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The purpose of this work is to classify pepper seeds using color filter array (CFA) images. This study focused specifically on Penja pepper, which is found in the Litoral region of Cameroon and is a type of Piper nigrum. India and Brazil are the largest producers of this variety of pepper, although the production of Penja pepper is not as significant in terms of quantity compared to other major producers. However, it is still highly sought after and one of the most expensive types of pepper on the market. It can be difficult for humans to distinguish between different types of peppers based solely on the appearance of their seeds. To address this challenge, we collected 5618 samples of white and black Penja pepper and other varieties for classification using image processing and a supervised machine learning method. We extracted 18 attributes from the images and trained them in four different models. The most successful model was the support vector machine (SVM), which achieved an accuracy of 0.87, a precision of 0.874, a recall of 0.873, and an F1-score of 0.874. [ABSTRACT FROM AUTHOR]

On average, 70% of the world's freshwater is used in agriculture, with farmers transitioning to electrical irrigation systems to increase productivity, reduce climate uncertainties, and decrease water consumption. In Brazil, where agriculture is a significant part of the economy, this transition has reached record levels over the last decade, further increasing the impact of energy consumption. This paper presents a methodology that utilizes the U-Net model to detect flooded rice fields using Sentinel-2 satellite images and estimates the electrical energy consumption required to pump water for this irrigation. The proposed approach involves grouping the detected flooded areas using k-means clustering with the electricity customers' geographical coordinates, provided by the Power Distribution Company. The methodology was evaluated in a dataset of satellite images from southern Brazil, and the results demonstrate the potential of using U-Net models to identify rice fields. Furthermore, comparing the estimated electrical energy consumption required for irrigation in each cluster with the billed energy values provides valuable insights into the sustainable management of rice production systems and the electricity grid, helping to identify non-technical losses and improve irrigation efficiency. [ABSTRACT FROM AUTHOR]

The differentiation between the main crop and weeds is an important step for selective spraying systems to avoid agrochemical waste and reduce economic and environmental impacts. In this sense, this study aims to classify and map the area occupied by weeds, determine the percentage of area occupied, and indicate treatment control strategies to be adopted in the field. This study was conducted by using a yellow Bourbon cultivar (IAC J10) with 1 year of implementation on a commercial coffee plantation located at Minas Gerais, Brazil. The aerial images were obtained by a remotely piloted aircraft (RPA) with an embedded multispectral sensor. Image processing was performed using PIX4D, and data analysis was performed using R and QGIS. The random forest (RF) and support vector machine (SVM) algorithms were used for the classification of the regions of interest: coffee, weed, brachiaria, and exposed soil. The differentiation between the study classes was possible due to the spectral differences between the targets, with better classification performance using the RF algorithm. The savings gained by only treating areas with the presence of weeds compared with treating the total study area are approximately 92.68%. [ABSTRACT FROM AUTHOR]

We applied a robust framework for agricultural drought identification in the State of Espírito Santo, Brazil, by employing the Vegetation Condition Index (VCI) based on data obtained through the Enhanced Vegetation Index (EVI). By doing so, we analyzed the interrelationships between the VCI and anomalies in the Land Surface Temperature (LST), along with connections between the VCI and data considering water deficits in vulnerable areas. When it came to image processing, we focused on the use of analytics and GIS algorithms, while the Scott–Knott method elucidated the statistical analyses. Consequently, we identified drought areas followed by periods susceptible to their occurrence, indicating 2016 as the driest year. The North macroregion presented the lowest average values regarding VCI values in the most vulnerable periods, followed by the Central one. We also call attention to the highest LST averages observed in 2015 and 2016, as strong El Niño events marked the same timeframe periods. The methodological approach was efficient for the identification, analysis, and characterization of agricultural drought occurrences, enabling mitigation actions, as well as the management of the exploitation and protection of water resources. Moreover, further research should be conducted by incorporating other indices to enhance the understanding of agricultural drought and its effects on vegetation. [ABSTRACT FROM AUTHOR]

Copyright of Revista Caatinga is the property of Revista Caatinga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas' rotation, especially the eucalypt's short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations' characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales. [ABSTRACT FROM AUTHOR]

Coriander seeds have high socio-economic value in several regions of Brazil, especially in the North and Northeast. Seed maturation determined by color influences the seed quality. With this, digital image processing has become an important tool for separating seeds by color since this classification is usually performed by humans and is highly susceptible to error. The study established parameters for separating coriander seeds by red green and blue (RGB) image analysis, seeking a better selection of coriander seeds according to their color, and evaluating the physiological quality by the germination test. Separation was carried out from two coriander seed lots to obtain samples of 20 g each in three shades: yellowish, gray, and mixed. Images were acquired by the HP C4480 Scanner and processed in the MATLAB software; then, a histogram was constructed for each color analyzed in each sample by the RGB system. ANOVA tested the averages of the scales to ratify the difference in the components' distributions. The germination test was performed to confirm the results of seed separation using image analysis. The best selection of coriander seeds was achieved by the blue scale, and the germination test indicated that yellow seeds have a higher physiological quality than brownish/greyish seeds. [ABSTRACT FROM AUTHOR]

Since the approval of the Brazilian Federal Technical Assistance Law, whose objective is to guarantee adequate technical assistance to the residents of poor areas to improve their homes, several municipalities have implemented programs, not always successful, to improve housing in slums. Aiming to subsidize these programs implementation workflow, the present paper analyses use of drones as a tool to gather information about the physical characteristics slum households in Salvador de Bahia city, Brazil. As an experiment we flight over a single census sector within the Alto das Pombas slum, and after image processing we extracted and organized the collected data, extracting the possible information that could be applied to identify and quantify the most precarious houses that could be prioritized from a health improvement perspective. We conclude that many of the necessary data needed, on the urbanistic scale in Brazilian slums, can benefit from drone photogrammetry at low cost and fast execution. [ABSTRACT FROM AUTHOR]

Leaf area is an important growth variable in agricultural crops and the leaf is the main variable of interest in the tobacco industry. So, the aim of this scientific research was to estimate the Burley tobacco leaf area by linear dimensions of the leaves and to determine which mathematical model is more adequate for this purpose. Two experiments were carried out with Burley tobacco, cultivar DBH 2252, in 2016/2017 and 2018/2019 agricultural years, respectively, in the municipalities of Itaqui and Vanini - RS - Brazil. In 600 leaves were measured length (L), width (W), length×width product (LW), length/width ratio (L/W) and determined the real leaf area (LA). Four hundred and fifty leaves were separated to generate models of the leaf area as a function of linear dimension and the other 150 leaves were used for model's validation. The power model LA = 0.5037LW1.04435 (R² = 0.9960) is the most adequate for Burley tobacco 'DBH 2252' leaf area estimation. Alternatively, the models LA=2.0369W1.8619 (R²=0.9796) and LA=0.1222L2.2771 (R²=0.9738) based on width and length, respectively, can be used when only one leaf dimension is measured. [ABSTRACT FROM AUTHOR]

The objective of the study is to analyze the development of research in Education in Brazil. What theoretical / methodological problems limit the work of researchers?. This work is the result of reflections and studies carried out in the Graduate Program in Education at the Federal University of Triângulo Mineiro (UFTM). It seeks to characterize, by means of bibliographic survey, the current scenario of qualitative research, to present the contemporary educational context in the country, as well as its obstacles. The transition from spontaneous to rational knowledge is explained, showing the differences between scientific method in Natural and Human Sciences. A historical picture of the maturation process of research in education is outlined, the methodological issues, challenges and relevance of research for the advancement of knowledge in the area. The idea is defended that adequate qualitative research can point out ways to improve Brazilian education. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Gully erosion represents the most severe soil loss, with far-reaching consequences beyond the immediate site. Assessing the stability of gullies is particularly challenging in tropical regions with sandy soils and limited accurate data. Nonetheless, initiating gully inventories is a crucial first step in guiding public policies and conservation projects. In this study, we focus on the Upper Taquari River Basin (UTRB) situated on the fringes of the Brazilian Pantanal, where extensive erosion occurs in the upper regions and flooding occurs in the plains. We present the first qualitative and quantitative analysis of gullies in this region. Considering the historical context of the UTRB, it has long suffered from land mismanagement, particularly in livestock activities. Our objective was to evaluate the correspondence between gullies and land use classes in the MapBiomas Project, Brazil's most reliable non-governmental land use map, and the Rural Environmental Registry (CAR), the official information shared between landowners and public authorities. Thirteen remote-sensed indicators encompassing vegetation, water, soil, and terrain indices were assessed for 2022. Gullies were digitized through visual interpretation of a high-resolution Maxar Vivid Basic 2017 image. The classification was performed using the Random Forest (RF) algorithm, wherein pixels were classified into three classes: active, intermediate, and stable, based on the degree of vegetation cover and bare soil. The agreement of the gullies with the features of MapBiomas and CAR was also examined. The results revealed an overall accuracy of 96% and a Kappa index of 93% for the pixel classification. In the study area, 2960 gullies were digitized, with 60% classified as active features and only 2% as stable. Furthermore, the MapBiomas algorithm misclassified many pixels with active gullies as pasture. Conversely, the CAR data failed to identify gullies as areas demanding restoration. To address these issues, we recommend revising both land use maps to accurately represent the presence of erosions and improve decision-making that favors efficient conservation efforts of the region. As a further result of our actions, the method described here may prove valuable in formulating restoration plans for other tropical savanna regions. [Display omitted] • Visual interpretation and machine learning provide accurate gully mapping. • The percentage of vegetation cover is a viable metric for evaluating pixels. • Active pixels were misinterpreted in CAR and MapBiomas maps. • Include erosion class into land use platforms would help guide for prompt recovery. • Our method serves as previous inventory at severely degraded basins at low cost. [ABSTRACT FROM AUTHOR]

The world production of meats is mainly concentrated in the countries with a hot climate. Among these countries, Brazil, considered "world's breadbasket", is located in the intertropical zone, with hot climates and presents lower thermal amplitude. Brazilian poultry is a leader in the production and export of broilers. Brazil has the second largest cattle herd in the world, has the largest commercial herd besides being the largest exporter of beef. Brazilian swine breeding, among the most advanced production chains in the world, occupy the position of fourth largest producer of pork in the world. Projections for Brazil's meat production sector indicate strong growth in the coming years, with countries in hot climates that will continue to sustain future growth in world meat production, ensuring food security in many countries. One of the factors responsible for the success or failure of animal production is the environment, defined by the sum of all physical and biological factors that affect animals. In these regions of hot climates, climatic factors are among the main limiting factors to the development of animal production, which may compromise animal welfare and productivity indices. Behavioral information can aid in the analysis of problems arising from environmental conditions unfavorable to animals, helping both decision making and the use of different environmental conditioning systems. The introduction of technification in the animal behavior evaluation processes, through the use of video cameras and image processing programs, allowed a better interpretation of the behavioral responses, quickly, accurately and non-invasively, gradually being used with greater frequency in the animal production sectors. The study of animal behavior assumes an important role in animal production, since, to rationalize the breeding methods, management, feeding and facilities techniques have been developed that interfere with animal behavior. In this context, the use of digital image analysis techniques, where the animals themselves are used as biosensors in response to environmental conditions, contributes to the analysis of animal behavior, and consequently also to the assessment of the internal environment of the production in hot weather. [ABSTRACT FROM AUTHOR]

The expansion of the productive activities in plateau sections of the Upper Paraguai River Basin (BAP) can potentially impact the abiotic and biotic systems of the Pantanal. The objective of this study is to investigate the dynamics of sugarcane plantation in the Bugres river basin, Mato Grosso state. The methodological procedures followed are based on the generation of data from satellite images and processing in a GIS. In 1990 sugarcane occupied, in the area under study, 137,44 Km2 increasing to 981.11 Km2 with an expansion of 613.84%, preferentially in North-Northeast direction. Sugarcane occupied areas of Sub-montane Semi-Deciduous Seasonal Forests, Alluvial Forests and Livestock farms, not advancing over Urban Areas and Areas of Permanent Protection, the so-called “"APP’'s"”. The declivity is an impeditive factor to increase sugar cane plantations in BAP, wherein it was established in sectors of flat to light undulated relief. As far as soils are concerned, plantations predominate on Oxisols. It was verified that the undulated relief is correlated with the proximal variables: roads, drainage and Indian lands. It was concluded that the good physical soil conditions and flat to light undulated terrain, favored the sugarcane plantation which, under such conditions, presents no restrictions to mechanization. [ABSTRACT FROM AUTHOR]

Significance: Rehabilitation through facial prostheses' main goal is to aid individual's social reintegration as well as improving their quality of life. However, this treatment is not yet widely available in Brazil due to the lack of specialized clinics and the cost associated with the high number of necessary medical appointments until the final result. One of the steps in the process consists of measuring skin color, which is observer-dependent and may suffer from the effect of metamerism. Aim: The methodology of our work aims to obtain a standard between different devices and greater fidelity to the color seen in person in order to reduce face-to-face iterations, reduce costs, and ensure better final results. Approach: A physical device and a computer program were improved from previous projects. The changes included implementing the Thin-Plate Spline 3D algorithm for color calibration, in addition to an optional non-uniform illumination correction in the process. We also aim to improve the project's accessibility using a colorimeter. The methodology and the algorithms were both compared to readings from direct skin measurements as well as color references. Results: After processing, the ΔEab* metric between images from the same segments is taken with different cameras and conditions of illumination decreased from 18.81 ± 4.85 to 4.85 ± 1.72. In addition, when the images were compared to colorimetric readings of the skin, the difference went from 14.93 ± 4.11 to 5.85 ± 1.61. It was also observed that using a less expensive device did not impact the readings. The project is open source and available at Github. Conclusions: The results demonstrate the possibility of applying the methodology to assist in the manufacturing of facial prostheses to decrease the total number of consultations, in addition to providing greater reliability of the final result. [ABSTRACT FROM AUTHOR]

In this study an initial validation of the Landsat 8 (L8) Operational Land Imager (OLI) Surface Reflectance (SR) product was performed. The OLI SR product is derived from the L8 Top-of-Atmosphere product via the Landsat Surface Reflectance Code (LaSRC) software and generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of this study is to develop and evaluate proper validation methodology for the OLI L2 SR product. Validation was performed using near-simultaneous ground truth SR measurements during Landsat 8 overpasses at 13 sites located in the U.S., Brazil, Chile and France. The ground truth measurements consisted of field spectrometer measurements, automated hyperspectral ground measurements operated by the Radiometric Calibration Network (RadCalNet) and derived SR measurements from Airborne Observation Platforms (AOP) operated by the National Ecological Observatory Network (NEON). The 13 sites cover a broad range of 0–0.5 surface reflectance units across the reflective solar spectrum. Results show that the mean reflectance difference between OLI L2 SR products and ground truth measurements for the 13 validation sites and all bands was under 2.5%. The largest uncertainties of 11% and 8% were found in the CA and Blue bands, respectively; whereas, the longer wavelength bands were within 4% or less. Results consistently indicated similarity between the OLI L2 SR product and ground truth data, especially in longer wavelengths over dark and bright targets, while less reliable performance was observed in shorter wavelengths and sparsely vegetated targets. [ABSTRACT FROM AUTHOR]

Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was "weed control + plant seedlings" (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations. [ABSTRACT FROM AUTHOR]

In Brazil the use of drones has become popular in commercial and scientific sectors. In geosciences, however, this tool is yet little applied, being restricted to the initiative of some research institutes. One example is the brazilian Instituto de Pesquisas Tecnológicas of the State of Sao Paulo (IPT) that has been using small multi-rotor UAVs for mapping and monitoring of geological-geomorphological surface processes and for natural disaster and risk management.Drones are an important tool for monitoring the evolution of geological-geomorphological process, allowing quick response, damage assessment and stability analysis, in places with limited access or that pose danger to the authorities. A working routine was created by the Institute, based on versatile tools, with low cost and easy-to-use. Popular commercial low-cost UAVs were applied, as well as free applications and softwares for flight planning and image processing, using mainly cloud services, allowing agility in data processing and in the obtention of its main products (orthomosaics and tridimensional models).We use the DJI UAVs Phantom 3 and DJI Inspire 1, with conventional RGB cameras. For flight planning and execution, we use DJI Go application. As a protocol, we make a preliminary reconnaissance flight on the field above the area of interest to recognize its main characteristics, especially regarding topographic differences and vegetation heights, anticipating possible collisions and distortions on the products obtained. Images are then processed in a cloud web service (Precision Mapper), through which the orthomosaics and digital elevation models are obtained.This routine allows the analysis and monitoring of geological-geomorphological processes, such as landslides, debris-flows and gullies evolution, allowing estimates of the volume of mobilized material, number of endangered houses and risk analysis associated with anthropic activities, such as mining, pipelines and landfills.Up to now the main results obtained through this routine consist in the monitoring of two major landslides and the evolution of a gully, located in the state of São Paulo. Regarding the landslide cases, based on the orthomosaic and the DEMs, we obtained the scar area and perimeter, extension of damages, volume of the mobilized material and its possible evolution. As for the gully monitoring, we obtained the volume of mobilized material and identified the morphologies associated with the process evolution for posterior analysis and monitoring.The use of drones in emergency and geomorphological disaster management has so far been very satisfactory, allowing the support of field teams, agility on data acquisition, damage analysis, evaluation of process evolution and the monitoring of potential instability conditions, allowing more fast and assertive decision making.However, there are limitations and challenges that need to be overcome, such as the presence of dense vegetation, of special importance in the humid tropics, pointing out the need of replacing conventional RGB cameras for sensors that penetrate the vegetation, like LiDAR. Also, the limited availability of free data and the cost of sensors in Brazil are other important limitations to the diffusion of this technology, even though its adoption is inevitable considering the various advantages that it presents. [ABSTRACT FROM AUTHOR]