Conservation of North America's eastern monarch butterfly (Danaus plexippus) population would require establishment of milkweed (Asclepias spp.) and nectar plants in the agricultural landscapes of North Central United States (U.S.). A variety of seed‐treatment and foliar insecticides are used to manage early‐ and late‐season pests in these landscapes. Thus, there is a need to assess risks of these insecticides to monarch butterfly life stages to inform habitat conservation practices. Chronic and acute dietary toxicity studies were undertaken with larvae and adults and acute topical bioassays were conducted with eggs, pupae, and adults using six representative insecticides: beta‐cyfluthrin (pyrethroid), chlorantraniliprole (anthranilic diamide), chlorpyrifos (organophosphate), and imidacloprid, clothianidin, and thiamethoxam (neonicotinoids). Chronic dietary LC50 values for monarch larvae ranged from 1.6 x 10^‐3 (chlorantraniliprole) to 5.3 (chlorpyrifos) μg/g milkweed leaf, with the neonicotinoids producing high rates of arrested pupal ecdysis. Chlorantraniliprole and beta‐cyfluthrin were generally the most toxic insecticides to all life stages, and thiamethoxam and chlorpyrifos were generally the least toxic. The toxicity results were compared to insecticide exposure estimates derived from a spray drift model and/or milkweed residue data reported in the literature. Aerial applications of foliar insecticides are expected to cause high downwind mortality in larvae and eggs with lower mortality predicted for adults and pupae. Neonicotinoid seed treatments are expected to cause little to no downslope mortality and/or sublethal effects in larvae and adults. Given the vagile behavior of non‐migratory monarchs, considering these results within a landscape‐scale context suggests adult recruitment will not be negatively impacted if new habitat is established in close proximity of maize and soybean fields in the agricultural landscapes of North Central U.S.

Air-assisted spraying is a commonly used spraying method for orchard plant protection operations. However, its spraying parameters have complex effects on droplet distribution. The lack of large-scale 3D droplet density distribution measurement methods of equipment has limited the optimization of spraying parameters. Therefore, there is a need to develop a method that can quickly obtain 3D droplet distribution. In this study, a 2D LiDAR was used to quickly scan moving droplets in the air, and a test method that can obtain the visualization of 3D droplet distribution was constructed by using the traveling mode of the machine perpendicular to the scanning plane. The 3D droplet distribution at different positions of the nozzle installed in the air-assisted system was tested at different fan rotation speeds, and the methods for signal processing, point cloud noise reduction, and point cloud division for 2D LiDAR were developed. The results showed that the LiDAR-based method for detecting 3D droplet distribution is feasible, fast, and environmentally friendly.

The nitrogen (N) and phosphorus (P) status of cotton leaves plays a crucial role in crop growth and fiber quality. Knowledge of the plant nutrient status is therefore essential for optimal site adapted fertilization and yields. Hyperspectral sensing is an emerging technology that enables rapid and non-destructive assessment of plant nutritional status. In this study, graded N and P fertilizer treatments were conducted to grow cotton plants with varying nutrient supply. Hyperspectral reflectance data of the cotton leaves were collected at the budding and the boll opening stage and processed using machine learning methods to analyze and select spectral band with respect to their importance. Based on the selected spectral bands, classification models were developed to distinguish different N and P treatments. In a further step, regression models were established to estimate leaf N and P concentrations. The results indicated that at least 50 spectral bands, primarily located in the green, red, and red-edge regions, were required to achieve reliable prediction performance. Among the evaluated models, the Support Vector Machine (SVM) demonstrated the best classification performance, achieving an accuracy of 91.0% for nitrogen treatments at the budding stage and 86.5% at the boll opening stage. For phosphorus treatments, the classification accuracy reached 82.7% at the budding stage and 74.0% at the boll opening stage. These findings highlight the potential of hyperspectral sensing combined with machine learning for precise optical monitoring and classification of cotton nutrient status.