with additional clone information (CGRG+C). The caffeine content was used as an output to be predicted. Each database was subjected to different machine learning models: artificial neural networks (ANNs), decision tree (DT), linear regression (LR), M5P, and random forest (RF) algorithms. Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. The support vector machine algorithm showed the best accuracy in predicting caffeine content when using hyperspectral data from roasted and ground coffee beans. This performance was significantly improved when clone information was included, allowing for an even more accurate analysis. Full article

The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. The aim of this study was to investigate the associations between spectral variables and soil physicochemical attributes, as well as to predict these attributes using spectral variables as inputs in machine learning models. One thousand soil samples were selected from agricultural areas 0–20 cm deep and collected from Northeast Mato Grosso do Sul state of Brazil. A total of 20 g of the dried and homogenized soil sample was added to the Petri dish to perform spectral measurements. Reflectance spectra were obtained by CROP CIRCLE ACS-470 using three spectral bands: green (532–550 nm), red (670–700 nm), and red-edge (730–760 nm). The models were developed with the aid of the Weka environment to predict the soil chemical attributes via the obtained dataset. The models tested were linear regression, random forest (RF), reptree M5P, multilayer preference neural network, and decision tree algorithms, with the correlation coefficient (r) and mean absolute error (MAE) used as accuracy parameters. According to our findings, sulfur exhibited a correlation greater than 0.6 and a reduced mean absolute error, with better performance for the M5P and RF algorithms. On the other hand, the macronutrients S, Ca, Mg, and K presented modest r values (approximately 0.3), indicating a moderate correlation with actual observations, which are not recommended for use in soil analysis. This soil analysis technique requires more refined correlation models for accurate prediction. Full article

The hypothesis of this study is that it is possible to determine the plant stand in the soybean (Glycine max L. Merril) crop based on the spatial variability of management units, which are limiting factors in maximizing crop yield. Our objectives were as follows: (I) to evaluate the relationship between soil physical and chemical attributes to establish potential management units for variable-rate seeding; (II) to propose a method for varying plant stands based on the law of minimum soil nutrients; an (III) to relate the interaction between different plant stands on soybean grain yield, taking into account the interaction between the spatial variability of the mapped attributes. Field experiments were carried out on two plots over two agricultural years. The areas were seeded by randomly varying the soybean stand across strips in the first year. The most limiting soil nutrient was established and used, together with the soil CEC, to determine management units (MUs), which were also used to seed soybeans in VRT (Variable Rate Technology) in the same plots in the second year. MUs with the lowest restriction for maximizing yield were sown in the second year with the lowest plant stand. Data were processed using multivariate statistics. Our findings reveal that it is possible to establish MUs for seeding soybeans with different stands following the spatial variability of limiting soil nutrients according to the law of the minimum and thus increase the crop grain yield. Spatial variability of potassium (K) in the plot, identified as limiting, affected the spatial variability of grain yield. Decreasing plant stands in MUs with the lowest limitation level increases yield. However, increasing the stand in MUs with a higher limitation level can lead to increased intraspecific competition, affecting yield as well as increasing input costs. Full article

Breeding programs rely on genotype-by-environment interaction (GEI) to recommend cultivars for specific locations. GEI describes how different genotypes perform under varying environmental conditions. Several methods were proposed to assess adaptability and stability across environments. These methods utilize various statistical approaches like parametric and non-parametric regression, multivariate analysis techniques, and even Bayesian frameworks and artificial intelligence. The accessibility of environmental data through platforms like NASA POWER allows breeders to integrate this information into a breeding process. It has been done by using multi-omics integration models that combine data across various biological levels to create accurate predictive models. In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. This work aimed to propose a novel approach that integrates weather information into adaptability and stability analysis, combining SEM with the established Eberhart and Russell model. Additionally, a user-friendly applet, denoted ECERSEM-AdaptStab, was made available to perform the analysis. This approach utilized data from 12 cotton cultivar trials conducted across two growing seasons at 19 sites. This approach successfully integrated environmental covariates into a phenotypic adaptability and stability analysis of cotton cultivars. Specifically, the genotypes TMG 41 WS, IMA CV 690, DP 555 BGRR, BRS 286 and BRS 369 RF were recommended for favorable environments, while the genotypes TMG 43 WS, IMA 5675 B2RF, IMA 08 WS, NUOPAL, DELTA OPAL, BRS 335, and BRS 368 RF are more suitable for unfavorable environments. Full article

Panicum maximum cultivars have distinct characteristics, especially morphological ones related to the leaf structure and coloration, and there may be differences in the spectral behavior captured by sensors. These differences can be used in classification using machine learning (ML) algorithms to differentiate biodiversity within the same species. The objectives of this study were to identify ML models able to differentiate P. maximum cultivars and determine which is the best spectral input for these algorithms and whether reducing the sample size improves the response of the algorithms. The experiment was carried out at the experimental area of the Forage Sector of the School Farm belonging to the Federal University of Mato Grosso do Sul (UFMS). The leaf samples of the cultivars Massai, Mombaça, Tamani, Quênia, and Zuri were collected from experimental plots in the field. Analysis was carried out on 120 leaf samples from the P. maximum cultivars using a VIS/NIR hyperspectral sensor. After obtaining the spectral data and separating them into bands, the data were submitted for ML analysis to classify the cultivars based on the spectral variables. The algorithms tested were artificial neural networks (ANNs), REPTree and J48 decision trees, random forest (RF), and support vector machine (SVM). A logistic regression (LR) was used as a traditional classification method. Two input models were evaluated in the algorithms: the entire spectrum band provided by the sensor (ALL) and another input configuration using the calculated bands. The reflectances from the P. maximum cultivars showed different behavior, especially in the green and NIR regions. RL and ANN algorithms using all information in the spectrum are able to accurately classify the cultivars, reaching accuracies above 70 for CC and above 0.6 for kappa and F-score. VIS/NIR leaf reflectance can be a powerful tool for low-cost, non-destructive, and high-performance analysis to distinguish P. maximum cultivars. Here, we achieved better model accuracy using only 40 leaf samples. In the present study, the J48 decision tree model proved to have good classification performance regardless of the sample size used, which makes it a strategic model for forage cultivar classification studies in smaller or larger datasets. Full article

Agronomic properties are more likely to be impacted by water deficits that affect the nutrient uptake and production of secondary metabolites based on their timing and intensity. The aim of this study was to assess the effects of the water deficit on the nutritional quality of forage sorghum (Sorghum bicolor) hybrids. For that purpose, a factorial, completely randomized experiment was conducted by considering three forage sorghum hybrids (AGRI 002-E, BREVANT SS318, and BRS 658) and two levels of evapotranspiration water replacement (50% and 100% of ETc). Parameters relating to water consumption, secondary metabolites (isoflavones daidzein, daidzin, genistein, and genistin), leaf nutrients (P, K, Ca, Mg, S, Mn, and Zn), and bromatological attributes (dry matter, crude protein, neutral detergent fiber, and mineral material) were evaluated at the end of the crop cycle. Isoflavone levels differed between the hybrids and were highest in water-deficient sorghum. There was a significant interaction between the factors only for the daidzin. The leaf content of the other compounds was influenced either by hybrids (genistein) or by the replacement of evapotranspired water levels (daidzein). The leaf content of P and S was influenced by the interaction between the factors, while the levels of K, Ca, and Mg were influenced by the effect of a single factor. The leaf contents of Mn and Zn were not influenced by the treatments. There was a difference between the hybrids for dry mass and crude protein contents, and hybrids x water deficit was only significant for dry mass. The hybrids Brevant SS318 and BRS 658 had the highest crude protein. The presented results are novel and demonstrate that water deficits can significantly affect the levels of secondary metabolites and the nutritional quality of forage sorghum, depending on the hybrid. The mentioned indices are important parameters for evaluating the nutritional quality and development of agricultural crops, particularly in response to adverse environmental conditions such as water stress. Full article