In natural habitats, there is a strong evolutionary selection pressure on herbivorous insects to avoid danger and choose suitable host plants. Similar selection pressures may drive movement and choices of oviposition hosts by herbivorous insects living in agricultural cropping systems, in which insecticides are often used. In this study, we quantified movement responses and nymph emergence (collectively referred to as bio-responses) of western-tarnished plant bug (Lygus hesperus Knight (Hemiptera: Miridae)) individuals when exposed to environments associated with a perceived “risk” (experimental insecticide treatments and their corresponding controls). We introduce a novel analytical approach in which treatments (risk environments) are ranked in ascending order based on bio-responses (movement or nymph emergence). Consequently, linear regression coefficients were generated and used to interpret bio-responses of Lygus individuals in different life stages to risk environments. Initially, we predicted movement by Lygus individuals to be positively associated with environmental risk and nymph emergence to be negatively associated with environmental risk. Overall, based on a comprehensive combination of no- and two-choice bioassays, we found that: (1) In no-choice bioassays, movement parameters (both total distance moved and movement percentage) by all three life stages were lowest in low-risk environments and highest when Lygus individuals were exposed to either malathion or Grandevo. Accordingly, environments involving malathion or Grandevo were considered high-risk. (2) No-choice movement bioassays also revealed that Lygus males moved significantly more (based on comparison of regression intercepts) than other life stages, and that they responded significantly more (based on comparison of regression slopes) than conspecific females and nymphs. (3) In two-choice movement bioassays, neem elicited the most consistent movement responses by Lygus individuals, and adult life stages showed the strongest response. Two-choice movement bioassays also revealed that Lygus adults, compared to nymphs, were more likely to spend time in low-risk areas of the test arenas. (4) Nymph emergence was markedly lower in no-choice compared to two-choice bioassays, and in two-choice bioassays, Grandevo and malathion elicited especially biased nymph emergence from low-risk beans. To our knowledge, this is the first study in which movement bioassays have been used to quantify and characterize behavioral responses by Lygus life stages to environments associated with varying degrees of risk. The novel analytical approach presented in this study provides a high degree of complementarity to more traditional performance-testing methods used to evaluate responses to insecticides. Furthermore, we believe that this analytical approach can be of considerable relevance to studies of animal phenomics and behavioral studies of animals more broadly, in which adaptation and fitness parameters are examined in response to environmental risk and heterogeneity.

In recent decades, unmanned aerial vehicles (UAVs) have gained considerable popularity in the agricultural sector, in which UAV-based actuation is used to spray pesticides and release biological control agents. A key challenge in such UAV-based actuation is to account for wind speed and UAV flight parameters to maximize precision-delivery of pesticides and biological control agents. This paper describes a data-driven framework to predict density distribution patterns of vermiculite dispensed from a hovering UAV as a function of UAV’s movement state, wind condition, and dispenser setting. The model, derived by our proposed learning algorithm, is able to accurately predict the vermiculite distribution pattern evaluated in terms of both training and test data. Our framework and algorithm can be easily translated to other precision pest management problems with different UAVs and dispensers and for difference pesticides and crops. Moreover, our model, due to its simple analytical form, can be incorporated into the design of a controller that can optimize autonomous UAV delivery of desired amount of predatory mites to multiple target locations.

Many studies provide insight into calibration of airborne remote sensing data but very few specifically address the issue of temporal radiometric repeatability. In this study, we acquired airborne hyperspectral optical sensing data from experimental objects (white Teflon and colored panels) during 52 flight missions on three separate days. Data sets were subjected to four radiometric calibration methods: no radiometric calibration (radiance data), empirical line method calibration based on white calibration boards (ELM calibration), and two atmospheric radiative transfer model calibrations: 1) radiometric calibration with irradiance data acquired with a drone-mounted down-welling sensor (ARTM), and 2) modeled sun parameters and weather variables in combination with irradiance data from drone-mounted down-welling sensor (ARTM+). Spectral bands from 900-970 nm were found to be associated with disproportionally lower temporal radiometric repeatability than spectral bands from 416-900 nm. ELM calibration was found to be highly sensitive to time of flight missions (which is directly linked to sun parameters and weather conditions). Both ARTM calibrations outperformed ELM calibration, especially ARTM2+. Importantly, ARTM+ calibration markedly attenuated loss of radiometric repeatability in spectral bands beyond 900 nm and therefore improved possible contributions of these spectral bands to classification functions. We conclude that a minimum of 5% radiometric error (radiometric repeatability<95%), and probably considerably more error, should be expected when airborne remote sensing data are acquired at multiple time points across days. Consequently, objects being classified should be in classes that are at least 5% different in terms of average optical traits for classification functions to perform with high degree of accuracy and consistency. This study provides strong support for the claim that airborne remote sensing studies should include repeated data acquisitions from same objects at multiple time points. Such temporal replication is essential for classification functions to capture variation and stochastic noise caused by imaging equipment, and abiotic and environmental variables.