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This study aimed to detect soft rot caused by Pectobacterium carotovorum subsp. carotovorum in lettuce using images obtained by multispectral sensors mounted on an unmanned aerial vehicle (UAV). A ...

Problemas fitossanitários, como doenças, pragas e plantas daninhas, que competem com as plantas cultivadas por recursos e, consequentemente, prejudicam seu pleno desenvolvimento, são responsáveis por perdas significativas no setor agrícola, ano após ano. Portanto, conhecer o status sanitário da lavoura é um fator crucial no planejamento de ações de manejo, além de embasar políticas públicas de investimento e de proteção, a serem adotadas, com o objetivo de prevenir perdas produtivas e de assegurar a segurança alimentar da nação. Quando expostas a esses problemas, as plantas ativam respostas de defesa, cujos mecanismos moleculares são muito complexos. Nos estágios iniciais de um ataque de pragas ou de uma doença, embora os sintomas ainda não sejam visíveis no dossel, as plantas reagem com diferentes mecanismos fisiológicos, tais como a redução da taxa de fotossíntese, que induz a um aumento da fluorescência e da emissão de calor. Dessa forma, plantas estressadas produzem uma assinatura espectral que difere da assinatura de uma planta saudável. Dentro do Sensoriamento Remoto, inúmeras pesquisas têm estudado a relação entre diferentes problemas fitossanitários com a resposta espectral registrada em sensores multi ou hiperespectrais (sejam imageadores ou não imageadores, ativos ou passivos e embarcados em plataformas terrestres, aéreas ou orbitais). Nesse contexto, o objetivo desta revisão é descrever o estado da arte da tecnologia de Sensoriamento Remoto aplicada a aspectos fitossanitários de lavouras, nos níveis de coleta de dados terrestre, aéreo e orbital, envolvendo diferentes modalidades de sensores.

The technologies such as the Laser Scanner stand out within the area of architectural registry. Laser Scanner represents a precise technology, which enables the creation of three-dimensional models through the acquisition of milions of points. On the other hand, photogrammetric techniques are also used for similar purposes, making use of images in order to generate three-dimensional representation. Both methodologies aim to obtain reliable representations of reality. However, there are potential discrepancies regarding the accuracy and costs of generating these models. The objective of this letter is to compare the models generated by terrestrial Photogrammetry and Laser Scanner, in order to evaluate the feasibility and efficiency of conventional cameras applied to three-dimensional models for facade restoration purposes. For that, the surveys by terrestrial Photogrammetry and Laser Scanner were used on the facades of a century-old church in Monte Carmelo, MG. From the products, it was observed that the distance between the surfaces generated by the clouds of points from the photogrammetric process and Laser was less than 5 cm.

The formation of seedlings is one of the most important phases of lettuce cultivation. Therefore, any strategy that aims to obtain high-quality seedlings can increase productivity. One of these strategies is the prediction of morphophysiological attributes based on optical properties. The objective of this study was to quantitatively estimate the biometric variables of lettuce from parametric and non-parametric models based on the response of multispectral camera images. The experiment was conducted in a greenhouse in the municipality of Uberaba, Minas Gerais State, Brazil. Twenty days after sowing, multispectral images of the plants were captured using a MAPIR Survey 3 camera. To compose the estimation models, along with the original bands of the camera, the multispectral vegetation indices were calculated using the calibrated original camera bands. Bands B550, B660, and B850 and the near-infrared indices contributed significantly to estimating the physiological variable models, with B850 contributing the most to the biometric and nutritional variables. From the near-infrared band (B850) and derived indices, it was possible to estimate all the agronomic variables from the models generated by the M5 algorithm, with an accuracy of up to 1.6% for the maximum quantum yield. Thus, it is possible to quantify the biometric, physiological, and nutritional variables of lettuce using a multispectral camera. Among the Mapir camera bands, B660 exhibited the greatest variability, showing that the red range was the most sensitive. RESUMO A formação de mudas é umas das fases mais importantes da cultura da alface. Desta forma, qualquer estratégia que vise obtenção de mudas de alta qualidade pode representar aumento de produtividade. Uma dessas formas tem sido a estimativa de atributos morfofisiológicos baseados nas propriedades ópticas. Com este estudo, o objetivo foi estimar quantitativamente variáveis biométricas da alface a partir de modelos paramétricos e não paramétricos baseados na resposta de imagens multiespectrais tomadas por câmara multiespectral. O experimento foi conduzido em casa de vegetação, no município de Uberaba, MG, Brasil. Aos 20 dias após a semeadura foram capturadas imagens multiespectrais das plantas, utilizando-se para isto a câmara Survey 3 da MAPIR. Para compor os modelos de estimativa, juntamente com as bandas originais da câmara, foram calculados os índices de vegetação multiespectrais, a partir das bandas originais calibradas da câmara. As bandas B550, B660, and B850 e os índices derivados do infravermelho próximo contribuiram ao máximo para estimar os modelos das variáveis fisiológicas. Já a banda B850 foi a que mais contribuiu para as variáveis biométricas e nutricionais. A partir da banda do infravermelho próximo (B850) e índices derivados, foi possível estimar quase todas as variaveis agronômicas a partir de modelos gerados pelo algoritmo M5, com precisão de até 1,6% para rendimento quântico máximo. Conclui-se que é possível quantificar as variaveis biométricas, fisiológicas e nutricionais da alface por meio de câmara multiespectral. Dentre as bandas de câmeras Mapir, a B660 exibiu a maior variabilidade, mostrando que a faixa do vermelho foi a mais sensível.

Este estudo analisa a evolução urbana de Monte Carmelo (MG), enquanto uma cidade de pequeno porte. A hipótese levantada neste artigo vincula a evolução da paisagem urbana carmelitana à instalação do campus da Universidade Federal de Uberlândia na cidade. O campus serviria como vetor de atração à expansão urbana e, ao mesmo tempo, agente catalisador de processos urbanos em toda a cidade. Selecionou-se Monte Carmelo como estudo de caso, pois essa é uma cidade pequena que sofre com a falta de dados espaciais: situação que impacta negativamente a construção de políticas efetivas de gestão territorial. Para cumprir com o objetivo, nesta pesquisa utilizou-se de imagens orbitais de baixo custo para estudar a dinâmica de evolução urbana carmelitana. Com essa proposta concretizada construiu-se um modelo alternativo para observação de uma cidade pequena, com tecnologia acessível aos gestores, preenchendo a lacuna de acessibilidade tecnológica. Por meio do processamento digital de imagens e de uma análise visual, foi possível quantificar e qualificar a evolução urbana local, entre os anos de 2013 e 2018. Concluiu-se que o adensamento urbano de Monte Carmelo pode ser explicado pela chegada da Universidade Federal de Uberlândia à cidade, especialmente com o início das atividades de ensino em 2011.

The spatial distribution and levels of available plant nutrients (elements) in the soil can limit coffee yield and must be evaluated for effective crop management. Therefore, we analyzed spatial variability in yield and plant nutrients in the saturation extract of a clayey Oxisol cropped with fertigated coffee. The experiment was carried out on 14 hectares of coffee in Monte Carmelo, Minas Gerais, Brazil. Soil samples were collected (0 - 0.2m layer) at 61 regular grid points (spaced 50x50m) and used to determine plant nutrients in the saturation extract. Coffee yield was also determined at these points. Descriptive statistics were calculated for each variable and geostatistics were used to build a spatial variability model representing the physical attributes of the soil. Variographic analysis was performed using semivariograms. These showed that yield and soil chemistry varied throughout the study site. Thus, the maps generated from geostatistics can be useful tools for soil management in fertigated coffee crops.

A mineracao e constituida por processos, atividades e industrias que possuem o objetivo de extrair substâncias minerais a partir de depositos a ceu aberto, minas subterrâneas, entre outros. Atualmente no Brasil, os minerais que se destacam pela extracao sao os metalicos, como o ferro, o manganes e a bauxita e em menor escala o ouro, o cobre e o niobio. As extracoes desses minerais sao a base da industria mineradora brasileira, sendo Minas Gerais o estado onde se encontra a maior parte delas. O calculo do volume do minerio e de grande importância para qualquer mineradora, pois a partir desse calculo e possivel decidir qual sera o melhor emprego dos materiais produzidos. O minerio, apos o processo de extracao, e depositado em pilhas para posterior beneficiamento. O objetivo deste artigo foi mensurar e comparar o calculo do volume das pilhas a partir da integracao entre tecnicas topograficas e fotogrametricas, utilizando dados obtidos por medicoes planialtimetricas coletadas com uma estacao total e por fotogrametria terrestre, onde imagens da pilha foram coletadas por câmera fotografica nao metrica de baixo custo. Em prossecucao realizou-se o calculo do erro relativo percentual, resultando em um erro de 0,184% do levantamento fotogrametrico em relacao ao levantamento topografico.

Remote sensing has been increasingly applied in precision agriculture due to its strong correlation with agricultural variables. The spectral combination of visible bands has become an alternative cameras with absence of near infrared, due to a relative correlation with the vegetation and, consequently, with the agricultural variables. In this way it is possible to identify changes present in a plantation due to pest and pathogen attack, being possible to detect the presence of nematodes in the coffee crop with this tool. In this context low-cost remotely piloted aircraft (RPAS) allow the continuous monitoring of these crops being an increasingly used alternative for the generation of pest/pathogens management maps, assisting in monitoring productivity of the plantations, minimizing losses. In this work, the classification by the maximum likelihood method was used to discriminate areas of the coffee crop infested by nematodes. For this, the classification was applied in a combination of Normalized Difference Vegetation Index and analysis of the accuracy of the mapping was validated from the Kappa index. The confounding matrix presented 20% omission error (for nematode), 28.57% commission (for exposed soil), being the highest values obtained, in addition to the Kappa index of 0.75. O sensoriamento remoto vem sendo aplicado cada vez mais na agricultura de precisão, devido a sua forte correlação com variáveis agrícolas. A combinação espectral de bandas do visível vem se tornando uma alternativa em câmeras com ausência do infravermelho próximo, devido a uma relativa correlação com a vegetação e, consequentemente, com as variáveis agrícolas. Assim, é possível identificar alterações presentes na plantação devido ao ataque de pragas e patógenos, sendo detectável a presença de nematoides na cultura cafeeira com essa ferramenta. Nesse contexto, as Aeronaves Remotamente Pilotadas (ARPs) de baixo custo possibilitam monitoramento contínuo dessas culturas, sendo uma alternativa utilizada para a geração de mapas de manejo de pragas e patógenos, auxiliando no monitoramento da produtividade dos cultivos, minimizando perdas. Neste trabalho, foi utilizada a classificação pelo método de máxima verossimilhança para discriminação de áreas da cultura cafeeira infestada por nematoides. Para tanto, a classificação foi aplicada em combinação de bandas do visível com NDVI e a análise da acurácia do mapeamento foi validada a partir do índice Kappa. A Matriz de confusão apresentou 20% de erro de omissão (para nematoide) e 28,57% de comissão (para solo exposto), sendo estes os maiores valores obtidos, além do índice Kappa de 0,75.

Made available in DSpace on 2020-12-12T02:27:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Universidade Federal de Uberlândia Universidade Estadual Paulista Nematodes are among the most important coffee pathogens, causing significant losses of productivity. The infection of the coffee plant by nematodes can compromise the root system inducing the manifestation of reflex symptoms in its upper part. In addition, nutritional deficiencies may trigger an increase in host predisposition to various other pathogens. Thus, the monitoring of the nutritional levels of plants grown in areas predisposed to the occurrence of nematodes is fundamental. In this study, it was evaluated the potential of empirical models to estimate macro and micronutrient contents in an coffe experimental nematode infested area from a RapidEye multispectral image. For this purpose, laboratory analyzes were performed to determine the contents of macro and micronutrients, as well as the level of nematode infestation, in two experimental plots located in the coffee region of Monte Carmelo (MG). It was verified that the correlation between nutrient content and nematode concentration was higher for the Mg, S, Cu and Mn (correlation coefficients of 0.62, 0.51, 0.71 and 0.75, respectively), while other nutrients had higher correlations with spectral bands or vegetation indices, mainly Ca which had coefficients higher than 0.7 with all indices derived from the spectral bands of red, red edge and near infrared. Empirical models for nutrient estimation were generated from spectral bands and vegetation indices with correlations greater than 0.5. The red edge band, positioned in a spectral region sensitive to variations in vegetation, individually participated in the models to infer the concentrations of the macronutrients Mg and S, besides the micronutrients B, Cu, Fe and Mn, but all calibrated with correlation coefficients below 0,41. The near infrared band was used in the estimation of the N, P and Na contents (R2 equal to 0.25, 0.36 and 0.49, respectively). The NDVI participated in the formulation of the inference model of Ca content and resulted in the highest calibration R2 (0.61), although the validation error was high (13.56%). The choropleth maps of Ca, Mg, Cu, Fe, Mn and Zn spatial distribution had a similar configuration, indicating almost homogeneous and high concentrations of these nutrients in most of the experimental area. The Na and B contents were different in the two plots of the experimental area, while K and S had a more heterogeneous distribution. The maps of N and P reflect well the deficiency of these nutrients in the whole area, mainly in the P content. The empirical models adjusted for the estimation of most of the nutrients were consistent with the condition of excess or deficiency of nutrients in the experimental area. Universidade Federal de Uberlândia – UFU, Rod. LMG 746, km 01, s/n, bloco 1, Campus Monte Carmelo Universidade Estadual Paulista-UNESP, Rua Roberto Simonsen, 305. Bairro: Centro Caixa Postal 468 Universidade Federal de Uberlândia – UFU Instituto de Ciências Agrárias Campus Monte Carmelo, Rod. LMG 746, km 01, s/n, bloco 1, Campus Monte Carmelo Universidade Estadual Paulista-UNESP, Rua Roberto Simonsen, 305. Bairro: Centro Caixa Postal 468

The physical properties of the soil are limiting factors for coffee cultivation and yields. Therefore, we analyzed spatial variability in the physical properties of a clayey Oxisol under coffee cultivation. The experiment was carried out on 14-hectares of a coffee (Coffea arabica) plantation in the city of Monte Carmelo, in the Brazilian state of Minas Gerais. Soil samples were collected from two layers (0 - 0.1 m and 0.1 - 0.2 m) at 61 grid-points spaced at 50 x 50 meters. These samples were saturated to determine total porosity and soil bulk density. Soil resistance readings were also taken from the same grid points and layers using an impact penetrometer. Descriptive statistics were used to evaluate all variables. Additionally, geostatistics were used to model spatial variability within the soil physical properties. Variographic analysis was performed using semivariograms. We found that density, total porosity and soil resistance to penetration varied throughout the study area, which demonstrates that management type can alter soil physical properties and that maps generated by geostatistics can help coffee growers make decisions related to soil management.

Made available in DSpace on 2018-11-26T17:44:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01 Nematodes are a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study was to use biophysical parameters and remote sensing data to discriminate and map healthy, moderately infected, and severely infected coffee plants. An experimental area in southern Minas Gerais State, in which the occurrence of nematodes was certified, was selected, and biophysical and spectral measurements of the leaves were made. Hyperspectral data were also used in a band simulation of the RapidEye sensor to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. These bands, plus a normalized difference vegetation index image, were used for a multispectral classification of healthy and nematode-infected areas. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulation indicated that red, red edge, and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. The multispectral classification defined the spatial distribution of healthy, moderately infected, and severely infected coffee plants, with an overall accuracy of 78% and Kappa coefficient of 0.71. Consideringthe degree of uncertainty and high cost involved in conventional detection of soil parasites, thelevels of accuracy achieved were adequate. Sao Paulo State Univ, Grad Program Cartog Sci, BR-19060900 Presidente Prudente, SP, Brazil Sao Paulo State Univ, Dept Cartog, BR-19060900 Presidente Prudente, SP, Brazil Univ Fed Uberlandia, Inst Agr Sci, BR-38500000 Mt Carmelo, MG, Brazil Sao Paulo State Univ, Grad Program Cartog Sci, BR-19060900 Presidente Prudente, SP, Brazil Sao Paulo State Univ, Dept Cartog, BR-19060900 Presidente Prudente, SP, Brazil

In this paper we evaluated the performance of similar algorithms implemented in different geoinformation systems, in order to verify if there are differences among the results of the image classification processes, according to the platform used. We have selected two geoinformation systems commonly used for this purpose: SAGA GIS and ENVI. We have idealized a scenario where a user without formal education in Remote Sensing techniques carried the classification and interpreted the results. Therefore, we have evaluated the impacts of such analyses, which possibly would lead to mistakes. For this reason, we have given equivalent conditions for all algorithms and the systems selected, i.e., we have used the default parameters, for simulating an operator without formal education in Remote Sensing. In fact, the results showed that the same algorithm implemented in different software, generates different results, which impacts directly on the user’s interpretation. Therefore, it was confirmed that there must be attention while selecting a geoinformation system and an algorithm in order to perform an image classification. Otherwise, there will be inequivalent results among the software, which impacts on the user’s capacity of interpretation.

A Spodoptera frugiperda (Smith) (lagarta-do-cartucho) e a principal praga do milho, com a intensificacao da agricultura, os cultivos sucessivos possibilitam maior infestacao pela praga. Isso levou ao surgimento de populacoes resistentes a inseticidas e culturas transgenicas. Pesquisas de campo para iniciar tratamentos com inseticidas sao demoradas e exaustivas. Pensando em agilidade e qualidade, neste trabalho foi utilizado uma aeronave remotamente pilotada (ARP) equipado com uma câmera RGB e uma câmera MAPPIR 3 para capturar imagens de uma lavoura de milho, com o objetivo de estimar o indice de area foliar (IAF) de um talhao infestado por S. frugiperda. Durante o ciclo da cultura do milho foram realizadas varias avaliacoes: determinacao do indice de area foliar (IAF), severidade do ataque da praga e, voos para aquisicao das imagens. Modelos radiometricos para estimativa do IAF foram obtidos a partir de modelos de regressao linear compostos pelas bandas que melhor correlacionaram com os parâmetros medidos. Os resultados obtidos demonstraram eficiencia e maior precisao na estimativa do IAF para o modelo radiometrico composto pela a banda do infravermelho proximo na câmera MAPPIR 3. Nesta ocasiao, o RMSE calculado foi de 885,0714 cm². Use of images for attack detection of Spodoptera frugiperda in corn under function of loss of foliar area A B S T R A C T Spodoptera frugiperda (Smith) (cartridge-caterpillar) is the main pest of maize, with the intensification of agriculture, the successive crops allow greater infestation by the pest. This has led to the emergence of populations resistant to insecticides and transgenic crops. Field research to begin treatments with insecticides is time consuming and exhaustive. Thus, thinking of agility and quality, in this work was used a remotely piloted aircraft (ARP) equipped with an RGB camera and a MAPPIR 3 camera to capture images of a maize crop with the objective of estimating the leaf area index (LAI) of a field infested by S. frugiperda. The radiometric models for the estimation of LAI were obtained from linear regression models composed by the bands that best correlated with the measured parameters. The obtained results demonstrated efficiency and greater accuracy in the estimation of the LAI for the radiometric model composed by the near infrared band (IVP) in the MAPPIR 3 camera. On this occasion, the RMSE calculated was 885.0714 cm². Keywords: Corn; Cartridge Caterpillar; Remotely Piloted Aircraft.

O cultivo da soja possui grande importância economica para o Brasil, por ser um dos principais geradores de divisas cambiais para o pais. Porem, mesmo sobre as tecnologias existentes, as lavouras ainda sao constantemente acometidas por doencas foliares, tais como a ferrugem asiatica ( Phakopsora pachyrhizi ), das quais e uma das doencas mais destrutivas para a producao de soja, que muitas vezes provoca perda de rendimento significativo e rapidamente dissemina-se de campo para campo atraves de uredosporos dispersos no ar. Para implementar tratamentos de fungicidas em tempo habil a um controle eficaz da doenca, e essencial detectar a infeccao e a severidade da ferrugem da soja. Como forma de identificacao primaria desta infeccao, a presente pesquisa embasou-se na utilizacao do sensoriamento remoto atraves de imagem multiespectral de alta resolucao espacial (RapidEye) para detectar, discriminar e espacializar a possivel ocorrencia de ferrugem na soja em distintos niveis de gravidade. Para isto foi utilizado um indice de cor da lesao e a caracterizacao multiespectral para se detectar o patogeno nas imagens, e os indices de vegetacao para inferir as areas de infestacao. A partir das cartas geradas foi possivel caracterizar espacialmente as areas de ferrugem em estagio intermediario e avancado da infeccao, mesmo considerando-se a inexistencia de pontos de verificacao em campo. Entretanto a metodologia aqui aplicada, nao foi capaz de detectar o patogeno precoce. Identification of soil rust through high space image images A B S T R A C T Soybean cultivation has had great economic importance for Brazil, since it is one of the main generators of foreign exchange for the country. However, despite all existing technologies, crops are still constantly being affected by foliar diseases such as Asian soybean rust ( Phakopsora pachyrhizi ), which is one of the most destructive diseases of soybean, frequently causing significant loss of yield and rapid field-to-field dissemination through urospores dispersed in the air. In order to implement fungicide treatments in a timely manner for effective disease control, it is essential to detect an infection and severity of soybean rust. As a form of primary infection identification, the present research was based on the use of remote sensing recorded by multispectral high spatial resolution image (RapidEye) to detect, discriminate and spatialize a possible occurrence of soybean rust at different levels of severity. For this, a color index and a multispectral characterization were used for pathogen detection in images, and the vegetation indices to be inferred as areas of infestation. From the generated letters, it was possible to characterize spatially as rust areas in intermediate and advanced infection stages, even considering the lack of checkpoints in the field. However, a methodology applied here was not able to detect the early pathogen. Keywords: Glycine max, Phakopsora pachyrhizi, imagens multiespectrais; deteccao de areas infectdas.

O cultivo da cana-de-açúcar no Brasil, embora assistido por técnicas modernas de plantio, é alvo constante de parasitas do sistema radicular. Por registrar seletivamente o fluxo espectral da radiação eletromagnética refletida pela vegetação, o sensoriamento remoto tornou-se uma poderosa ferramenta na detecção das plantas infectadas por patógenos do solo. Com o objetivo de caracterizar espectralmente a cana-de-açúcar sadia e infectada por nematoides e pela larva do besouro Migdolus fryanus, foram tomadas medidas radiométricas in situ e geradas curvas hiperespectrais de plantas sadias e infectadas. Técnicas específicas de análise espectral, como a determinação da posição da borda do vermelho limítrofe (Red Edge Position Determination - REPD) e diferentes índices espectrais foram avaliados para discriminar as três ocorrências. As curvas de reflectância mostraram diferenças em magnitude principalmente nos comprimentos de onda do vermelho e infravermelho próximo e, assim como a determinação do REP e os índices de clorofila b, NDVI, MCARI e TCARI, permitiram distinguir apenas entre plantas sadias e infectadas. As razões espectrais sensíveis aos pigmentos clorofila a e carotenoides, porém, discriminaram as três ocorrências, inclusive plantas infectadas por nematoides e Migdolus fryanus. A melhor discriminação foi obtida com o índice de carotenoides, um pigmento fortemente relacionado com estresse da planta Despite all technological support used in sugarcane cultivation in Brazil, this planting is still subject to the action of root pathogens. Due to its ability to selectively record the spectral flux of electromagnetic radiation reflected by vegetation, remote sensing is a powerful tool in detecting plants infected by pests and parasites. The objective of this work was to characterize the spectral response of healthy sugarcane and its plants infected by nematodes and Migdolus fryanus beetle larva. In situ radiometric measurements were performed and specific spectral analysis techniques, such as Red Edge Position Determination (REPD) and different vegetation indices were evaluated to discriminate these occurrences. The reflectance curves generated showed differences in magnitude mainly at red and near-infrared wavelengths and, as well as REPD, chlorophyll b index, NDVI, MCARI and TCARI, were able to discriminate only healthy and infected plants. The spectral ratios sensitive to chlorophyll a and carotenoids pigments, however, discriminated the three occurrences, including nematode-infected plants and Migdolus fryanus. The best discrimination was obtained by the index of carotenoids, a pigment whose content is related to plant stress.