Resumo
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.
Assuntos
Inteligência Artificial , Indústria do Café , Café , AgriculturaResumo
In recent decades, research on precision irrigation driven by climate change has developed a multitude of strategies, methods and technologies to reduce water consumption in irrigation projects and to adapt to the increasing occurrence of water scarcity, agricultural droughts and competition between agricultural and industrial sectors for the use of water. In this context, the adoption of water-saving and application practices implies a multidisciplinary approach to accurately quantify the water needs of crops under different water availability and management practices. Thus, this review article presented a review of technologies and new trends in the context of precision irrigation, future perspectives and critically analyze notions and means to maintain high levels of land and water productivity, which minimize irrational water consumption at the field level.
Nas últimas décadas pesquisas voltadas à irrigação de precisão, impulsionadas pelas mudanças climáticas, desenvolveram uma infinidade de estratégias, métodos e tecnologias para reduzir o consumo de água em projetos de irrigação, para adaptação à crescente ocorrência de escassez de água, secas agrícolas e competição entre os setores agrícolas e industriais pelo uso da água. Nesta conjuntura, a adoção de práticas de economia e aplicação de água, implica em uma abordagem multidisciplinar para a quantificação precisa das necessidades de água das culturas, sob diversas práticas de disponibilidade e manejo da água. Dessa forma, este artigo de revisão tem como objetivo apresentar uma revisão sobre as tecnologias e novas tendências no contexto da irrigação de precisão, as perspectivas futuras e analisar criticamente noções e meios para manter altos índices de produtividade da terra e da água, que minimizem o consumo de água irracional a nível de campo.
Assuntos
Consumo de Água (Saúde Ambiental) , Uso Eficiente da Água , Irrigação Agrícola/métodosResumo
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.
Assuntos
Agricultura/instrumentação , Confiabilidade dos Dados , Precisão da Medição DimensionalResumo
The concept of production environments, which is widely used to classify the yield potential of soils, and magnetic susceptibility (MS), is emerging as an important tool for mapping ultra-detailed areas. Given this background, this paper aims to evaluate the use of MS as a tool for the identification of areas with different potential the enhancing of sugarcane yield and quality, and the allocation of varieties. An area of 445 ha was sampled at 1 point every 7 ha, and 14 points were determined for stratified sampling following the top of the landscape. Particle size and MS of samples at depths of 0.0-0.2 and 0.2-0.4 m were analyzed. The data on yield and quality of raw material were obtained from a nine crop season database and biometry performed in the 2018/19 crop season. The multivariate analysis of historical results showed the formation of three groups with different yield and quality potential, with a difference of up to 17.28 mg of cane per hectare between the group with the highest and lowest potential, based on soil MS. An analysis of the performance of the varieties involved showed that MS is effective in identifying areas with different potential for sugarcane yield and quality, differentiating by up to 34.5 % the performance of the same variety in different MS classes and by up to 38.5 % the performance of different varieties in similar MS classes. Thus, MS is an effective tool for identifying areas with different potential for sugarcane yield and quality, and can be used for allocating varieties in the field.
Assuntos
Microbiologia do Solo , Características do Solo/economia , Saccharum/crescimento & desenvolvimento , Melhoria de Qualidade/economiaResumo
Few studies have investigated the biometric attributes of citrus orchards under formation that use RGB sensors on board unmanned aerial vehicles (UAV) and the challenges are great. This study aimed to develop and validate a method of using aerial UAV images by automated routines to evaluate the biometric attributes of a crop of 'Tahiti' acid lime under formation. We used a multirotor UAV, programmed to capture images at three different map scales, with a frontal and side overlap of 80 %. Geoprocessing was carried out both with and without ground control points on each scale. An automated routine was developed in an open-source environment, consisting of three processing phases: i) Estimation of the plant biometric attributes, ii) Statistical analysis, and iii) Statistical Report Map (SRM). The use of the developed routine allowed to delimit and estimate the crown projection area with an accuracy of more than 95 % as well as identify and quantify the plants with an accuracy of over 97 %. The use of ground control points during the processing stage does not increase accuracy in estimating the biometric attributes under evaluation. On the other hand, map scale is strongly correlated with the quality of the estimates, especially plant height. The results allowed to define a method for the acquisition and analysis of aerophotogrammetric data using a UAV, which can be used to measure the plant biometric attributes under analysis and the method can be easily adapted to perennial crops.
Assuntos
Fotogrametria/estatística & dados numéricos , Citrus/crescimento & desenvolvimento , Tecnologia de Sensoriamento Remoto/métodos , Tecnologia de Sensoriamento Remoto/estatística & dados numéricos , Confiabilidade dos DadosResumo
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
Mapa , Agricultura/métodos , Análise Espacial , 24444 , Condutividade ElétricaResumo
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 spatial variability of soil organic matter, plant water availability, and soil nitrogen (N) availability are drivers of crop response to mineral N fertilizer. The complex interaction of these factors is responsible for within-field corn and wheat yield variability. The hairy vetch (HV) cover crop is an economic, environmentally friendly, and efficient N source capable of conciliating crop yield and soil health. Nevertheless, the HV effects to mitigate yield gap of management zone (MZ) have not yet been documented. This study evaluated the effects of HV and mineral N fertilization, in single or combined input, on alleviating crop yield gap. The study was carried out in two croplands southern Brazil. The experimental design was a complete randomized block in a split plot having MZ (high, medium, and low yield) and N fertilizer rates. Wheat and corn N uptake and grain yield had a quadratic adjustment with N fertilizer input, but there was a significant MZ effect, where low yield zone (LYZ) was less responsive. Consequently, mineral N fertilization as a single input to mitigate the yield gap in this MZ was not efficient. On the other hand, HV increased corn N uptake and grain yield mainly in LYZ compared to MYZ and HYZ. HV fully mitigated the yield gap between MYZ and HYZ. The combined use of HV and mineral N fertilization rate adjusted according to N legume credit and MZ was an efficient strategy to boost yield, favoring soil health and environmental protection.
Assuntos
Vicia/crescimento & desenvolvimento , Fertilizantes , Nitrogênio , Triticum , 24444 , Zea maysResumo
Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.
Avaliou-se a correlação entre as técnicas de sensoriamento proximal e os resultados laboratoriais de variáveis qualitativas, mais os atributos agronômicos do solo de um vinhedo de 3,0 ha no município de Muitos Capões, região nordeste do estado do Rio Grande do Sul, Brasil, na safra 2017/2018. Objetivou avaliar a substituição das análises laboratoriais convencionais em viticultura por Índices de Vegetação, em situações de indisponibilidade de acesso ao laboratório. Com base em pesquisa bibliográfica, buscaram-se índices vegetativos desenvolvidos ou utilizados para análise de refletância de dossel em videiras e cujas bandas de trabalho estavam dentro do intervalo espectral fornecido pelo equipamento utilizado, obtendo-se um total de 17 candidatos viáveis. Esses índices de vegetação escolhidos foram correlacionados, por meio de Pearson (5%), com atributos agronômicos do solo (condutividade elétrica aparente, argila, pH em H2O, fósforo, potássio, matéria orgânica, alumínio, cálcio, magnésio, CTC efetivo, CTC em pH 7,0, zinco, cobre, enxofre e boro) para profundidades de 0 - 20 cm e 20 - 40 cm, e tecido vegetal (nitrogênio, fósforo, potássio, cálcio, magnésio, enxofre, cobre, zinco, ferro, manganês e boro), além de alguns parâmetros enológicos e fitotécnicos essenciais para a quantificação da produção e qualidade do vinho. Deste cruzamento foram obtidas 139 correlações significativas, resultando 36 coeficientes moderados entre 19 variáveis de parâmetros versus 12 dos índices. Concluímos que nos casos em que o acesso ou disponibilidade de análises laboratoriais é difícil ou impraticável, a utilização de índices de vegetação é possível, desde que os coeficientes de correlação atinjam, pelo menos, a magnitude moderada, servindo como suporte para a tomada de decisão até a falta de estrutura analítica ser remediada.
Assuntos
Vitis/crescimento & desenvolvimento , Produção Agrícola/instrumentação , Produção Agrícola/métodos , Brasil , Qualidade do Solo , Tomada de Decisões , Tecnologia de Sensoriamento Remoto/métodosResumo
This study determined the spatial variability of soil penetration resistance and yield of the soybean crop in lowland areas. The soil resistance to penetration at four different depths (0 to 0.10 m; 0.11 to 0.20 m; 0.21 to 0.30 m and 0.31 to 0.40 m), volumetric humidity of the soil at two depths (0 to 0.20 m and 0.21 to 0.40 m) and soybean yield were determined in an area of 1.13 hectares, using a sample mesh of 10 x 10 m. The corresponding data were subjected to descriptive statistical analysis. Pearsons simple linear correlation analysis (p0.05) was conducted and the spatial dependence was assessed by analyzing the isotropic semivariograms using spherical, exponential, linear, and Gaussian models. The results showed that the soil penetration resistance increased with depth, with restrictive values to root growth between 0.05 and 0.35 m. There was no correlation between yield and soil penetration resistance, and the semivariograms did not show a defined ascending region regarding the soil penetration resistance data. For the conditions under which the experiment was conducted, the sample 10 x 10 m mesh was suitable for assessing the spatial variability of soil resistance to penetration in depths exceeding 0.10 m.(AU)
Este trabalho teve como objetivo identificar a variabilidade espacial da resistência do solo à penetração e na produtividade da cultura da soja, em área de várzea. Foram realizadas determinações de resistência do solo à penetração, em quatro profundidades (0 a 0,10 m; 0,11 a 0,20 m; 0,21 a 0,30 m e 0,31 a 0,40 m); umidade volumétrica do solo, em duas profundidades (0 a 0,20 m e 0,21 a 0,40 m); e produtividade da soja em uma área de 1,13 hectares, utilizando-se malha amostral de 10 x 10 m. Os dados foram submetidos à análise estatística descritiva. Realizou-se análise de correlação linear simples de Pearson (p0,05) e a dependência espacial foi avaliada pela análise de semivariogramas isotrópicos, utilizando os modelos: esférico, exponencial, linear e gaussiano. Os resultados indicaram que, a resistência do solo à penetração aumentou em profundidade, com valores restritivos ao crescimento radicular entre 0,05 e 0,35 m. Não se obteve correlação entre produtividade e resistência do solo à penetração sendo que, para os dados de resistência do solo à penetração, os semivariogramas não apresentaram uma região ascendente definida. Para as condições em que o experimento foi realizado, a malha amostral de 10 x 10 m utilizada foi adequada para avaliar a variabilidade espacial da resistência do solo à penetração em profundidades superiores a 0,10 m.(AU)
Assuntos
Solo , Agricultura/métodos , Glycine maxResumo
The objective of this study is to determine the vegetation indices (IV) as a means of identifying the nutritional status of corn, with respect to the soil nitrogen and potassium, using the aerial images received through an RGB camera loaded on an unmanned aerial vehicle. The images were obtained for an experiment of the nitrogen levels (0, 60, 120 and 180 kg ha-¹) and potassium levels (0, 50, 100 and 150 kg ha-¹), in the random block design, with a factorial scheme of 4 x 4, having three repetitions. Ten leaves were plucked per plot during the flowering phase to assess the total N (NF) and K+ leaf contents. The Pearsons correlation analysis, as well as the analyses of variance and regression between the IV and the concentrations of N and K2O. NF, K+ and the grain yield, responded only to the soil N levels. A significant correlation was observed for the indices of Red Index, Normalized Difference Index and Visible Atmospherically Resistant Index with the NF, which endorses them as favorable in identifying the nutritional standing of corn, with respect to the N level. Not even a single one of the indices evaluated could detect the nutritional ranking of corn in the context of the potassium level.
O estudo teve como objetivo avaliar índices de vegetação (IV) para detecção do status nutricional do milho com relação ao nitrogênio e potássio por meio de imagens aéreas obtidas por câmera RGB embarcada em veículo aéreo não tripulado. As imagens foram adquiridas em ensaio de níveis de nitrogênio (0, 60, 120 e 180 kg ha-¹) e potássio (0, 50, 100 e 150 kg ha-¹), em blocos ao acaso, fatorial 4 x 4, com três repetições. Coletaram-se dez folhas por parcela na fase de florescimento para avaliação do teor foliar de N total (NF) e K+. Efetuou-se análise de correlação de Pearson, análise de variância e de regressão entre os IV e os níveis de N e de K2O. NF, K+ e a produtividade de grãos responderam apenas aos níveis de N no solo. Houve correlação significativa para os índices Excess Red Index, Normalized Difference Index e Visible Atmospherically Resistant Index com o NF, que os credencia como promissores na detecção do status nutricional do milho em relação ao N. Nenhum dos índices avaliados foi capaz de detectar o status nutricional do milho com relação ao potássio.
Assuntos
Nitrogênio/análise , Potássio/análise , Tecnologia de Sensoriamento Remoto , Zea mays/química , Análise de Regressão , Análise de VariânciaResumo
ABSTRACT: The objective of this study is to determine the vegetation indices (IV) as a means of identifying the nutritional status of corn, with respect to the soil nitrogen and potassium, using the aerial images received through an RGB camera loaded on an unmanned aerial vehicle. The images were obtained for an experiment of the nitrogen levels (0, 60, 120 and 180 kg ha-1) and potassium levels (0, 50, 100 and 150 kg ha-1), in the random block design, with a factorial scheme of 4 x 4, having three repetitions. Ten leaves were plucked per plot during the flowering phase to assess the total N (NF) and K+ leaf contents. The Pearson's correlation analysis, as well as the analyses of variance and regression between the IV and the concentrations of N and K2O. NF, K+ and the grain yield, responded only to the soil N levels. A significant correlation was observed for the indices of Red Index, Normalized Difference Index and Visible Atmospherically Resistant Index with the NF, which endorses them as favorable in identifying the nutritional standing of corn, with respect to the N level. Not even a single one of the indices evaluated could detect the nutritional ranking of corn in the context of the potassium level.
RESUMO: O estudo teve como objetivo avaliar índices de vegetação (IV) para detecção do status nutricional do milho com relação ao nitrogênio e potássio por meio de imagens aéreas obtidas por câmera RGB embarcada em veículo aéreo não tripulado. As imagens foram adquiridas em ensaio de níveis de nitrogênio (0, 60, 120 e 180 kg ha-1) e potássio (0, 50, 100 e 150 kg ha-1), em blocos ao acaso, fatorial 4 x 4, com três repetições. Coletaram-se dez folhas por parcela na fase de florescimento para avaliação do teor foliar de N total (NF) e K+. Efetuou-se análise de correlação de Pearson, análise de variância e de regressão entre os IV e os níveis de N e de K2O. NF, K+ e a produtividade de grãos responderam apenas aos níveis de N no solo. Houve correlação significativa para os índices Excess Red Index, Normalized Difference Index e Visible Atmospherically Resistant Index com o NF, que os credencia como promissores na detecção do status nutricional do milho em relação ao N. Nenhum dos índices avaliados foi capaz de detectar o status nutricional do milho com relação ao potássio.
Resumo
The objective of this study is to determine the vegetation indices (IV) as a means of identifying the nutritional status of corn, with respect to the soil nitrogen and potassium, using the aerial images received through an RGB camera loaded on an unmanned aerial vehicle. The images were obtained for an experiment of the nitrogen levels (0, 60, 120 and 180 kg ha-¹) and potassium levels (0, 50, 100 and 150 kg ha-¹), in the random block design, with a factorial scheme of 4 x 4, having three repetitions. Ten leaves were plucked per plot during the flowering phase to assess the total N (NF) and K+ leaf contents. The Pearsons correlation analysis, as well as the analyses of variance and regression between the IV and the concentrations of N and K2O. NF, K+ and the grain yield, responded only to the soil N levels. A significant correlation was observed for the indices of Red Index, Normalized Difference Index and Visible Atmospherically Resistant Index with the NF, which endorses them as favorable in identifying the nutritional standing of corn, with respect to the N level. Not even a single one of the indices evaluated could detect the nutritional ranking of corn in the context of the potassium level.(AU)
O estudo teve como objetivo avaliar índices de vegetação (IV) para detecção do status nutricional do milho com relação ao nitrogênio e potássio por meio de imagens aéreas obtidas por câmera RGB embarcada em veículo aéreo não tripulado. As imagens foram adquiridas em ensaio de níveis de nitrogênio (0, 60, 120 e 180 kg ha-¹) e potássio (0, 50, 100 e 150 kg ha-¹), em blocos ao acaso, fatorial 4 x 4, com três repetições. Coletaram-se dez folhas por parcela na fase de florescimento para avaliação do teor foliar de N total (NF) e K+. Efetuou-se análise de correlação de Pearson, análise de variância e de regressão entre os IV e os níveis de N e de K2O. NF, K+ e a produtividade de grãos responderam apenas aos níveis de N no solo. Houve correlação significativa para os índices Excess Red Index, Normalized Difference Index e Visible Atmospherically Resistant Index com o NF, que os credencia como promissores na detecção do status nutricional do milho em relação ao N. Nenhum dos índices avaliados foi capaz de detectar o status nutricional do milho com relação ao potássio.(AU)
Assuntos
Zea mays/química , Nitrogênio/análise , Potássio/análise , /métodos , Tecnologia de Sensoriamento Remoto , Análise de Variância , Análise de RegressãoResumo
Spectroscopic techniques have great potential to evaluate soil properties. However, there are still questions regarding the applicability of spectroscopy to analyze soil phosphorous (P) availability, especially in tropical soils with low nutrient contents. Therefore, this study evaluated the possibility to estimate P availability in soil and its pools (labile, moderately labile and non-labile) via Vis-NIR spectroscopy based on intra-field calibration. We used soils from two different locations, a plot experiment that received application of phosphate fertilizers (Field-A) and a cultivated field where a grid soil sampling was performed (Field-B). We used the technique of diffuse reflectance in the visible and near-infrared (Vis-NIR) to obtain the spectra of soil samples. Predictive modeling for P availability and labile, moderately labile and non-labile pools of P in soil were obtained via partial least squares (PLS) regression; classification modeling was performed via Soft Independent Modeling of Class Analogy (SIMCA) on three P availability levels in order to overcome the limitation on quantifying P via Vis-NIR spectroscopy. We found that isolating P contents as the only variable (Field-A), Vis-NIR spectroscopy does not allow estimating P pools in the soil. In addition, quantification of P available in the soil via predictive modeling has limitations in tropical soils. On the other hand, estimating P content in soil through classes of availability is a feasible and promising alternative.
Assuntos
Características do Solo , Fertilizantes , Fósforo , Química do Solo , Espectrofotometria InfravermelhoResumo
Spectroscopic techniques have great potential to evaluate soil properties. However, there are still questions regarding the applicability of spectroscopy to analyze soil phosphorous (P) availability, especially in tropical soils with low nutrient contents. Therefore, this study evaluated the possibility to estimate P availability in soil and its pools (labile, moderately labile and non-labile) via Vis-NIR spectroscopy based on intra-field calibration. We used soils from two different locations, a plot experiment that received application of phosphate fertilizers (Field-A) and a cultivated field where a grid soil sampling was performed (Field-B). We used the technique of diffuse reflectance in the visible and near-infrared (Vis-NIR) to obtain the spectra of soil samples. Predictive modeling for P availability and labile, moderately labile and non-labile pools of P in soil were obtained via partial least squares (PLS) regression; classification modeling was performed via Soft Independent Modeling of Class Analogy (SIMCA) on three P availability levels in order to overcome the limitation on quantifying P via Vis-NIR spectroscopy. We found that isolating P contents as the only variable (Field-A), Vis-NIR spectroscopy does not allow estimating P pools in the soil. In addition, quantification of P available in the soil via predictive modeling has limitations in tropical soils. On the other hand, estimating P content in soil through classes of availability is a feasible and promising alternative.(AU)
Assuntos
Fertilizantes , Fósforo , Química do Solo , Características do Solo , Espectrofotometria InfravermelhoResumo
Vis-NIR-SWIR reflectance spectra of leaf samples, collected in the laboratory, allow the calibration of predictive models to quantify their physicochemical attributes in a practical manner and without producing chemical residues. This technique should enable the development of management strategies for intensification of pasture use. However, spectral analysis performed in the laboratory may be affected by the deterioration of plant material during transport from the field to the lab, so storage methods are necessary. This research aimed to evaluate the effects of different storage methods on the spectral response of Mombasa grass leaves. Three methods were evaluated: (i) artificially refrigerated environment, (ii) humid environment, and (iii) without microenvironment control. These methods were tested in five different storage times: 2 hours, 4 hours, 8 hours, 24 hours and 48 hours. The spectral behavior of the leaves still inserted in the plant was used as a quality reference. Results showed notable changes at the earliest storage time for the treatment without microenvironment control. Both methods with microenvironment control stabilized the occurrence of spectral changes over 48 hours of the samples storage, thus both were suggested for this species.(AU)
Espectros de reflectância vis-NIR-SWIR de amostras foliares, coletados em laboratório, permitem a calibração de modelos preditivos para quantificação de seus atributos físico-químicos de maneira prática e sem produção de resíduos químicos. Esta técnica permite o desenvolvimento de estratégias de manejo para a intensificação do uso de pastagens. Contudo, análises espectrais realizadas em laboratório podem ser afetadas pela deterioração do material vegetal durante o transporte do campo ao laboratório, fazendo-se necessário a utilização de métodos de armazenamento. O presente trabalho objetivou avaliar o efeito de diferentes métodos de armazenamento na resposta espectral de folhas de capim Mombaça. Avaliou-se três métodos: (i) ambiente refrigerado artificialmente; (ii) ambiente úmido; e (iii) ao ar livre, sem controle do microambiente; assim como, cinco diferentes tempos de armazenamento: 2 horas, 4 horas, 8 horas, 24 horas e 48 horas. O comportamento espectral das folhas ainda inseridas na planta foi utilizado como referência de qualidade. Os resultados mostraram alterações pronunciadas para o armazenamento ao ar livre já nos primeiros intervalos de tempo. Ambos métodos com controle de microambiente permitiram estabilizar a ocorrência de alterações espectrais ao longo das 48h de armazenamento das amostras, sendo ambos sugeridos para esta espécie.(AU)
Assuntos
Brachiaria/ultraestrutura , Folhas de Planta/ultraestrutura , Pastagens , Análise EspectralResumo
Sugarcane (saccharum spp.) in Brazil is managed on the basis of production environments. These production environments are used for many purposes, such as variety allocation, application of fertilizers and definition of the planting and harvesting periods. A quality classification is essential to ensure high economic returns. However, the classification is carried out by few and, most of the time, non-representative soil samples, showing unreal local conditions of soil spatial variability and resulting in classifications that are imprecise. One of the important tools in the precision agriculture technological package is the apparent electrical conductivity (ECa) sensors that can quickly map soil spatial variability with high-resolution and at low-cost. The aim of the present work was to show that soil ECa maps are able to assist classification of the production environments in sugarcane fields and rapidly and accurately reflect the yield potential. Two sugarcane fields (35 and 100 ha) were mapped with an electromagnetic induction sensor to measure soil ECa and were sampled by a dense sampling grid. The results showed that the ECa technique was able to reflect mainly the spatial variability of the clay content, evidencing regions with different yield potentials, guiding soil sampling to soil classification that is both more secure and more accurate. Furthermore, ECa allowed for more precise classification, where new production environments, different from those previously defined by the traditional sampling methods, were revealed. Thus, sugarcane growers will be able to allocate suitable varieties and fertilize their agricultural fields in a coherent way with higher quality, guaranteeing greater sustainability and economic return on their production.
Assuntos
Condutividade Elétrica , 24444 , Saccharum , Zonas Agrícolas/análiseResumo
Sugarcane (saccharum spp.) in Brazil is managed on the basis of production environments. These production environments are used for many purposes, such as variety allocation, application of fertilizers and definition of the planting and harvesting periods. A quality classification is essential to ensure high economic returns. However, the classification is carried out by few and, most of the time, non-representative soil samples, showing unreal local conditions of soil spatial variability and resulting in classifications that are imprecise. One of the important tools in the precision agriculture technological package is the apparent electrical conductivity (ECa) sensors that can quickly map soil spatial variability with high-resolution and at low-cost. The aim of the present work was to show that soil ECa maps are able to assist classification of the production environments in sugarcane fields and rapidly and accurately reflect the yield potential. Two sugarcane fields (35 and 100 ha) were mapped with an electromagnetic induction sensor to measure soil ECa and were sampled by a dense sampling grid. The results showed that the ECa technique was able to reflect mainly the spatial variability of the clay content, evidencing regions with different yield potentials, guiding soil sampling to soil classification that is both more secure and more accurate. Furthermore, ECa allowed for more precise classification, where new production environments, different from those previously defined by the traditional sampling methods, were revealed. Thus, sugarcane growers will be able to allocate suitable varieties and fertilize their agricultural fields in a coherent way with higher quality, guaranteeing greater sustainability and economic return on their production.(AU)
Assuntos
Saccharum , 24444 , Zonas Agrícolas/análise , Condutividade ElétricaResumo
The use of optical sensors to identify the nutritional needs of agricultural crops has been the subject of several studies using precision agriculture techniques. In this work, we sought to overcome the lack of research evaluating the use of these techniques in the management of nitrogen (N) fertilizer in pastures. We evaluated the methodology of the nitrogen sufficiency index (NSI) in N management at variable rates (VR) using a portable chlorophyll meter. In addition, the use of color vegetation indices generated from a digital camera was evaluated as a low-cost alternative. The work was conducted in four management cycles at different times of year, evaluating the productivity and quality of Brachiaria brizantha cv. Xaraés grass. Three NSIs (0.85, 0.90 and 0.95) were evaluated, applying complementary doses of N according to the response of monitored plots using a chlorophyll meter and comparing the productivity and leaf N content of these treatments to the reference treatment (TREF), which received a single dose of N (150 kg ha-1). Together with these treatments, plots without N application (control) were analyzed, totaling five treatments with six replications in a completely randomized design. The dry mass productivity and N leaf concentration of the VR treatments were statistically equal to TREF in all management cycles (P < 0.05). Most color vegetation indices correlated significantly (P < 0.05) to the chlorophyll readings. The use of NSI methodology in pastures allows the same productivity gains, with significant input savings. In addition, the use of digital cameras presents itself as a viable alternative to monitoring the N status in pastures.
O uso de sensores óticos para identificação das necessidades nutricionais de culturas agrícolas tem sido objeto de diversas pesquisas empregando técnicas de agricultura de precisão. Nesse trabalho buscou-se suprir a carência de pesquisas avaliando o emprego dessas técnicas no manejo de adubo nitrogenado (N) em pastagens. Avaliamos a metodologia do índice de suficiência de nitrogênio (NSI) no manejo de N a taxa variada (VR) com o uso de um medidor de clorofila portátil. Além disso, avaliou-se o uso de uma câmera digital como uma alternativa de baixo custo. O trabalho foi conduzido por quatro ciclos de manejo em diferentes épocas do ano, avaliando a produtividade e qualidade do capim Brachiaria brizantha cv. Xaraés. Foram avaliados três NSIs (0,85; 0,90 e 0,95), aplicando doses complemantares de N de acordo com a resposta da planta monitorada com o medidor de clorofila, comparando a produtividade e teor de N foliar desses tratamentos com o tratamento de referência (TREF), que recebeu uma dose única de N (150 kg ha-1), conforme recomendações tradicionais. Junto com esses tratamentos foram analisadas parcelas sem aplicação de N (controle), compondo assim cinco tratamentos, com seis repetições, em delineamento inteiramente casualizado. A produtividade de massa seca e de N foliar dos tratamentos a VR foi estatisticamente igual a TREF em todos os períodos avaliados (P < 0,05). A maioria dos índices de vegetação aplicados às imagens obtidas com a câmera digital se correlacionaram significativamente (P < 0,05) com as leituras realizadas com o clorofilômetro portátil. O uso da metodologia do NSI em pastagens permite os mesmos ganhos de produtividade, com economias significativas de insumo. E o uso de câmera digital apresenta-se como uma alternativa viável ao monitoramento do status de N em pastagens.
Assuntos
Brachiaria/crescimento & desenvolvimento , Clorofila/análise , Fenômenos Fisiológicos Vegetais , Fertilizantes , Necessidades Nutricionais , Nitrogênio/administração & dosagem , Pastagens/métodosResumo
The use of optical sensors to identify the nutritional needs of agricultural crops has been the subject of several studies using precision agriculture techniques. In this work, we sought to overcome the lack of research evaluating the use of these techniques in the management of nitrogen (N) fertilizer in pastures. We evaluated the methodology of the nitrogen sufficiency index (NSI) in N management at variable rates (VR) using a portable chlorophyll meter. In addition, the use of color vegetation indices generated from a digital camera was evaluated as a low-cost alternative. The work was conducted in four management cycles at different times of year, evaluating the productivity and quality of Brachiaria brizantha cv. Xaraés grass. Three NSIs (0.85, 0.90 and 0.95) were evaluated, applying complementary doses of N according to the response of monitored plots using a chlorophyll meter and comparing the productivity and leaf N content of these treatments to the reference treatment (TREF), which received a single dose of N (150 kg ha-1). Together with these treatments, plots without N application (control) were analyzed, totaling five treatments with six replications in a completely randomized design. The dry mass productivity and N leaf concentration of the VR treatments were statistically equal to TREF in all management cycles (P < 0.05). Most color vegetation indices correlated significantly (P < 0.05) to the chlorophyll readings. The use of NSI methodology in pastures allows the same productivity gains, with significant input savings. In addition, the use of digital cameras presents itself as a viable alternative to monitoring the N status in pastures.(AU)
O uso de sensores óticos para identificação das necessidades nutricionais de culturas agrícolas tem sido objeto de diversas pesquisas empregando técnicas de agricultura de precisão. Nesse trabalho buscou-se suprir a carência de pesquisas avaliando o emprego dessas técnicas no manejo de adubo nitrogenado (N) em pastagens. Avaliamos a metodologia do índice de suficiência de nitrogênio (NSI) no manejo de N a taxa variada (VR) com o uso de um medidor de clorofila portátil. Além disso, avaliou-se o uso de uma câmera digital como uma alternativa de baixo custo. O trabalho foi conduzido por quatro ciclos de manejo em diferentes épocas do ano, avaliando a produtividade e qualidade do capim Brachiaria brizantha cv. Xaraés. Foram avaliados três NSIs (0,85; 0,90 e 0,95), aplicando doses complemantares de N de acordo com a resposta da planta monitorada com o medidor de clorofila, comparando a produtividade e teor de N foliar desses tratamentos com o tratamento de referência (TREF), que recebeu uma dose única de N (150 kg ha-1), conforme recomendações tradicionais. Junto com esses tratamentos foram analisadas parcelas sem aplicação de N (controle), compondo assim cinco tratamentos, com seis repetições, em delineamento inteiramente casualizado. A produtividade de massa seca e de N foliar dos tratamentos a VR foi estatisticamente igual a TREF em todos os períodos avaliados (P < 0,05). A maioria dos índices de vegetação aplicados às imagens obtidas com a câmera digital se correlacionaram significativamente (P < 0,05) com as leituras realizadas com o clorofilômetro portátil. O uso da metodologia do NSI em pastagens permite os mesmos ganhos de produtividade, com economias significativas de insumo. E o uso de câmera digital apresenta-se como uma alternativa viável ao monitoramento do status de N em pastagens.(AU)