Precision agriculture on grassland: Applications, perspectives and constraints

Schellberg, J.; Hill, Michael J.; Gerhards, Roland; Rothmund, M.; Braun, Matthias

doi:10.1016/j.eja.2008.05.005

Cited by 169 publications

(104 citation statements)

References 117 publications

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“…These results confirm that the spectral data from RS can be used to detect and define management zones. These management zones are areas with different physical-chemical soil properties that influence plant yield and quality (Schellberg et al, 2008;Silva Júnior et al, 2013;Escribano Rodríguez et al, 2014). Moreover, the results indicated that differences could be clearly and consistently distinguished, even with a mix of crops (corn and pasture).…”

Section: Resultsmentioning

confidence: 99%

“…Satellite-derived vegetation indexes have been widely used to estimate crop and grassland biomass; RS provides temporal and spatial patterns of ecosystem change and has been used to estimate the biophysical characteristics of crops and grasslands (Moges et al, 2004;Numata et al, 2007;Schellberg et al, 2008;Silva Júnior et al, 2013;Bernardi et al, 2014). Normalized difference vegetative indexes (NDVI) are commonly used to evaluate plant health, biomass, and nutrient content (Moges et al, 2004;Numata et al, 2007;Escribano Rodríguez et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing (RS) is another PA tool in which acquired images are transformed into vegetation indexes, which may be related to some crop variables (Schellberg et al, 2008;Bernardi et al, 2014;Escribano Rodríguez et al, 2014). Satellite-derived vegetation indexes have been widely used to estimate crop and grassland biomass; RS provides temporal and spatial patterns of ecosystem change and has been used to estimate the biophysical characteristics of crops and grasslands (Moges et al, 2004;Numata et al, 2007;Schellberg et al, 2008;Silva Júnior et al, 2013;Bernardi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Bernardi

Grego

Andrade

et al. 2017

Rev. bras. eng. agríc. ambient.

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A B S T R A C TThe knowledge of soil property spatial variability is useful for determining the rational use of inputs, such as the site-specific application of lime and fertilizer. The objective of this study was to evaluate the vegetation index and spatial variability of physical and chemical soil properties in an integrated crop-livestock system (ICLS). Soil samples were taken from a 6.9 ha area in a regular hexagon grid at 0-0.20 m depths. Soil P, K, Ca, Mg, and cation exchange capacity -CEC; base saturation; clay and sand were analyzed. Soil electrical conductivity (ECa) was measured with a contact sensor. The site was evaluated at the end of the corn season (April) and during forage production (October) using Landsat 5 images, remote sensing techniques and a geographic information system (GIS). Results showed that the normalized difference vegetation index (NDVI) was associated with ECa and soil parameters, indicating crop and pasture variations in the ICLS. Geostatistics and GIS were effective tools for collecting data regarding the spatial variability of soil and crop indicators, identifying variation trends in the data, and assisting data interpretation to determine adequate management strategies.Variabilidade espacial de índices de vegetação e propriedades do solo em sistema de integração lavoura-pecuária R E S U M O O conhecimento da variabilidade espacial dos atributos físicos e químicos do solo é útil para o uso racional de insumos, como na aplicação localizada de corretivos e fertilizantes. Objetivou-se, nesta pesquisa, avaliar a variabilidade espacial dos índices de vegetação e atributos físicos e químicos do solo em um sistema de integração lavoura-pecuário (ILP). Uma área de 6,9 ha foi amostrada com grade regular hexagonal na profundidade de 0-0,20 m. Os valores no solo de P, K, Ca, Mg, e CTC, saturação por bases, argila e areia foram analisados. A condutividade elétrica aparente do solo (CEa) foi medida com um sensor de contato. O talhão foi avaliado no final do ciclo do milho (abril) e para a produção de pastagem (outubro) por imagens do satélite Landsat 5, utilizando-se técnicas de sensoriamento remoto e sistemas de informação geográfica (SIG). Os resultados mostraram que o índice de vegetação por diferença normalizada (NDVI) foi associado à CEa e aos parâmetros de solo indicando as variações da cultura do milho e pastagem em sistema ILP. A geoestatística e o SIG foram ferramentas eficazes para unir os dados de variabilidade espacial do solo e da cultura, indicar as tendências dos resultados e interpretar a informação para apoiar estratégias de manejo adequadas.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Bernardi

Grego

Andrade

et al. 2017

Rev. bras. eng. agríc. ambient.

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“…Forage composition, attributes, biomass and plant species and cultivars can be measured using field-portable instruments at ground level or from planes or satellites (Milton et al, 2009). This has been applied to pastures and rangelands (Kumar et al, 2001;Schellberg et al, 2008). For example, in temperate Australia 50% to 70% of the variance in growth rate of annual pastures could be predicted from satellite imagery (Hill et al, 2004;Donald et al, 2010) and accumulated pasture growth usefully estimated from sequential measurements.…”

Section: Evaluation Of Pasture Attributesmentioning

confidence: 99%

The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics

Boval

Dixon

2012

Animal

163

106

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The global importance of grasslands is indicated by their extent; they comprise some 26% of total land area and 80% of agriculturally productive land. The majority of grasslands are located in tropical developing countries where they are particularly important to the livelihoods of some one billion poor peoples. Grasslands clearly provide the feed base for grazing livestock and thus numerous high-quality foods, but such livestock also provide products such as fertilizer, transport, traction, fibre and leather. In addition, grasslands provide important services and roles including as water catchments, biodiversity reserves, for cultural and recreational needs, and potentially a carbon sink to alleviate greenhouse gas emissions. Inevitably, such functions may conflict with management for production of livestock products. Much of the increasing global demand for meat and milk, particularly from developing countries, will have to be supplied from grassland ecosystems, and this will provide difficult challenges. Increased production of meat and milk generally requires increased intake of metabolizable energy, and thus increased voluntary intake and/or digestibility of diets selected by grazing animals. These will require more widespread and effective application of improved management. Strategies to improve productivity include fertilizer application, grazing management, greater use of crop by-products, legumes and supplements and manipulation of stocking rate and herbage allowance. However, it is often difficult to predict the efficiency and cost-effectiveness of such strategies, particularly in tropical developing country production systems. Evaluation and on-going adjustment of grazing systems require appropriate and reliable assessment criteria, but these are often lacking. A number of emerging technologies may contribute to timely low-cost acquisition of quantitative information to better understand the soil–pasture–animal interactions and animal management in grassland systems. Development of remote imaging of vegetation, global positioning technology, improved diet markers, near IR spectroscopy and modelling provide improved tools for knowledge-based decisions on the productivity constraints of grazing animals. Individual electronic identification of animals offers opportunities for precision management on an individual animal basis for improved productivity. Improved outcomes in the form of livestock products, services and/or other outcomes from grasslands should be possible, but clearly a diversity of solutions are needed for the vast range of environments and social circumstances of global grasslands.

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“…Para tal, os recursos usados nestas funções incluem conjunto de sensores exteroceptivos, visão de máquina (monocular, correspondência estéreo, omnidirecional), sistemas de informação geográfica, acesso remoto aéreo multiespectral, imagens com gradação térmica, variabilidade do ambiente, entre outros (ADRIAN, et al, 2005;CORWIN & PLANT, 2005;HATFIELD et al, 2008;SCHELLBERG et al, 2008;LEE et al, 2010;AHAMED et al, 2011;AQEEL UR et al, 2011;TABILE, 2012).…”

Section: A Multidisciplinaridade Formada Pelas áReas Que Englobam a Tunclassified

Fusão sensorial por classificação cognitiva ponderada no mapeamento de cenas naturais agrícolas para análise quali-quantitativa em citricultura

Lulio¹

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Computer systems are used in Precision Agriculture (PA) to provide relational sampling, accuracy and data processing required for agricultural practices and schemes, which are not common to conventional agriculture, demanding higher costs of production and research directed to the remote sensing for mapping and inspection of crop rows. Tasks are carried out using a priori On-the-Go sensors, and proprioceptive and exteroceptive ones, embedded instrumentation, geographic information and existing implements on production and farming, which these activities ensure during the steps of planting, maturation, maintenance and harvesting of a particular culture. Also, these tasks are aided with the use of terrestrial agricultural mobile robots such as autonomous vehicle platforms for locomotion between the crop rows in the acquisition of field data. Given this information and relating the investment and development of these technologies, the goal of this work is to assist the inspection, quantification and qualification of agricultural crops (citrus) of planting area through analyzing and identifying data for sensor fusion, associated with digital image processing, optical thermal imaging and optical flow sensors and reflectance, based on the extraction of real natural scenes objects, identifying items such as fruits, grasses, stems, branches and leaves, thus providing a qualitative and quantitative set of data analyzed culture. As of a computer vision system and sensor fusion, Gigabit Ethernet cameras, sonars, thermal camera, infrared optical flow sensors and monochrome CMOS sensors are embedded in two sides of the vehicle structure, with the same geometric center ratio of the agricultural mobile robot. After data acquisition, image and signal processing techniques are applied for non homogeneous region segmentation, and pattern recognition through statistical classifiers. Such techniques are programmed in MATLAB and OpenCV, embedded in a computer platform. Cognitive classifiers that perform pattern and classes matching are intended on a combination of weighted data fusion techniques, that during locomotion of the agricultural mobile robot, the steps of image processing and combination of parameters for classification are manipulated for analysis retrospectively, conflicting existing data with likely changes resulting in culture and biomass. Figura 3.6: Na sequência de imagens acima, da esquerda para direita tem-se a imagem visível, imagem NIR e imagem UVFL; na sequência de imagens abaixo, da esquerda para direita temse a imagem do fruto com irregularidade, segmentação usando CART, e segmentação usando LDA (Adaptado de BLASCO et al., 2007b) Figura 3.7: Etapas de segmentação e classificação, considerando o índice de diferença normalizado, o filtro de variância, componentes Cb e Cr, e inserção de rótulo no objeto encontrado, separado do plano de fundo (PAYNE et al., 2013) Figura 3.8: Imagens multimodais registradas de folhas de macieiras, apresentando sete níveis de patógenos que causam irregularidades na superfície, 21 d...

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Precision agriculture on grassland: Applications, perspectives and constraints

Cited by 169 publications

References 117 publications

Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics

Fusão sensorial por classificação cognitiva ponderada no mapeamento de cenas naturais agrícolas para análise quali-quantitativa em citricultura

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