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The use of multispectral images has great potential to assess seed quality and represents a significant technological advance in the search for fast and non-destructive analysis techniques. However, the devices currently available are expensive. Thus, this study aimed to propose a low-cost method for acquisition and processing of multispectral images of soybean seeds and to evaluate their potential for rapid determination of seed physiological potential. The study was conducted in three steps: implementation of the multispectral image acquisition system, development of an algorithm for automatic image processing, and evaluation of the relationship between the data obtained through image analysis and the results of standard tests used to evaluate seed physiological potential. A total of 43 variables were assessed, eight related to seed physiological potential (germination and vigor) and 35 obtained from the analysis of the multispectral images. Of the variables obtained from multispectral images, 21 were related to pixel values in the images in the different bands evaluated (green, red, and infrared) and 14 associated with seed morphometric characteristics. The proposed system is efficient in obtaining multispectral images and the algorithm developed was efficient to extract morphometric characteristics and pixel information from the images. The parameters obtained from the NIR spectrum region showed a good relationship with the physiological potential of soybean seeds. The use of multispectral images has great potential to assess seed quality and represents a significant technological advance in the search for fast and non-destructive analysis techniques. However, the devices currently available are expensive. Thus, this study aimed to propose a low-cost method for acquisition and processing of multispectral images of soybean seeds and to evaluate their potential for rapid determination of seed physiological potential. The study was conducted in three steps: implementation of the multispectral image acquisition system, development of an algorithm for automatic image processing, and evaluation of the relationship between the data obtained through image analysis and the results of standard tests used to evaluate seed physiological potential. A total of 43 variables were assessed, eight related to seed physiological potential (germination and vigor) and 35 obtained from the analysis of the multispectral images. Of the variables obtained from multispectral images, 21 were related to pixel values in the images in the different bands evaluated (green, red, and infrared) and 14 associated with seed morphometric characteristics. The proposed system is efficient in obtaining multispectral images and the algorithm developed was efficient to extract morphometric characteristics and pixel information from the images. The parameters obtained from the NIR spectrum region showed a good relationship with the physiological potential of soybean seeds.

There exist two options for digital cameras that can capture the near-infrared (NIR) band. Conventional red–green–blue (RGB, visible bands) cameras with a single sensor provide NIR band visibility based on the removal of the internal NIR-blocking filter. Alternatively, multisensor cameras exist that have a specific sensor for each band. The modified RGB cameras are of a lower price. In this context, the objective of this study was to compare the performance of a modified RGB camera with that of a multisensor camera for obtaining the normalized difference vegetation index (NDVI) in an area with coffee cultivations. A multispectral camera with five sensors and another camera with only one sensor were used. The NDVI of the coffee field was also measured using the GreenSeeker handheld NDVI sensor manufactured by Trimble. The images were calibrated radiometrically based on the targets in shades of gray made of napa, and the NDVI was calculated after image calibration. The calibration curves showed a high coefficient of determination. The NDVI value obtained with the calibrated images from the cameras showed a significant correlation with the values obtained by the GreenSeeker NDVI sensor, making it possible to obtain the variability pattern of the vegetation index. However, the NDVI obtained using the multisensor camera was closer to the NDVI obtained by the GreenSeeker NDVI sensor.

Delineation of site-specific nutrient management zones (MZ) provides a basis for practical and cost-effective management of spatial soil fertility in precision agriculture. Therefore, the objective...

The increasing market interest in coffee beverage, lead coffee growers around the world to adopt more efficient methods to select the best-quality coffee beans. Currently, coffee beans selection is carried out either manually, which is a costly and unreliable process, or using electronic sorting machines, which are often inefficient because some coffee beans defects, such as sour and immature beans, have similar spectral response patterns. In this sense, the present work aimed to assess coffee beans quality using both computer vision and machine learning techniques, such as Support Vector Machine (SVM), Deep Neural Network (DNN) and Random Forest (RF). For this purpose, an algorithm written in Python language was developed to extract shape and color features from coffee beans images. The obtained dataset was then used as input to the machine learning algorithms. The data reported in this study pointed to the importance of color descriptors for classifying coffee beans defects. Among the variables used, the components from RGB (Red, Green and Blue) and HSV (Hue, Saturation and Value) color spaces presented the most relevant contribution for the classification models. Also, the results reported in this study provides evidence that computer vision along with machine learning algorithms can be used to identify and classify coffee beans with a very high accuracy (> 90%). Key words: Deep neural network; classification; artificial intelligence; image processing; granulometry.

Soil apparent electrical conductivity (ECa) sensors have been used to detect spatial variability because they correlate with soil attributes. Studies with soil attributes have shown that the number of subsamples and sampling points influences mapping. However, there are no studies that investigated the influence of sampling or subsampling density on ECa maps. Therefore, this study verified the influence of ECa readings per sample point on the semivariance and kriging analysis. The data were collected from an area (2.5 ha) of coffee plants. One hundred sampling points were measured considering 20 readings each. 1, 5, 10, 15, and 20 sample point readings were tested. The influence of the number of readings per sampling point on the ECa mapping was determined using linear regression analysis at a significance level of 5%. The results obtained showed that ECa readings per sampling point significantly influence ECa maps. In addition, they demonstrated that reducing the number of readings per sampling point increases prediction errors by kriging. Thus, ECa maps determined with the highest readings per sampling point were mostly accurate.

The growing human population added to the rural exodus has aggravated the pressure in the agricultural sector for greater production. Faced with this problem, research has developed optical sensors for more productive agriculture with the purpose of minimizing the effects of rural exodus, obtaining rapid information and promoting the rational use of natural resources. Optical sensors have a differential consisting of the ability to use the spectral signature of an attribute or part of it to gain information, often not obvious. This review provides recent advances in optical sensors as well as future challenges. The studies have shown the wide range of applicability of optical sensors in agriculture, from detection of weeds to identification of soil fertility, which favors management in different areas of agriculture. The main limitation to the use of optical sensors in agriculture in most parts of the world has been the cost of purchasing the devices, especially in poor countries. So one of the future challenges is the reduction of final prices paid by consumers.

The challenge of recovering degraded soils due to salinity excess leads to the search for more effective strategies that can overcome this problem. Among these, one of the approaches is the use of resistant plant varieties in affected areas. This study aimed to evaluate the influence of different doses of salts on seed germination and seedling formation of two lettuce cultivars (Hanson and H121) and to verify the existence of tolerance among the cultivars. A completely randomized design was used in a 2x5x4 factorial scheme, where the cultivars were evaluated under five distinct salt doses (0, 25, 50, 75 and 100 mol.m-3, conductivities of 0.0, 2.8, 5.4, ​​8.0 and 10.6 dS.m-1, respectively), with four replicates. In this sense, the following variables were evaluated: germination rate (GR), germination speed index (GSI), seedling height (SH), root length (RL) and percentage of dry matter in relation to fresh matter (DM%). As a result, the Hanson cultivar presented better performance than the H121, under all the different salt doses, in all the studied variables. Also, the EC of 2.8 dS.m-1 did not affect any of the studied variables, including both cultivars. However, EC above 2.8 dS.m-1had, significantly, reduced the development of the cultivars. The Hanson cultivar was influenced only in the variables SH, RL and DM%, where and DM% were influenced by EC values above 8.0 and 10.6 dS.m-1, respectively. The cultivar H121 was significantly influenced by all evaluate dvariables, which demonstrates its greater susceptibility to salinity.

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.

This work aimed to determine potential areas for the establishment of cocoa moniliasis in Bahia state, Brazil, by means of fuzzy logic, based on historical datasets of temperature and air relative humidity, available for 519 measurement points distributed throughout the state of Bahia. The data were initially submitted to a descriptive statistical analysis. The spatial variability was determined through geostatistical analysis, followed by interpolation to map the spatial-temporal structure dependence of the phenomenon. Simulations of continuous pixel-to-pixel classification of variables were performed using fuzzy mapping to model the climatic risk of disease establishment. The exponential fuzzy model was applied to temperature data, while the linear model was used for air relative humidity data. The potential areas were defined for each month, using data of temperature and air relative humidity. The fuzzy models used allowed for modeling of the climatic risk of cocoa moniliasis establishment. A large area of the state is at high risk of disease, thus requiring mitigating measures to avoid the pathogen’s introduction and dissemination. RESUMO Com este trabalho objetivou-se definir áreas potenciais de estabelecimento da monilíase do cacaueiro no Estado da Bahia, por meio da lógica fuzzy, com base em séries históricas de temperatura e umidade relativa do ar, disponíveis para 519 pontos de medição distribuídos no Estado da Bahia. Os dados foram submetidos inicialmente a uma análise estatística descritiva. A variabilidade espacial foi determinada através de análise geoestatística, seguida de interpolações para mapear a estrutura da dependência espaço-temporal do fenômeno. Para modelar o risco climático de estabelecimento da doença foram realizadas simulações de classificação contínua, pixel-a-pixel, das representações das variáveis, utilizando o mapeamento fuzzy. Aos dados de temperatura, aplicou-se o modelo fuzzy exponencial, enquanto que aos dados de umidade relativa do ar, o modelo utilizado foi o linear. As definições de áreas potenciais foram realizadas para cada mês, utilizando a informação conjunta das pertinências da temperatura e da umidade relativa do ar. Os modelos fuzzy utilizados permitiram modelar o risco climático de estabelecimento da monilíase no Estado da Bahia. A maior extensão do Estado da Bahia tem riscos elevados para o desenvolvimento da doença, o que exige medidas mitigadoras para evitar a introdução e disseminação do patógeno.

Nitrogen (N) plays a key role for optimal plant growth and, in particular, for profitable crop production. Optimal N management using optical sensors can provide a rapid assessment of a crop N status. Therefore, this study aimed at comparing the efficiency of two chlorophyll meters in assessing the N status in corn plants when using an economical optimum dose of nitrogen under variable rate application. To do so, a field experiment using a randomized block design with 5 treatments and 5 replicates was carried out in Januária - Brazil (15| 28' 55" S, 44° 22' 41'' W; 474 m) in 2018. Treatments consisted of comparing the pre-fixed N fertilization system with three strategies of variable rate N application using different thresholds of the nitrogen sufficiency index (NSI) in the V4, V8, V10, and V14 vegetative stages. As a result, both chlorophyll meters were capable of detecting the need for N application and differentiating between the plants that received or did not receive N application. In addition, readings from both sensors were highly correlated (> 0.92) from the V6 to the V18 stage. Lastly, both chlorophyll meters were able to identify the crop development variability and could be a suitable tool to monitor the nutritional status and for guiding N fertilization in corn crops.

ALGORITMO CLASSIFICADOR BAYESIANO ITERATIVO DE CODIGO ABERTO PARA AVALIACAO DA QUALIDADE DE GRAOS DE CAFE BENEFICIADOS A selecao de graos de cafe desempenha um papel fundamental na qualidade final do produto. Apos o processamento, os graos de cafe sao classificados de acordo com a quantidade de defeitos. Tradicionalmente, essa classificacao e executada manualmente, o que torna o processo trabalhoso e demorado. Este problema pode ser resolvido com tecnicas de processamento digital de imagens, uma vez que os graos defeituosos possuem caracteristicas visuais unicas. Considerando a dificuldade de classificacao manual dos defeitos, este trabalho teve como objetivo elaborar um algoritmo classificador bayesiano para identificar esses defeitos em graos de cafe beneficiados, com base em sua forma e cor. Para tal, foram utilizados 630 graos de cafe arabica, somando oito imagens ao todo. O algoritmo objetivou classificar quatro classes, que foram: graos normais, graos normais quebrados, graos pretos e graos pretos quebrados. Para avaliar a precisao do algoritmo classificador, calculou-se a exatidao global e o coeficiente Kappa, o que permite inferir se o classificador e melhor que uma classificacao aleatoria. Concluiu-se que o algoritmo desenvolvido apresentou uma precisao global de 76% e kappa igual a 0,6. Alem disso, a metodologia proposta mostrou grande potencial para aplicacao na avaliacao da qualidade de outros produtos, cujos parâmetros de forma e espectrais sao relevantes na avaliacao de sua qualidade.Palavras-chave: qualidade de graos de cafe; processamento digital de imagens; Jupyter Notebook; classificacao supervisionada. ABSTRACT: The selection of coffee beans plays a key role in the product's final quality. After processing, coffee beans are classified according to their quantity of defects. Traditionally this classification is performed manually, which makes the process laborious and time-consuming. This problem can be solved with digital image processing techniques since defective grains have unique visual characteristics. Considering the difficulty of manual classification of the defects, this study aimed to elaborate a Bayesian classifier algorithm to identify these defects in benefited coffee beans, based on its shape and color. To do so, 630 grains of arabica coffee were used, composing eight images in total. The algorithm aimed to classify four classes, which were: regular beans, normal broken beans, black beans, and black broken beans. In order to evaluate the accuracy of the classifier algorithm, it was calculated the global accuracy and the Kappa coefficient, which allows inferring if the classifier is better than a random classification. It was concluded that the developed algorithm presented a global accuracy of 76% and kappa equals to 0.6. Also, the proposed methodology showed great potential for application in the quality evaluation of other products, whose shape and spectral parameters are relevant in evaluating its quality.Keywords: coffee beans quality; digital image processing; Jupyter Notebook; supervised classification.

This study evaluated the use of low-cost materials for radiometric calibration of multispectral images. Four materials were tested: plywood panels painted with matte paint (M1); plywood panels cove...

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0–20 cm) in a 2574 km2 agricultural area located in Sao Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (−0.26 > r > −0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.

A crescente demanda por alimentos e por técnicas que viabilizem a exploração agronômica e econômica de áreas com problemas ocasionados pelo excesso de água no solo, são algumas razões que justificam o uso da drenagem. Na drenagem agrícola, os drenos são instalados geralmente sobre a camada impermeável ou acima desta, sendo a profundidade de instalação dos drenos, a porosidade drenável e a condutividade hidráulica importantes parâmetros no cálculo do espaçamento de drenos. Este cálculo pode ser realizado com emprego de diferentes métodos. Entretanto, algumas metodologias aplicam-se melhor para um determinado regime de chuva do que outras. Neste contexto, objetivou-se com este trabalho avaliar diferentes equações utilizadas para se estimar o espaçamento de drenos sob regime de escoamento variável e permanente. Por meio da análise do Desvio Quadrático Médio dos modelos, estabeleceu-se uma comparação referente a precisão de cada modelo. Qualquer um dos modelos para regime não permanente podem ser empregados. Enquanto que para o regime permanente, o pior modelo foi o de Kirkham e os outros modelos apresentaram desvios quadrático médio estatisticamente iguais.

Spatial interpolation methods are frequently used to characterize soil attributes’ spatial variability. However, inconclusive results, about the comparative performance of these methods, have been reported in the literature. Therefore, the present study aimed to analyze the efficiency of ordinary kriging (OK) and inverse distance weighting (IDW) methods in estimating the soil penetration resistance (SPR), soil bulk density (SBD), and soil moisture content (SM) using two distinct sampling grids. The soil sampling was performed on a 5.7 ha area in Southeast Brazil. For data collection, a regular grid with 145 points (20 x 20 m) was created. Soil samples were taken at a 0.20 m layer depth. In order to compare the accuracy of OK and IDW, another grid was created from the initial grid (A), by eliminating one interspersed line, which resulted in a grid with 41 sampled points (40 x 40 m). Results showed that sampling grid A presented less errors than B, proving that the more sampling points, the lower the errors that are associated with both methods will be. Overall, the OK was less biased than IDW only for SBD (A) and SM (B) maps, whereas IDW outperformed OK for the other attributes for both sampling grids.

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.

The tomato root-knot nematode is one of the main phytosanitary problems in crops. Chemical control is the phytosanitary method most used by farmers, and the study of alternative management of phytonematodes is crucial. The objective of this study was to evaluate the effect of silicon dioxide (SiO2) on the initial development of tomato plants, as well as to determine the best dose of SiO2 for inducing resistance to parasitism by M. incognita. This experiment was set up under a completely randomized design with ten treatments and five replicates in a 5x2 factorial arrangement consisting of five concentrations of SiO2 (0, 0.15, 0.3, 0.45 and 0.6 g dm-3 of soil) with the presence and absence of M. incognita, under greenhouse conditions. The following variables were evaluated: plant height; number of leaves; fresh and dry weight of shoot; percentage of shoot dry matter; root fresh weight; number of galls; final population of nematodes; and population per gram of root. The M. incognita infection affected plant height, number of leaves and shoot fresh weight, while the application of SiO2 negatively affected the formation of galls in the roots of the inoculated plants and the population per gram of root, reducing the final population of nematodes in the root system. SiO2 also provided greater development in the tomato plants, with a significant effect on plant height. The ideal dose was 0.34 g dm-3 of SiO2. RESUMEN Los nematodos se encuentran dentro de los principales problemas fitosanitarios del cultivo de tomate. El control químico es el método fitosanitario más utilizado por los productores y la búsqueda de medidas alternativas para su control se hacen indispensable. El objetivo de esta investigación fue evaluar el efecto del dióxido de silicio (SiO2) en el desarrollo inicial de plantas de tomate, así como determinar las mejores dosis en la inducción de resistencia al parásito M. incognita. Se estableció un diseño completamente al azar, con diez tratamientos y cinco repeticiones, en arreglo factorial 5x2, consistiendo en cinco concentraciones de SiO2 (0; 0,15; 0,3; 0,45 y 0,6 g dm-3 de suelo) y la presencia y ausencia de M. incognita, en invernadero. Se evaluaron las variables altura de la planta, número de hojas, masa fresca y seca de la parte aérea, porcentaje de materia seca de la parte aérea, masa fresca de la raíz, número de agallas, población final y por gramo de raíz de nematodos. La infección por M. incognita afectó las variables altura de las plantas, número de hojas y masa fresca de la parte aérea, mientras que la aplicación de SiO2 afectó de forma negativa la formación de ramas en las raíces de las plantas inoculadas y la población por gramo de raíz, reduciendo la población final de nematodos en el sistema radicular de las plantas. La aplicación de SiO2 también proporcionó mayor desarrollo a las plantas de tomate, presentando efecto significativo sobre la altura de las plantas, siendo 0,34 g dm-3 de SiO2 la dosis ideal.

Soil tillage systems alter soil physical attributes and may affect crop growth and yield. In this sense, the objective of this study was to assess the short-term impacts of tillage systems on soil physical properties, spectral response, and yield of the bean crop. The field experiment was laid out in a randomized block design with three tillage systems (NT: No-tillage; MT: Minimum tillage; and CT: Conventional tillage) and six replicates. Data collected included soil physical properties (SPR: Soil penetration resistance, SBD: bulk density, and SWC: water content), crop's spectral response (NDVI: Normalized difference vegetation index) through different multispectral sensors, and lastly, grain yield. Results showed that SBD and SPR values were significantly higher in the NT system at 9 days after planting. Moreover, the SPR in the NT system remained significantly higher (p

The spatiotemporal pattern of particulate matter (PM) concentrations is an important factor in predicting health issues in inhabitants of urban areas. The integration of satellite-derived aerosol optical depth (AOD) data with ground-level PM concentration data, obtained from monitoring networks, has contributed to better characterization of the spatiotemporal variability of aerosols worldwide. However, before using satellite AOD data as a proxy for PM in epidemiological and air quality studies in specific regions, the applicability of that strategy must be evaluated. In this study, we evaluate the use of the high-resolution AOD, derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, as a predictor of surface PM concentrations in the Metropolitan Area of Sao Paulo (MASP). We found relatively weak or negative correlations between PM concentrations and MAIAC AOD, even after vertical correction by planetary boundary layer height and the hygroscopic growth factor. The weak correlations reported in this study are mainly due to the mismatch between the current MAIAC aerosol model and the properties of local aerosols in the MASP. Our results suggest that sources of aerosol particles in the MASP are quite diverse and that there is therefore no single optical model suitable for use with satellite-derived AOD.

O estudo da temperatura é muito importante para o desenvolvimento das culturas, sendo um estudo de grande aplicabilidade no cenário nacional e regional. O Estado da Bahia por apresentar o cultivo de diversas culturas em seu território, com destaque para o cacau, é uma região em que esse tipo de trabalho se torna necessário, uma vez que a temperatura não só está relacionada com o crescimento da lavoura, mas também à propagação e aparecimento de doenças. Com este trabalho, se objetivou estimar a temperatura média anual do Estado da Bahia por meio da geoestatística, a partir de sua relação com a altitude, utilizando o método de interpolação multivariada da cokrigagem. Com este método foi possível obter estimativas mais precisas, em que os resultados podem ser utilizados em diversas tomadas de decisão relacionada às culturas das áreas em questão e também no planejamento ambiental.

Objetivou-se analisar a influência do sombreamento sobre o teor foliar de clorofila, área foliar específica e sobre o índice de sólidos solúveis de plantas de café conilon cultivadas em consorcio com Ingá. As medições de teor foliar de clorofila e área foliar específica foram realizadas mensalmente enquanto o grau brix foi medido semanalmente no início da fase fenológica de maturação. A diferença entre épocas de medição foi determinada pelo teste Tukey a 5% de probabilidade. Regressões múltiplas foram realizadas para analisar o efeito do sistema arborizado sobre as variáveis mensuradas. O teor foliar de clorofila apresentou variabilidade temporal, o que não foi encontrado para a área foliar específica. O sombreamento não influenciou a maturação dos frutos, não havendo diferença estatística entre as diferentes épocas de medição. À medida que o valor da área foliar específica aumenta, o teor de sólidos solúveis também aumenta, desde que o valor do teor foliar de clorofila seja intermediário.

Com esse trabalho se objetivou utilizar a análise de componentes principais para determinar a fertilidade do solo e estimar a produtividade do café arábica, através de regressão múltipla. A pesquisa foi desenvolvida em uma lavoura cultivada com Coffea arabica L., variedade catucaí. Construiu-se uma grade irregular com 100 pontos amostrais. As amostras de solo foram realizadas em cada ponto, para avaliar os macro e micronutrientes. A produção foi avaliada em julho e convertida em valores de produção de café beneficiado por hectare. Os valores encontrados foram submetidos a uma análise de componentes principais (PCA). Às componentes foram ajustados modelos de regressão linear múltipla para estimar a produtividade. A análise geoestatística foi utilizada para quantificar a dependência espacial. Comprovada a dependência, construiu-se mapas temáticos. A PCA foi capaz de reduzir a dimensão da fertilidade do solo. A estimativa de produtividade à partir de modelos de regressão múltipla utilizando PCA minimizou a amplitude de variação do rendimento da cultura do café, sem causar sub ou super-estimativas.

Characterizing the land surface temperature (LST) and its diurnal cycle is important in understanding a range of surface properties, including soil moisture status, evaporative response, vegetation stress and ground heat flux. While remote-sensing platforms present a number of options to retrieve this variable, there are inevitable compromises between the resolvable spatial and temporal resolution. For instance, the spatial resolution of geostationary satellites, which can provide sub-hourly LST, is often too coarse (3 km) for many applications. On the other hand, higher-resolution polar orbiting satellites are generally infrequent in time, with return intervals on the order of weeks, limiting their capacity to capture surface dynamics. With recent developments in the application of unmanned aerial vehicles (UAVs), there is now the opportunity to collect LST measurements on demand and at ultra-high spatial resolution. Here, we detail the collection and analysis of a UAV-based LST dataset, with the purpose of examining the diurnal surface temperature response: something that has not been possible from traditional satellite platforms at these scales. Two separate campaigns were conducted over a bare desert surface in combination with either Rhodes grass or a recently harvested maize field. In both cases, thermal imagery was collected between 0800 and 1700 local solar time. The UAV-based diurnal cycle was consistent with ground-based measurements, with a mean correlation coefficient and root mean square error (RMSE) of 0.99 and 0.68 °C, respectively. LST retrieved over the grass surface presented the best results, with an RMSE of 0.45 °C compared to 0.67 °C for the single desert site and 1.28 °C for the recently harvested maize surface. Even considering the orders of magnitude difference in scale, an exploratory analysis comparing retrievals of the UAV-based diurnal cycle with METEOSAT geostationary data yielded pleasing results (R = 0.98; RMSE = 1.23 °C). Overall, our analysis revealed a diurnal range over the desert and maize surfaces of ~20 °C and ~17 °C respectively, while the grass showed a reduced amplitude of ~12 °C. Considerable heterogeneity was observed over the grass surface at the peak of the diurnal cycle, which was likely indicative of the varying crop water status. To our knowledge, this study presents the first spatially varying analysis of the diurnal LST captured at ultra-high resolution, from any remote platform. Our findings highlight the considerable potential to utilize UAV-based retrievals to enhance investigations across multi-disciplinary studies in agriculture, hydrology and land-atmosphere investigations.

A estimativa da umidade relativa do ar para o Estado da Bahia é de grande importância para a atividade agrícola praticada na região, uma vez que essa variável influência em vários aspectos da cultura e também no bem-estar animal, já que a região possui uma forte atividade agropecuária. Com este trabalho se objetivou estimar a umidade relativa do ar no Estado da Bahia por meio da geoestatística, a partir de sua relação com a altitude, utilizando um método de interpolação multivariada, a cokrigagem. A cokrigagem permite obter estimativas mais precisas quando analisadas pares de variáveis. A utilização da altitude como variável auxiliar possibilitou bom desempenho para a interpolação por cokrigagem, sendo recomendada nesse tipo de estudo. A cokrigagem maximizou a variabilidade espacial da umidade relativa do ar no Estado da Bahia, reduzindo a continuidade do fenômeno principalmente nas regiões montanhosas do estado, na região próxima ao rio São Francisco e na divisa com os Estados de Pernambuco, Alagoas e Sergipe.

Objetivou-se com esse trabalho correlacionar de forma espacial, a incidência média da ferrugem do cafeeiro conilon com a massa de casca estraida dos grãos secos. O experimento foi conduzido em uma área de aproximadamente 2 ha, cultivada comercialmente com plantas de café conilon, para o levantamento das informações de incidência da ferrugem e da massa de casca seca por hectare, foi montado na área um grid irregular totalizando 120 pontos amostrais, os pontos amostrais foram georreferenciados com auxílio de uma estação total. As variáveis em estudo apresentaram dependencia espacial, foram construidos variogramas com patamares bem definidos, e posteriormente foi construido mapas temáticos de distribuição espacial. A média da incidência da ferrugem avaliada ao longo do ano não se correlacionou com a massa de casca produzida pela lavoura. A massa de casca apresentou variação de 0,3 a 2,3 Mg.ha-1, sendo que a maior parte da área teve produção de casca inferior a 1,5 Mg.ha-1, com destaque para a região central da área, que apresentou os maiores valores de massa.