ABSTRACT
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.
ABSTRACT
The use of selective insecticides aids farmers in maintaining pest populations below the economic threshold level. The integrated use of biological and chemical control is only possible if the effects of insecticides on natural enemies are studied. Although the IOBC/WPRS standards allow us to compare these studies worldwide, the methods used are sometimes inconsistent. This study determined the effects of ready-mix insecticides applied on pupae of Trichogramma pretiosum (Riley, 1879) (Hymenoptera: Trichogrammatidae) and compared the effects on emergence of two different methods of exposing T. pretiosum pupae to insecticides: immersed or sprayed using a Potter tower. Both methods gave the same results, indicating that they can be compared. Moreover, it is important to go beyond IOBC/WPRS classification and study the effects of pesticides on different biological parameters of natural enemies. This additional step may increase the likelihood of successful integration of biological and chemical control. Based on the emergence reduction, Chlorantraniliprole + lambda-cyhalothrin, abamectin + chlorantraniliprole, and alpha-cypermethrin + teflubenzuron were classified as innocuous (class 1). Cypermethrin + profenofos and cyproconazole + thiamethoxam were classified as slightly harmful (class 2). Methanol + methomyl and lufenuron + profenofos were classified as harmful (class 4). Abamectin + chlorantraniliprole, although classified as innocuous, reduced the parasitism, longevity, and flight capability of the adult parasitoids. None of these insecticides altered the emergence and sex ratio of the second generation.
Subject(s)
Hymenoptera , Insecticides , Wasps , Animals , Insecticides/toxicity , Pest Control, Biological , Pupa , ThiamethoxamABSTRACT
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.
