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Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

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Proximal soil sensing technologies, such as visible and near infrared diffuse reflectance spectroscopy (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS), are dry-chemistry techniques that enable rapid and environmentally friendly soil fertility analyses. The application of XRF and LIBS sensors in an individual or combined manner for soil fertility prediction is quite recent, especially in tropical soils. The shared dataset presents spectral data of VNIR, XRF, and LIBS sensors, even as the characterization of key soil fertility attributes (clay, organic matter, cation exchange capacity, pH, base saturation, and exchangeable P, K, Ca, and Mg) of 102 soil samples. The samples were obtained from two Brazilian agricultural areas and have a wide variation of chemical and textural attributes. This is a pioneer dataset of tropical soils, with potential to be reused for comparative studies with other datasets, e.g., comparing the performance of sensors, instrumental conditions, and/or predictive models on different soil types, soil origin, concentration range, and agricultural practices. Moreover, it can also be applied to compose soil spectral libraries that use spectral data collected under similar instrumental conditions.

RESUMO

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

RESUMO

The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.(AU)

Assuntos

RESUMO

The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

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Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s-1, representing a MAPE of 0.7%. The fusion of sensor signals improves prediction accuracy, with higher performance than studies with approaches that used a single sensor. The mass flow approach with multiple sensors is a potential approach to replace conventional yield monitors. The system generates accurate data with high sample density within sugarcane rows.

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Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface ('skin') (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

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The adaptation of the Global Navigation Satellite Systems (GNSS) technology to fit the needs of farmers requires knowledge of the accuracy level delivered by a GNSS receiver in working conditions. To date, no methodology indicates the minimum number of replications to perform a statistical comparison. This study aims to advance knowledge on the methodological approach for evaluating the static and dynamic performance of GNSS receivers commonly used in agricultural operations. For the static test, a supporting frame in the ground carried all the receivers with coordinates properly transported. In the dynamic test, a circular rail with a 9.55 m radius was installed at ground level with a platform driven by an electric motor to carry the receivers at a constant speed. The transversal error of the receiver to the circular reference line was measured. The error with 95 % probability (E95) to receivers without differential correction ranged between 4.22 m and 0.85 m in the static test, and 2.25 m and 0.98 m in the dynamic test. Receivers with differential correction had E95 values below 0.10 m in the static test and 0.16 m in the dynamic test. Receivers with C/A code require five replications at minimum and 13 replications are needed for L1/L2 with differential correction signals in the dynamic test. The static test needs nine replications for C/A and five for L1/L2 with differential correction signals.

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The adaptation of the Global Navigation Satellite Systems (GNSS) technology to fit the needs of farmers requires knowledge of the accuracy level delivered by a GNSS receiver in working conditions. To date, no methodology indicates the minimum number of replications to perform a statistical comparison. This study aims to advance knowledge on the methodological approach for evaluating the static and dynamic performance of GNSS receivers commonly used in agricultural operations. For the static test, a supporting frame in the ground carried all the receivers with coordinates properly transported. In the dynamic test, a circular rail with a 9.55 m radius was installed at ground level with a platform driven by an electric motor to carry the receivers at a constant speed. The transversal error of the receiver to the circular reference line was measured. The error with 95 % probability (E95) to receivers without differential correction ranged between 4.22 m and 0.85 m in the static test, and 2.25 m and 0.98 m in the dynamic test. Receivers with differential correction had E95 values below 0.10 m in the static test and 0.16 m in the dynamic test. Receivers with C/A code require five replications at minimum and 13 replications are needed for L1/L2 with differential correction signals in the dynamic test. The static test needs nine replications for C/A and five for L1/L2 with differential correction signals.(AU)

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Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data.

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The mapping of sugarcane yield is still not as widely available as it is for grain crops. Sugarcane harvesters cut and process the cane in a single or maximum of two rows, facilitating the monitoring of cane yield and its behavior on a small scale. This study tested a method for sugarcane yield data cleaning, investigating if the data recording frequency influences the characterization of yield variations in mapping high-resolution spatial data within a single row. Four data sets from yield monitors of single row harvesting were used. A cleaning process with global and anisotropic filtering in a single sugarcane row was applied. The local outlier cleaner compares the yield value of a point with its nearest neighbors within the same row. Even after the elimination of outliers, there is great variation in yield between the rows, and this variation is much smaller in a single row. A frequency of 2 Hz was required for identifying and characterizing small yield variations within the sugarcane rows whilst other frequencies tried (0.2 and 0.1 Hz) resulted in loss of information on yield variability within the row. The difference between the root mean square error (RMSE) of ordinary kriging (OK) and inverse distance weighting (IDW) techniques is large enough to suggest the use of an individual yield line. Individual yield lines saved information in the data generated by the yield monitor unlike IDW and OK interpolation methods which omitted information over short distances within the rows and compromised the quality of high-resolution maps.

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The mapping of sugarcane yield is still not as widely available as it is for grain crops. Sugarcane harvesters cut and process the cane in a single or maximum of two rows, facilitating the monitoring of cane yield and its behavior on a small scale. This study tested a method for sugarcane yield data cleaning, investigating if the data recording frequency influences the characterization of yield variations in mapping high-resolution spatial data within a single row. Four data sets from yield monitors of single row harvesting were used. A cleaning process with global and anisotropic filtering in a single sugarcane row was applied. The local outlier cleaner compares the yield value of a point with its nearest neighbors within the same row. Even after the elimination of outliers, there is great variation in yield between the rows, and this variation is much smaller in a single row. A frequency of 2 Hz was required for identifying and characterizing small yield variations within the sugarcane rows whilst other frequencies tried (0.2 and 0.1 Hz) resulted in loss of information on yield variability within the row. The difference between the root mean square error (RMSE) of ordinary kriging (OK) and inverse distance weighting (IDW) techniques is large enough to suggest the use of an individual yield line. Individual yield lines saved information in the data generated by the yield monitor unlike IDW and OK interpolation methods which omitted information over short distances within the rows and compromised the quality of high-resolution maps.(AU)

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Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder (<2 mm). Pellets were prepared using soil and a cellulose binder at 10% w w-1 followed by grinding for 20 min. Sample homogeneity was probed by X-ray fluorescence microanalysis. Exchangeable nutrients were assessed by pXRF furnished with a Rh X-ray tube and silicon drift detector. The calibration models were obtained using 58 soil samples and leave-one-out cross-validation. The predictive capabilities of the models were appropriate for both exchangeable K (ex-K) and Ca (ex-Ca) determinations with R2 ≥ 0.76 and RPIQ > 2.5. Although XRF analysis of pressed pellets allowed a slight gain in performance over loose powder samples for the prediction of ex-K and ex-Ca, satisfactory performances were also obtained with loose powders, which require minimal sample preparation. The prediction models with local samples showed promising results and encourage more detailed investigations for the application of pXRF in tropical soils.

RESUMO

Soybean is the main product of Brazilian agribusiness, both production and income. Considering the increase in food and energy demand and the search for more sustainable production systems, this study aimed to analyze inputs and energy use of a possible area of expansion of soybean production: a system under sub irrigation management located in a lowland area of Cerrado biome, northern region of Brazil. Its environmental performance was compared to other Brazilian locations among them traditionally soybean producers. The evaluation and comparison was made through material and energy flow tools in order to determine the inputs embodied per area, as well as energy demand, availability and efficiency in the analyzed production system. Energy demand (IE) and energy availability (OE) of the analyzed production system were 7.6 and 57.1 GJ ha-1, respectively. Energy balance (EB) was 49,5 GJ ha-1, energy return over investment (EROI) was 7.5 and embodied energy in grains (EE) was 2,2 MJ kg-1, respectively. Highest energy consumption was due to the use of fertilizers, fuel and herbicide. The system is energy efficient, since it provides more energy than demands, and efficient when compared to usual production systems in other regions, however it is highly dependent on non-renewable energy.(AU)

A soja é o principal produto do agronegócio Brasileiro, em volume e geração de renda. Considerando o aumento da demanda por alimentos e energia, bem como a busca por sistemas de produção mais sustentáveis, o presente estudo teve como objetivo analisar o uso de energia oriunda de insumos agrícolas em área de possível expansão de produção de soja: sistema de produção sob subirrigação em área de várzea no Cerrado, região Norte do Brasil. Seu desempenho ambiental foi comparado a outros locais no Brasil, entre os quais regiões tradicionalmente produtores de soja. A avaliação e comparação foram feitas por meio do uso de ferramentas de fluxo de materiais e energia, a fim de determinar a quantidade de insumos utilizados por área, bem como a demanda, disponibilidade e eficiência do uso de energia no sistema de produção avaliado. A demanda (IE) e disponibilidade (OE) de energia foram de 7.6 e 57.1 GJ ha-1, respectivamente. O balanço energético (BE), o retorno de energia sobre o investimento (EROI) e a energia incorporada dos grãos (EE) foram 49.5 GJ ha-1, 7.5 e 2.2 MJ kg-1, respectivamente. O maior consumo de energia foi devido à utilização de fertilizantes, herbicidas e combustível. O sistema analisado é eficiente no uso da energia, uma vez que fornece mais energia do que é demandado, e eficiente quando comparado a sistemas de produção usuais em outras regiões, embora seja altamente dependente de energia de origem não-renovável.(AU)

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ABSTRACT: Soybean is the main product of Brazilian agribusiness, both production and income. Considering the increase in food and energy demand and the search for more sustainable production systems, this study aimed to analyze inputs and energy use of a possible area of expansion of soybean production: a system under sub irrigation management located in a lowland area of Cerrado biome, northern region of Brazil. Its environmental performance was compared to other Brazilian locations among them traditionally soybean producers. The evaluation and comparison was made through material and energy flow tools in order to determine the inputs embodied per area, as well as energy demand, availability and efficiency in the analyzed production system. Energy demand (IE) and energy availability (OE) of the analyzed production system were 7.6 and 57.1 GJ ha-1, respectively. Energy balance (EB) was 49,5 GJ ha-1, energy return over investment (EROI) was 7.5 and embodied energy in grains (EE) was 2,2 MJ kg-1, respectively. Highest energy consumption was due to the use of fertilizers, fuel and herbicide. The system is energy efficient, since it provides more energy than demands, and efficient when compared to usual production systems in other regions, however it is highly dependent on non-renewable energy.

RESUMO: A soja é o principal produto do agronegócio Brasileiro, em volume e geração de renda. Considerando o aumento da demanda por alimentos e energia, bem como a busca por sistemas de produção mais sustentáveis, o presente estudo teve como objetivo analisar o uso de energia oriunda de insumos agrícolas em área de possível expansão de produção de soja: sistema de produção sob subirrigação em área de várzea no Cerrado, região Norte do Brasil. Seu desempenho ambiental foi comparado a outros locais no Brasil, entre os quais regiões tradicionalmente produtores de soja. A avaliação e comparação foram feitas por meio do uso de ferramentas de fluxo de materiais e energia, a fim de determinar a quantidade de insumos utilizados por área, bem como a demanda, disponibilidade e eficiência do uso de energia no sistema de produção avaliado. A demanda (IE) e disponibilidade (OE) de energia foram de 7.6 e 57.1 GJ ha-1, respectivamente. O balanço energético (BE), o retorno de energia sobre o investimento (EROI) e a energia incorporada dos grãos (EE) foram 49.5 GJ ha-1, 7.5 e 2.2 MJ kg-1, respectivamente. O maior consumo de energia foi devido à utilização de fertilizantes, herbicidas e combustível. O sistema analisado é eficiente no uso da energia, uma vez que fornece mais energia do que é demandado, e eficiente quando comparado a sistemas de produção usuais em outras regiões, embora seja altamente dependente de energia de origem não-renovável.

RESUMO

Soybean is the main product of Brazilian agribusiness, both production and income. Considering the increase in food and energy demand and the search for more sustainable production systems, this study aimed to analyze inputs and energy use of a possible area of expansion of soybean production: a system under sub irrigation management located in a lowland area of Cerrado biome, northern region of Brazil. Its environmental performance was compared to other Brazilian locations among them traditionally soybean producers. The evaluation and comparison was made through material and energy flow tools in order to determine the inputs embodied per area, as well as energy demand, availability and efficiency in the analyzed production system. Energy demand (IE) and energy availability (OE) of the analyzed production system were 7.6 and 57.1 GJ ha-1, respectively. Energy balance (EB) was 49,5 GJ ha-1, energy return over investment (EROI) was 7.5 and embodied energy in grains (EE) was 2,2 MJ kg-1, respectively. Highest energy consumption was due to the use of fertilizers, fuel and herbicide. The system is energy efficient, since it provides more energy than demands, and efficient when compared to usual production systems in other regions, however it is highly dependent on non-renewable energy.

A soja é o principal produto do agronegócio Brasileiro, em volume e geração de renda. Considerando o aumento da demanda por alimentos e energia, bem como a busca por sistemas de produção mais sustentáveis, o presente estudo teve como objetivo analisar o uso de energia oriunda de insumos agrícolas em área de possível expansão de produção de soja: sistema de produção sob subirrigação em área de várzea no Cerrado, região Norte do Brasil. Seu desempenho ambiental foi comparado a outros locais no Brasil, entre os quais regiões tradicionalmente produtores de soja. A avaliação e comparação foram feitas por meio do uso de ferramentas de fluxo de materiais e energia, a fim de determinar a quantidade de insumos utilizados por área, bem como a demanda, disponibilidade e eficiência do uso de energia no sistema de produção avaliado. A demanda (IE) e disponibilidade (OE) de energia foram de 7.6 e 57.1 GJ ha-1, respectivamente. O balanço energético (BE), o retorno de energia sobre o investimento (EROI) e a energia incorporada dos grãos (EE) foram 49.5 GJ ha-1, 7.5 e 2.2 MJ kg-1, respectivamente. O maior consumo de energia foi devido à utilização de fertilizantes, herbicidas e combustível. O sistema analisado é eficiente no uso da energia, uma vez que fornece mais energia do que é demandado, e eficiente quando comparado a sistemas de produção usuais em outras regiões, embora seja altamente dependente de energia de origem não-renovável.

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The increasing cost of nitrogen fertilizer, combined with high losses, demand management practices that result in high efficiency of nitrogen use by crops, considering the reduction of risks to the environment. Our objectives were to evaluate the index of normalized difference vegetation NDVI the variation of nitrogen and growth regulator and its relationship to foliar N and chlorophyll in cotton crops. The experiment was conducted on Distroferric Latosol (Oxisol), in Dourados, MS. We adopted a randomized block design in split plot with four replications. The main treatments consisted of doses of growth regulator (0, 0.30 and 0.60 L ha-1), the secondary treatments consisted of five N rates (0, 30, 70, 110 and 150 kg ha-1). The NDVI obtained by an active optical sensor was influenced significantly by the N and the growth regulator application, but on most of the readings the interactions between these two factors were not significant. NDVI values can be used on the diagnostic of N nutritional deficiencies for cotton.(AU)

O custo crescente dos fertilizantes nitrogenados, aliado às elevadas perdas, aumenta a necessidade de práticas de manejo que resultem em alta eficiência de utilização do nitrogênio pelas culturas, considerando a redução de riscos ao ambiente. Nessa premissa, objetivou-se avaliar a resposta do índice de vegetação da diferença normalizada (NDVI) à variação de doses de nitrogênio e regulador de crescimento e sua relação com os teores foliares de N e de clorofila na cultura do algodoeiro. O experimento foi conduzido em um Latossolo Vermelho distroférrico, em Dourados, MS. Adotou-se um delineamento em blocos aleatorizados, no esquema de parcelas subdivididas, com 4 repetições. Os tratamentos principais consistiram de doses de regulador de crescimento (0, 0,30 e 0,60 L ha-1); os tratamentos secundários consistiram de 5 doses de N (0, 30, 70, 110 e 150 kg ha-1). O NDVI obtido através de sensor óptico ativo foi influenciado significativamente, tanto pelas doses de N quanto pela aplicação do regulador de crescimento, porém, na maior parte das leituras não houve interação significativa entre estes dois fatores. Os valores do NDVI podem ser usados para diagnosticar deficiências nutricionais relativas ao N na cultura. (AU)

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RESUMO

The increasing cost of nitrogen fertilizer, combined with high losses, demand management practices that result in high efficiency of nitrogen use by crops, considering the reduction of risks to the environment. Our objectives were to evaluate the index of normalized difference vegetation NDVI the variation of nitrogen and growth regulator and its relationship to foliar N and chlorophyll in cotton crops. The experiment was conducted on Distroferric Latosol (Oxisol), in Dourados, MS. We adopted a randomized block design in split plot with four replications. The main treatments consisted of doses of growth regulator (0, 0.30 and 0.60 L ha-1), the secondary treatments consisted of five N rates (0, 30, 70, 110 and 150 kg ha-1). The NDVI obtained by an active optical sensor was influenced significantly by the N and the growth regulator application, but on most of the readings the interactions between these two factors were not significant. NDVI values can be used on the diagnostic of N nutritional deficiencies for cotton.

O custo crescente dos fertilizantes nitrogenados, aliado às elevadas perdas, aumenta a necessidade de práticas de manejo que resultem em alta eficiência de utilização do nitrogênio pelas culturas, considerando a redução de riscos ao ambiente. Nessa premissa, objetivou-se avaliar a resposta do índice de vegetação da diferença normalizada (NDVI) à variação de doses de nitrogênio e regulador de crescimento e sua relação com os teores foliares de N e de clorofila na cultura do algodoeiro. O experimento foi conduzido em um Latossolo Vermelho distroférrico, em Dourados, MS. Adotou-se um delineamento em blocos aleatorizados, no esquema de parcelas subdivididas, com 4 repetições. Os tratamentos principais consistiram de doses de regulador de crescimento (0, 0,30 e 0,60 L ha-1); os tratamentos secundários consistiram de 5 doses de N (0, 30, 70, 110 e 150 kg ha-1). O NDVI obtido através de sensor óptico ativo foi influenciado significativamente, tanto pelas doses de N quanto pela aplicação do regulador de crescimento, porém, na maior parte das leituras não houve interação significativa entre estes dois fatores. Os valores do NDVI podem ser usados para diagnosticar deficiências nutricionais relativas ao N na cultura.

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Os distribuidores centrífugos predominam na aplicação de produtos sólidos na agricultura, por apresentarem grande capacidade de campo operacional e pela grande amplitude de dosagens que permitem aplicar. Ensaios para a caracterização do seu desempenho são realizados sem qualquer impedimento físico (como a presença de plantas), durante o trajeto parabólico de queda das partículas dofertilizante até o solo. O objetivo do presente trabalho foi avaliar comparativamente a distribuição transversal de adubos sólidos aplicados em cobertura nas culturas de milho, soja e algodão. Foram utilizados distribuidores de adubos e corretivos do tipo centrífugo. As avaliações foram desenvolvidas de acordo com a Norma ASAE S341.3/99. Os ensaios de distribuição transversal foram constituídos em alinhar lado a lado, no campo, de forma transversal, coletores nas entrelinhas das culturas instaladas, possibilitando a pesagem do material depositado e posterior avaliação dos resultados. Pode-se concluir que a distribuição transversal de fertilizantes sólidos aplicados em cobertura e a lanço em culturas já instaladas de milho e algodão é afetada pela altura das plantas, ou seja, pelo estádio fenológico em que a cultura se encontra, interferindo diretamente na largura efetiva de aplicação. Já a distribuição ransversal de fertilizantes sólidos aplicados em cobertura na cultura da soja não foi afetada pelas plantas. Assim, recomenda-se que a avaliação da largura efetiva das faixas de aplicação a lanço de fertilizantes sólidos em cobertura nas culturas de milho e algodão seja realizada no interior dessas culturas.

Centrifugal spreaders dominate the application of solid materials in agriculture offering expressive operational field capacity and extended range of applied rates. Field tests for characterization of theirperformance are conducted without any physical obstacles (such as the presence of plants) during the parabolic trajectory of the falling particles of fertilizer to the soil. The purpose of this study was to comparatively evaluate the transverse distribution of solid fertilizers applied on cropped corn, soybeans and cotton. Evaluations of the spreaders were designed according to ASAE S341.3/99 Standard. Tests consisted in aligning side by side collectors in-between the cropped rows andweighting the material deposited. The results showed that transverse distribution of solid fertilizers applied over the cotton and corn crops is affected by the crop height, interfering directly on the effective width of the spreader application, which was not observedin the soybean crop, once the fertilizer application is done when the crop was still below the collector's height. The results suggest that evaluation of effective width of the spreaders application need to be done under real crop environment.

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