The increased incidence of glyphosate-resistant weeds has led to an exponential increase in the use of glufosinate on glufosinate-resistant corn, cotton, and soybean crops. Field experiments were conducted in 2021 and 2022 to evaluate peanut response to glufosinate at 25 and 60 d after planting, corresponding to vegetative (V3) and reproductive (R4) growth stages, at 1.2, 4.7, 18.9, 75.5, and 302 g ai ha−1 representing 1/514 to 1/2 of the labeled rate of 604 g ha−1. Peanut injury and canopy and yield reductions from glufosinate were <10% when applied at 1.2, 4.7, and 18.9 g ha−1. However, at 75.5 and 302 g ha−1 peanut injury ranged from 24% to 72% at the V3 exposure timing and 33% to 54% at the R4 exposure timing. Similarly, glufosinate applied at 75.5 and 302 g ha−1 reduced peanut canopy width by 10% to 23% at the V3 exposure timing and by 43% to 57% at the R4 exposure timing. Averaged across exposure timing, peanut yield was reduced by 15% and 61% when glufosinate was applied at 75.5 and 302 g ha−1, respectively. Averaged across rates, peanut yield reduction was 18% at the V3 exposure timing, with glufosinate at 298 g ha−1 required to cause an estimated 50% reduction in yield. Peanut yield was reduced by 20% when glufosinate was applied at the R3 peanut growth stage, whereas glufosinate applied at 243 g ha-1 caused an estimated 50% reduction in yield. There was no difference in normalized difference vegetative index (NDVI) values between untreated plants and peanut exposed to glufosinate at 1.2, 4.7, and 18.9 g ha−1. However, peanut exposed to glufosinate at 75.5 and 302 g ha−1 was distinguished from untreated plants with lower NDVI values. Based on the Pearson correlation coefficient, the best timing for assessing potential yield reduction based on injury was between 2 and 4 wk after treatment.

Timely estimation of silage maize protein provides an effective decision to adapt optimized strategies for nitrogen fertilizer management and also harvesting time for farmers. So, this research aimed to investigate whether using vegetative indices (VIs) derived from UAV remotely sensed multispectral (with 520–900 nm wavelengths) imagery and also Soil Plant Analysis Development (SPAD) greenness index can be used to detect the leaf protein concentration (LPC) of silage maize, as a function of various nitrogen rates (0, 50, 100, and 150% of recommended dosage). Results of principal component analysis (PCA) suggested that LPC was highly correlated with leaf greenness index in all developmental stages. In addition, LPC was highly correlated with most of the VIs investigated. A PCA clustering showed the meaningful pattern of N rates. Higher LPC values, VIs, and greenness index were more concentrated in the higher nitrogen (N100% and N150%) sectors. Nitrogen Reflectance Index (NRI) was identified as the most important VIs to monitor and predict LPC in the silage maize field, showing a strong polynomial relationship with LPC in both eight-leaf collar (V8) (R2 = 0.81, p ≤ 0.01) and tasseling (VT) (R2 = 0.98, p ≤ 0.001) stages. In addition, among VIs, the Normalized Difference Vegetation Index (NDVI) demonstrated a significant linear regression relationship with LPC (R2 = 0.80, p ≤ 0.01) in the VT. Findings suggested the high potential of VIs extracted by UAV-taken multispectral imagery and also SPAD proximal sensing to help farmers rapidly diagnose LPC in silage maize, in line with the objectives of precision farming.

The unmanned aerial vehicle (UAV) flocking among obstacles was transferred to a velocity-controllable UAV flocking problem, which means that multi-UAV gradually form and maintain the $\alpha$ -lattice geometry as they track the desired flocking velocity, and can be applied to tasks such as obstacle avoidance and velocity tracking. Velocity-controllable UAV flocking problem is a multi-objective flocking controller parameters optimisation problem, for which we design flocking velocity and geometry objective function, and solve them using a multi-objective particle swarm optimisation algorithm (MOPSO). On this basis, to address the problem that MOPSO has random results and long computation time, we propose to use a neural network (NN) to approximate the mathematical relationship between the UAV flocking state and the flocking controller parameters. We simulate the flight process of 5 and 49 UAVs performing obstacle avoidance and velocity tracking tasks, respectively. The results show that the proposed UAV flocking controller has better convergence performance, obtains reproducible results, reduces computation time, and can be used for large-scale UAV flocking control.

Full-coverage pedestrian survey to record cultural features on unexplored archaeological landscapes is costly in terms of time, money, and personnel. Over the past two decades, researchers have implemented remote sensing and landscape data collection techniques using unmanned aerial vehicles (UAVs) to combat some of these burdens, but the initial cost of equipment, software, and processing power has hindered the ubiquitous implementation of UAV technology as an accessible companion tool to traditional archaeological survey. This article presents a free and open-source, technology-independent analytical framework for the collection and processing of UAV images to produce high-resolution digital terrain models limited only by the equipment available to the researcher. Results from the free and open-source protocol are directly compared to those produced using proprietary software to illustrate the capabilities of freely available data processing tools for UAV-collected images. By replicating the methods outlined here, researchers should be able to identify and target areas of interest to increase fieldwork efficiency, decrease costs of implementing this technology, and produce high-resolution digital terrain models to conduct spatial analyses that pursue a deeper understanding of cultural landscapes.

In this paper we propose a dynamic image-based visual servoing (IBVS) control for a rotary wing unmanned aerial vehicle (UAV) which directly accounts for the vehicle's underactuated dynamic model. The motion control objective is to follow parallel lines and is motivated by power line inspection tasks where the UAV's relative position and orientation to the lines are controlled. The design is based on a virtual camera whose motion follows the onboard physical camera but which is constrained to point downwards independent of the vehicle's roll and pitch angles. A set of image features is proposed for the lines projected into the virtual camera frame. These features are chosen to simplify the interaction matrix which in turn leads to a simpler IBVS control design which is globally asymptotically stable. The proposed scheme is adaptive and therefore does not require depth estimation. Simulation results are presented to illustrate the performance of the proposed control and its robustness to calibration parameter error.