Parasitoids exploit numerous chemical cues to locate hosts and food. Whether they detect and learn chemicals foreign to their natural history has not been explored. We show that the parasitoid Microplitis croceipes can associate, with food or hosts, widely different chemicals outside their natural foraging encounters. When learned chemicals are subsequently detected, this parasitoid manifests distinct behaviors characteristic with expectations of food or host, commensurate with prior training. This flexibility of parasitoids to rapidly link diverse chemicals to resource needs and subsequently report them with recognizable behaviors offers new insights into their foraging adaptability, and provides a model for further dissection of olfactory learning related processes.

Palmer amaranth is resistant to several herbicides, including glyphosate, and there is concern that the resistance traits can be transferred between spatially segregated populations via pollen movement. The objective of this study was to describe the physical properties of Palmer amaranth pollen, specifically size, density, and settling velocity (Vs), that influence pollen flight. The mean diameter for Palmer amaranth pollen, as determined by light microscopy, was 31 µm (range of 21 to 38 µm); mean pollen diameter as measured with the use of an electronic particle sizer was 27 µm (range of 21 to 35 µm). The mean density of the solid portion of the pollen grain was 1,435 kg m−3. Accounting for the density of the aqueous fraction, the mean density of a fully hydrated pollen grain was 1,218 kg m−3. By Stokes's law, the estimated mean theoreticalVsfor individual Palmer amaranth pollen grains was 3.4 cm s−1for the range of pollen diameters with a mean of 31 µm and 2.6 cm s−1for the range of pollen diameters with a mean of 27 µm. Results from laboratory studies indicated the majority of single pollen grains settled at a rate of 5.0 cm s−1. The difference between the theoretical and empirical estimates ofVswas likely due to changes in pollen density and shape postanthesis, which are not accounted for using Stokes's law, as well as the presence pollen clusters.

Abstract-Autonomous crop monitoring at high spatial and temporal resolution is a critical problem in precision agriculture. While Structure from Motion and Multi-View Stereo algorithms can finely reconstruct the 3D structure of a field with low-cost image sensors, these algorithms fail to capture the dynamic nature of continuously growing crops. In this paper we propose a 4D reconstruction approach to crop monitoring, which employs a spatio-temporal model of dynamic scenes that is useful for precision agriculture applications. Additionally, we provide a robust data association algorithm to address the problem of large appearance changes due to scenes being viewed from different angles at different points in time, which is critical to achieving 4D reconstruction. Finally, we collected a high quality dataset with ground truth statistics to evaluate the performance of our method. We demonstrate that our 4D reconstruction approach provides models that are qualitatively correct with respect to visual appearance and quantitatively accurate when measured against the ground truth geometric properties of the monitored crops.

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