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.

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.

Although monitoring insect pest populations in the fields is essential in crop management, it is still a laborious and sometimes ineffective process. Imprecise decision-making in an integrated pest management program may lead to ineffective control in infested areas or the excessive use of insecticides. In addition, high infestation levels may diminish the photosynthetic activity of soybean, reducing their development and yield. Therefore, we proposed that levels of infested soybean areas could be identified and classified in a field using hyperspectral proximal sensing. Thus, the goals of this study were to investigate and discriminate the reflectance characteristics of soybean non-infested and infested with Bemisia tabaci using hyperspectral sensing data. Therefore, cages were placed over soybean plants in a commercial field and artificial whitefly infestations were created. Later, samples of infested and non-infested soybean leaves were collected and transported to the laboratory to obtain the hyperspectral curves. The results allowed us to discriminate the different levels of infestation and to separate healthy from whitefly infested soybean leaves based on their reflectance. In conclusion, these results show that hyperspectral sensing can potentially be used to monitor whitefly populations in soybean fields.

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.