Drones: Innovative Technology for Use in Precision Pest Management.

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers

Família e vulnerabilidade social: um estudo com octogenários

In order to guide the development of dementia-related public policies for the elderly, it is important to identify factors that vary together with the social vulnerability of this population. This study aimed to identify the relationship between the São Paulo Social Vulnerability Index (IPVS) and various indicators of family support for elderly people over 80 years of age, who presented cognitive alterations (N=49). All ethical guidelines were followed. Data were collected at the homes of the elderly people. A large majority of the respondents lived with family members (88%). In half of the cases, the respondents lived with one (41%) or two (9%) other elderly persons. On average, there was one more non-elderly person living in the high vulnerability family context (M = 3.6, sd = 1.70) than in contexts of very low vulnerability (M = 2.4, sd = 1.07), F(2.43) = 3.364, p < 0.05. However, the functionality of the support provided by these family members needs to be verified, in each of these contexts.Para elaborar políticas públicas para el cuidado de anciano con demencia, es importante verificar factores que varían con la vulnerabilidad social de esa población. El objetivo de este estudio fue identificar la relación entre una medida de vulnerabilidad social (IPVS) y algunos indicadores de apoyo familiar para ancianos, con más de 80 años, con alteraciones cognitivas (N=49). Todas las recomendaciones éticas fueron observadas. Los datos fueron recolectados en los domicilios de los ancianos. La gran mayoría de los entrevistados vivía con la familia (88%). En la mitad de las familias los ancianos vivían con uno (41%) o dos ancianos (9%). En promedio había una persona más, que no era anciana, viviendo en el contexto familiar de alta vulnerabilidad (M=3,6, DE=1,70) que en el contexto de muy baja vulnerabilidad (M=2,4, DE=1,07), F (2, 43)=3,364, pPara direcionar políticas públicas para cuidado ao idoso com demência, é importante verificar fatores que variam com a vulnerabilidade social dessa população. O objetivo foi identificar a relação entre uma medida de vulnerabilidade social (IPVS) e alguns indicadores de apoio familiar para idosos acima de 80 anos, com alterações cognitivas (N=49). Todos os cuidados éticos foram observados. Os dados foram coletados nos domicílios dos idosos. A grande maioria dos entrevistados morava com a família (88%). Em metade das famílias os idosos moravam com mais um (41%) ou dois idosos (9%). Em média havia uma pessoa a mais, não idosa, morando no contexto familiar de alta vulnerabilidade (M=3,6, dp=1,70) do que no contexto de muito baixa vulnerabilidade (M=2,4, dp=1,07), F (2, 43)=3,364,

Plant Growth Promoting Rhizobacteria (PGPR) and Plutella xylostella (L.) (Lepidoptera: Plutellidae) interaction as a resistance inductor factor in Brassica oleracea var. capitata

Resistance of Plutella xylostella populations to chemical insecticides has made its management difficult, and the utilization of resistant cabbage cultivars has been shown to be a useful alternative. The objective of this study was to demonstrate the induction of cabbage plant resistance to P. xylostella using PGPR and injuries caused by the pest larvae as elicitors. Therefore, we evaluated the insects’ responses utilizing a specific bioassay. Furthermore, this assay was used for selecting a PGPR strain that affects the insect’s biology, and to examine molecular and biochemical responses of the plants influenced by the plant-microbe-insect interaction. Among the strains used in this study, Kluyvera ascorbata showed the most relevant results by influencing biological characteristics of the insect. Thus, the following tests demonstrated that the cited strain possesses a high influence on plant metabolism when it undergoes different types of stress such as injuries caused by the pest. These findings were determined from the different responses obtained by the chemical analyses of the tested plants and from the differentiation in the genetic sequences obtained from plants inoculated with or without PGPR that were injured by the pest. The PGPR K. ascorbata alters the metabolism of cabbage plants, which directs a specific plant defense against P. xylostella

Monitoramento de insetos pragas em soja utilizando sensoriamento remoto

Arthropod pests are among the major problems in soybean production, and regular field sampling is required as a basis for decision-making. However, traditional sampling methods are laborious and time-consuming. Therefore, our first goal was to evaluate hyperspectral remote sensing as a tool to establish reflectance patterns from soybean plants infested by various densities of two species of stinkbugs [Euschistus heros and Diceraeus melacanthus (Hemiptera: Pentatomidae)], and two species of caterpillars [Spodoptera eridania and Chrysodeixis includens (Lepidoptera: Noctuidae)]. Bioassays were carried out in greenhouses with potted plants placed in cages with 5 plants infested with 0, 2, 5 and 10 insects. Plants were classified according to their reflectance, based on acquiring spectral data before and after infestation, using a hyperspectral push-broom spectral camera (Resonon Pika L, that works in the region 400-1000 nm). Infestation by stinkbugs did not cause significant differences in the reflectance patterns of infested or non-infested plants. In contrast, caterpillars caused changes in the reflectance patterns, which were classified using a deep-learning approach based on multilayer perceptron artificial neural network. High accuracies (> 70%) were achieved when the models classified low (0+2) or high (5+10) infestation and presence or absence of insects. This study provides an initial assessment to apply a non-invasive detection method to monitor caterpillars in soybean before causing economic damage. Future studies should be carried out under field conditions, using other sensors, such as multispectral cameras to automatize the detection of pest problems in the field. Such digital tools, among others, are shaping the new way to perform agriculture, where decisions are based on data and, therefore, are more precise. Regarding pest management, these new technologies offer growers the possibility of identifying problems at early stages and providing localized solutions. While the traditional Integrated Pest Management (IPM) approach suggests that control solutions should be delivered throughout the whole field, new approaches involving digital technologies will need to consider adaptations in the concepts of economic thresholds, sampling, population forecast, injury identification, and ultimately the localized use of control tactics. Therefore, our second goal was to review how the traditional IPM concepts could be adapted, considering this ongoing digital transformation in agriculture.Ataques de artrópodes pragas estão entre os maiores problemas na produção de soja, sendo necessária uma amostragem regular de campo para a tomada de decisões. No entanto, os métodos de amostragem tradicionais são trabalhosos e demorados. Portanto, nosso primeiro objetivo foi avaliar o sensoriamento remoto hiperespectral como uma ferramenta para estabelecer padrões de reflectância de plantas de soja infestadas por várias densidades de duas espécies de percevejos [Euschistus heros e Diceraeus melacanthus (Hemiptera: Pentatomidae)] e duas espécies de lagartas [Spodoptera eridania e Chrysodeixis includens (Lepidoptera: Noctuidae)]. Os bioensaios foram realizados em casa de vegetação com vasos de plantas colocados em gaiolas com 5 plantas infestadas com 0, 2, 5 e 10 insetos. As plantas foram classificadas de acordo com sua refletância, com base na aquisição de dados espectrais antes e depois da infestação, usando uma câmera hiperespectral de varredura (Resonon Pika L, que atua na região 400-1000 nm). A infestação por percevejos não causou diferenças significativas nos padrões de reflectância de plantas infestadas ou não infestadas. No entanto, as lagartas causaram mudanças nos padrões de reflectância, que foram classificadas usando uma abordagem de Deep Learning baseada em rede neural artificial perceptron multicamada. Altas acurácias (> 70%) foram alcançadas quando os modelos classificaram baixa (0+2) ou alta (5+10) infestação e, presença ou ausência de insetos. Este estudo fornece uma avaliação inicial para aplicar um método de detecção não invasivo para monitorar lagartas na soja antes de causar danos econômicos. Estudos futuros devem ser realizados em condições de campo, utilizando outros sensores, como câmeras multiespectrais para automatizar a detecção de problemas de pragas no campo. Essas ferramentas digitais, entre outras, estão moldando a nova forma de fazer agricultura, onde as decisões são baseadas em dados sendo, portanto, mais precisas. Em relação ao manejo de pragas, essas novas tecnologias oferecem aos produtores a possibilidade de identificar problemas em estágios iniciais e fornecer soluções localizadas. Embora a abordagem tradicional de Manejo Integrado de Pragas (MIP) sugira que as soluções de controle devam ser entregues em todo o campo, novas abordagens envolvendo tecnologias digitais precisarão considerar adaptações nos conceitos de limites econômicos, amostragem, previsão populacional, identificação de lesões e, finalmente, o uso localizado de táticas de controle. Portanto, nosso segundo objetivo foi revisar como os conceitos tradicionais de MIP poderiam ser adaptados, considerando essa transformação digital que está ocorrendo na agricultura

Remote sensing for monitoring whitefly, Bemisia tabaci biotype B (Hemiptera: Aleyrodidae) in soybean

Surtos de pragas em lavouras são imprevisíveis em relação ao local e ao momento. Contudo, um programa manejo integrado de pragas eficiente depende do conhecimento da distribuição dos insetos o mais cedo possível, antes que a população esteja bem estabelecida e atinja o Nível de Dano Econômico. É possível, no entanto, identificar no campo fatores que podem tornar as plantas mais atrativas a esses insetos, como o estresse hídrico. Uma possível maneira de tentar prever os surtos de pragas é pelo diagnóstico da suscetibilidade das plantas às pragas. Nesse sentido, objetivou-se com esse trabalho descrever os padrões de reflectância de plantas de soja estressadas, tanto pela restrição hídrica quanto pela infestação por Bemisia tabaci biótipo B. Para tanto, plantas de soja foram cultivadas em casa de vegetação sob diferentes regimes hídricos (30, 50, 70 e 100% de reposição do volume de água perdido diariamene) e oferecidas a adultos de B. tabaci biótipo B em ensaios com e sem escolha. Todas plantas utilizadas nos bioensaios foram previamente avaliadas quanto à sua reflectância utilizado o sensor hiperespectral FieldSpec&reg; 3, para posterior classificação nos grupos relativos ao regime hídrico imposto. Após os ensaios de suscetibilidade, foram realizados ensaios para determinar a possibilidade de uso do sensor hiperespectral para classificação de plantas estressadas, tanto pelo estresse hídrico, quanto pela infestação por B. tabaci biótipo B, em um esquema fatorial. Os regimes hídricos utilizados foram 70 e 100% de reposição e, ensaios com infestação controlada e não-controlada foram realizados. Nos ensaios de suscetibilidade, foi possível observar que, quando têm escolha, adultos de B. tabaci biótipo B depositam mais ovos em plantas cultivadas com 70 e 50% de reposição do volume de água perdida. Já quando os adultos não têm escolha, diferenças estatísticas entre a quantidade de ovos depositados nos regimes testados não foram encontradas. Quanto à classificação das plantas em grupos, pode-se afirmar que o sensor hiperespectral FieldSpec&reg; 3 fornece informação suficiente para tanto. No ensaio de infestação controlada, quatro grupos foram gerados, 70% de reposição do volume de água com infestação, 70% de reposição sem infestação, 100% de reposição com infestação e 100% de reposição sem infestação. A análise discriminante dos dados de reflectância demonstrou que, ao final do ensaio, os quatro grupos são significativamente diferentes. Ainda, com uma validação cruzada, foi possível classificar os respectivos grupos com 73,81% de precisão. No ensaio com infestação não controlada, as plantas infestadas foram classificadas em 3 grupos de acordo com o nível de infestação, baixa, média e alta. Da mesma maneira, a análise discriminante dos dados de reflectância demonstrou que há diferença entre os grupos e, a validação cruzada indicou que é possível classificar o nível de infestação com 91,98% de precisão. Portanto, conclui-se que o sensoriamento remoto hiperespectral pode somar ao manejo integrado de pragas, tanto na avaliação da suscetibilidade das plantas às pragas, quanto na identificação de plantas infestadas e sadias.Pest outbreaks in commercial fields are unpredictable in relation to location and timing. However, an efficient Integrated Pest Management depends on the knowledge of the distribution of the insects as early as possible, before the population is established and reaches the threshold of Economic Injury. It is possible, however, to identify factors in the field that may make plants more attractive to insects, such as water stress. One possible way to try to predict pest outbreaks is to diagnose the susceptibility of plants to insects. Thus, the objective of this study is to describe the reflectance patterns of soybean plants stressed by either water stress and Bemisia tabaci biotype B infestation. Soybean plants were grown in greenhouse under different irrigation regimes (30, 50, 70 and 100% daily water recharge), and offered to B. tabaci Biotype B adults in both choice and non-choice bioassays. All the plants used in the bioassays were previously evaluated for their reflectance, using the FieldSpec&reg; 3 hyperspectral sensor, to classify them later in the irrigation groups. After the susceptibility bioassays, new studies were carried out to evaluate the feasibility of using the hyperspectral sensor to classify plants under infestation and water stress, in a factorial scheme. Irrigation regimes were 70 and 100% daily water refill, and tests were performed with controlled and uncontrolled infestation. In the susceptibility tests, it was possible to observe that, when given the options, B. tabaci Biotype B adults lay more eggs in plants grown with 70 and 50% daily water refill. When adults have no options, no significant difference was observed between the amount of eggs deposited in all irrigation regimes. Regarding the classification of plants in groups, it is possible to state that the FieldSpec&reg; 3 hyperspectral sensor provides sufficient information for this. In the controlled infestation trial, four distinct groups were generated, 70% water refill + infestation, 100% water refill + infestation, 70% water refill without infestation and 100% water refill without infestation. Discriminant analysis showed that, after the assay, the groups were statistically different. In addition, using a cross-validation, it was possible to classify the groups with 73.81% accuracy. In the test with uncontrolled infestation, three groups were generated, according to the level of infestation: low, medium and high. Likewise, the Discriminant analysis showed that there is a significant difference between the groups, and cross validation indicated that it is possible to classify the level of infestation with 91.98% accuracy. Therefore, it is possible to conclude that hyperspectral remote sensing may be an additive tool for Integrated Pest Management, both to evaluate the susceptibility of plants to pests and to identify healthy and infested plants

Drones: Innovative Technology for Use in Precision Pest Management

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers

Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

Arthropod pests are among the major problems in soybean production and regular field sampling is required as a basis for decision-making for control. However, traditional sampling methods are laborious and time-consuming. Therefore, our goal is to evaluate hyperspectral remote sensing as a tool to establish reflectance patterns from soybean plants infested by various densities of two species of stinkbugs (Euschistus heros and Diceraeus melacanthus (Hemiptera: Pentatomidae)) and two species of caterpillars (Spodoptera eridania and Chrysodeixis includens (Lepidoptera: Noctuidae)). Bioassays were carried out in greenhouses with potted plants placed in cages with 5 plants infested with 0, 2, 5, and 10 insects. Plants were classified according to their reflectance, based on the acquisition of spectral data before and after infestation, using a hyperspectral push-broom spectral camera. Infestation by stinkbugs did not cause significative differences in the reflectance patterns of infested or non-infested plants. In contrast, caterpillars caused changes in the reflectance patterns, which were classified using a deep-learning approach based on a multilayer perceptron artificial neural network. High accuracies were achieved when the models classified low (0 + 2) or high (5 + 10) infestation and presence or absence of insects. This study provides an initial assessment to apply a non-invasive detection method to monitor caterpillars in soybean before causing economic damage

Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

Arthropod pests are among the major problems in soybean production and regular field sampling is required as a basis for decision-making for control. However, traditional sampling methods are laborious and time-consuming. Therefore, our goal is to evaluate hyperspectral remote sensing as a tool to establish reflectance patterns from soybean plants infested by various densities of two species of stinkbugs (Euschistus heros and Diceraeus melacanthus (Hemiptera: Pentatomidae)) and two species of caterpillars (Spodoptera eridania and Chrysodeixis includens (Lepidoptera: Noctuidae)). Bioassays were carried out in greenhouses with potted plants placed in cages with 5 plants infested with 0, 2, 5, and 10 insects. Plants were classified according to their reflectance, based on the acquisition of spectral data before and after infestation, using a hyperspectral push-broom spectral camera. Infestation by stinkbugs did not cause significative differences in the reflectance patterns of infested or non-infested plants. In contrast, caterpillars caused changes in the reflectance patterns, which were classified using a deep-learning approach based on a multilayer perceptron artificial neural network. High accuracies were achieved when the models classified low (0 + 2) or high (5 + 10) infestation and presence or absence of insects. This study provides an initial assessment to apply a non-invasive detection method to monitor caterpillars in soybean before causing economic damage