Oviposition behavior of western tarnished plant bug, Lygus hesperus Knight, as affected by residual insecticides, was studied in potted as well as field-grown cotton. In this study, we investigated the spatial distribution and phytotomical preference of Lygus oviposition in presquaring and blooming cotton, as affected by selected insecticides. Flonicamid, acephate, and cypermethrin were applied to cotton at 82, 516, and 114 g (active ingredient)/ha, respectively. At 3 d posttreatment, a gravid female Lygus was caged on the plants. After 4 d, caged plants were harvested and eggs were counted by whole plant dissection. Results indicated significantly greater egg deposition in untreated controls than treated plants in both potted and field-grown cotton. For untreated plants, Lygus preferred to oviposit on the pulvinus and leaf petiole, where 76 and 85% of eggs were laid in potted and field-grown cotton, respectively. For insecticide-treated plants, no plant structure preference was observed, although fewer eggs were laid. The upper stratum of the plant canopy had significantly more eggs than the lower or middle strata of untreated plants, while more eggs were observed in the middle strata of plants treated with acephate in field-grown cotton. Sublethal effects could not be adequately assessed in the cypermethrin treatment owing to high Lygus mortality.

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Due to high degrees of freedom of humanoids and induced redundancy, there are multiple ways of performing a given manipulation task. Finding optimal ways of performing tasks is one desirable property of any motion planning framework. This includes optimizing the path with respect to a certain objective function. Additionally, a variety of constraints need to be satisfied such as stability, self-collision and collision with objects in the environment and also kinematic closed-loop chains formed during the task. Time requirements of the planner is another important aspect that drives us to use sampling based methods. In this paper, we present an asymptotically optimal sampling based approach for generating statically stable motion plans. We use RRT*-connect algorithm which we obtained by modifying RRT-Connect. Moreover, we use a gradient based inverse kinematics solver to generate goal configurations. We evaluate the efficacy of our approach in the results section in a simulation environment on Hubo+ robot model. The results show a significant improvement in path costs as well as overall optimality of the given task.

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