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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.

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.

Multitemporal collections of satellite images and their products have recently been explored in digital soil mapping. This study aimed to produce a bare soil image (BSI) for the São Paulo State (Brazil) to perform a pedometric analysis for different geographical levels. First, we assessed the potential of the BSI for predicting the surface (0.00-0.20 m) and subsurface (0.80-1.00 m) clay, iron oxides (Fe 2 O 3 ), aluminum (m%) and bases saturation (V%) contents at the state level, which are important properties for soil classification. In this task, legacy soil samples, the BSI and terrain attributes were employed in machine learning. In a second moment, we evaluated the capacity of the BSI for clustering the landscape at the regional level, comparing the predicted patterns with a legacy semi-detailed soil map from a smaller reference site. In the final stage, the predicted soil maps from the state level were investigated at the farm level considering several sites distributed across the São Paulo state. Our results demonstrated that clay and Fe 2 O 3 reached the best prediction performance for both depths at the state level, reaching a RMSE of less than 10 %, RPIQ higher than 1.6 and R 2 of at least 0.41. Additionally, the predicted landscape clusters had a significant association with the main pedological classes, subsurface color, soil mineralogy and texture from the legacy semi-detailed soil map. Illustrative examples at the farm level indicated great capacity of BSI in detecting the variations of soils, which were linked to several soil properties, such as texture, iron content, drainage network, among others. Therefore, this study demonstrates that BSI is valuable information derived from optical Earth Observation data that can contribute to the future of soil survey and mapping in Brazil (PronaSolos).

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.

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, neste estudo, através de regressão múltipla, estimar a produtividade do café arábica, a partir dos atributos do solo. A pesquisa foi desenvolvida em uma área comercial de aproximadamente 1,2 ha, cultivada com Coffea arabica L., variedade Catucaí Amarelo 20/15 – 479. Para a realização das amostragens de solo e da produtividade foi montada uma uma malha de 100 pontos. As informações geográficas dos pontos foram obtidas com auxílio de estação total. Aos dados foram ajustados modelos de regressão linear múltipla para estimar essa variável em função dos atributos de solo. A análise geoestatística foi realizada para os dados reais de produtividade e para a estimada por regressão múltipla. O modelo que melhor se ajustou à distribuição múltipla dos dados foi o linear, sendo capaz de descrever a relação entre a produtividade e os atributos originais de solo. Na análise geoestatística, todas as variáveis apresentaram variogramas com patamares bem definidos. A produtividade estimada por regressão múltipla assemelhou-se à produtividade real, indicando que o modelo ajustado foi capaz de descrever com eficiência a sua variação.

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.

O objetivo deste trabalho foi estimar a produtividade de café com base em nutrientes foliares, utilizando regressão múltipla. O experimento foi conduzido em uma lavoura comercial de Coffea arabica L., variedade catucaí. Para a coleta das amostras de produção e de folha, foi montada uma malha amostral de 100 pontos em toda a lavoura. Dos onze atributos analisados apenas cinco apresentaram significância quanto a suas presenças nos tecidos foliares associados a produtividade ao nível de 5% de probabilidade. Somente esses atributos foram utilizados para compor o modelo linear. Os valores de produtividade apresentaram dependência espacial, com variogramas com patamares bem definidos. Foi possível estimar a produtividade a partir de cinco nutrientes presentes nas folhas. A produtividade estimada pela regressão múltipla minimizou a variabilidade espacial da produtividade. Os valores de produtividade estimada foram bem próximos ao real, o que torna o uso de modelos de regressão múltipla uma técnica aplicável à agricultura de precisão.

Objetivou-se com este trabalho analisar o comportamento temporal do teor foliar de clorofila, da área foliar específica e do índice de sólidos solúveis (medido pelo grau brix) de plantas de café conilon cultivadas em sistemas agroflorestais sombreados com pupunha. As medições de teor foliar de clorofila e área foliar específica foram realizados mensalmente enquanto o grau brix foi medido a partir do início da fase fenológica de maturação. As analises estatísticas foram determinadas pelo teste Tukey a 5% de probabilidade. Regressões múltiplas foram realizadas para analisar o efeito do sistema arborizado sobre qualidade do café. O teor foliar de clorofila e a área foliar específica apresentaram variabilidade temporal, com destaque para a última variável onde a variação foi elevada. O sombreamento não influenciou a maturação dos frutos. À medida que o teor foliar de clorofila e a área foliar específica se elevam, os valores de º Brix também aumentam.

Com esse trabalho se objetivou, através de componentes principais e regressão múltipla, estimar a produtividade de café com base em nutrientes foliares. A pesquisa foi desenvolvida em uma área cultivada com café arábica - Coffea arabica L.. Construiu-se uma grade irregular com 100 pontos amostrais. Para a realização da análise foliar, foram amostradas folhas do terceiro e quarto pares do ramo produtivo. A produção foi avaliada em julho de 2008 e convertida em produtividade. Os valores encontrados foram submetidos a uma análise de componentes principais (PCA). Com a finalidade de encontrar um modelo para a produtividade, foram ajustados modelos de regressão linear múltipla utilizando as componentes principais geradas. A análise geoestatística foi utilizada para quantificar o grau de dependência espacial. Comprovada a dependência, construiu-se mapas temáticos. A estimativa de produtividade a partir de modelos de regressão múltipla utilizando as componentes principais retornou valores semelhantes aos observados para os dados reais. Essa ferramenta é eficiente para estimar a produtividade com base nas componentes geradas à partir dos nutrientes foliares.