A Review of Airtanker Drop Characteristics, Effectiveness, and Future Research Directions

Unmanned aerial vehicle (UAV) based crop spraying has become a popular alternative in the field of precision agriculture. One of the key goals of UAV based spraying is achieving spray coverage that is as uniform as possible to ensure maximum spray efficacy. Most of the existing studies in the literature focus on analysing the effects of spraying parameters on the uniformity of coverage distribution using experimental studies. However, in this work, we propose a novel generalized data-driven optimal path-planning framework aimed at finding the optimal operational flight parameters (flight speed and pass widths) for a lawnmower coverage path plan to meet the specified spray coverage rate while ensuring the uniformity. The framework takes a spray distribution model as an input and computes the optimal operational parameters for the coverage path plan to minimize coverage non-uniformity without making any assumptions on the UAV type. Furthermore, we also propose a neural network structure using Gaussian kernel neurons to design the spraying model using experimental data. The neural network structure makes no assumption about the type of UAV, onboard nozzle placement, or the flight parameters. The accuracy of the modelling solution only depends on the quality of the training data. In other words, higher diversity of the training data in terms of the flight and spraying parameters would result in a modelling solution that is more representative of the spraying distribution and consequently improve the quality of the operational parameters obtained from the proposed optimization framework. In this work, we present a case study to demonstrate the use case and test the performance of the proposed framework via simulation and experiments using the DJI AGRAS-T10 drone. The results showed that the optimal pass-width solutions for low forward speeds were similar to optimizing the positioning of the nozzles on a boom sprayer to achieve uniform coverage. Whereas, at high speeds, the pass-width was comparatively higher as the spread of the effective coverage over each pass increased. A discussion contextualized in the case study is provided to highlight the salient features and limitations of the proposed framework.

The airport pavement is annually inspected by a visual survey for the presence of distress to keep a high level of safe air traffic. The basic airport pavement distress is a crack, whose main criterion for evaluation is its width. In relation to air traffic safety, the cracks are divided into small, medium, or large categories according to the severity. A modern way of conduction of airport pavement inspection is the use of unmanned aircraft. This article explores the effect of unmanned aircraft flight parameter settings to recognize the distress on the concrete runway. The image data were obtained from the flights at several altitudes above the runway of a former military airport and processed using commercial multi-view reconstruction software. The output orthomosaic images were evaluated according to pixel resolution, ground sampling distance, and ground resolved distance. The correlation between the variables was statistically analyzed using linear and polynomial regression. The low flight altitudes bring a higher level of captured detail, but only the small area is captured, and more data needs to be processed, whereas higher flight altitudes cover a larger area with less data, but the captured detail is getting low. The findings reveal in order to capture the required detail; it is necessary to divide the length of the runway into individual legs, which allows the flight in lower altitudes above ground level. For the used unmanned aircraft, a balance between the flight altitude and the captured detail corresponding to the flight time per battery has been found. The captured data will be further used to create a database of individual distress for deep learning.

Recent technical and jurisdictional advances, together with the availability of low-cost platforms, have facilitated the implementation of unmanned aerial vehicles (UAVs) in individual tree detection (ITD) applications. UAV-based photogrammetry or structure from motion is an example of such a low-cost technique, but requires detailed pre-flight planning in order to generate the desired 3D-products needed for ITD. In this study, we aimed to find the most optimal flight parameters (flight altitude and image overlap) and processing options (smoothing window size) for the detection of taxus trees in Belgium. Next, we tested the transferability of the developed marker-controlled segmentation algorithm by applying it to the delineation of olive trees in an orchard in Greece. We found that the processing parameters had a larger effect on the accuracy and precision of ITD than the flight parameters. In particular, a smoothing window of 3 × 3 pixels performed best (F-scores of 0.99) compared to no smoothing (F-scores between 0.88 and 0.90) or a window size of 5 (F-scores between 0.90 and 0.94). Furthermore, the results show that model transferability can still be a bottleneck as it does not capture management induced characteristics such as the typical crown shape of olive trees (F-scores between 0.55 and 0.61).

The interest in using unmanned aerial platforms for agrochemical spray applications continues to increase; however, methods for assessing the deposition patterns for these systems vary significantly across the current literature base. As with any application platform, understanding the effects that the system and its operational parameters have on spray deposition rate and uniformity across the application site and developing an operational understanding of the appropriate flight-line spacing (also known as the effective swath width) are critical to a successful broadcast application. This work presents results from efforts to scale and adapt the available standards for the measurement of swath width and deposition uniformity from unmanned aerial vehicles (UAVs). The small platform size of current UAVs being considered for broadcast applications, along with the limited nozzle numbers used, potentially provides for deposition patterns that vary across the time of application, making it difficult to achieve acceptable progressive broadcast deposition uniformity. Deposition uniformity and the applied spray rate from finer spray treatments were less sensitive to changes in swath width than coarse spray applications. The inherent variability between replicate passes within a given spray treatment made using current standard methods problematic in providing appropriate guidance on effective swath widths for precise, uniform broadcast spray applications. Improvements in guidance were found with an increased number of replicates and analysis methods that incorporated the random variance between spray passes. The results provide a foundation for further improving methods for measuring spray deposition uniformity and operational guidance for broadcast applications using UAVs, which will be key in determining appropriate nozzle selection, operation, placement, and corresponding flight parameters that result in precise, constant application rates that are biologically effective.

Hyperspectral imaging (HSI) sensors acquire rich spectral information of objects in hundreds of narrow spectral bands, which can be useful in extracting unique features. In recent years, Unmanned Aerial Vehicle (UAV) based HSI techniques are widely used in remote sensing fields due to their wide field of coverages, short revisiting periods, high spectral and spatial resolutions. Pushbroom sensors are line scanners, which acquire data in lines/frames. When a push-broom HSI sensor is used in a UAV platform, the image quality, ground pixel resolution are governed by the UAV and sensor operating parameters, which need to be carefully chosen. In this paper, we propose a mathematical approach for choosing the optimal combination of operating parameters such as UAV speed, flight altitude, sensor frame rate to acquire quality hyperspectral (HS) images with desired ground pixel resolution. Different combinations of camera and flight parameters were tested and evaluated with the classification performance of a convolutional neural network (CNN) model on acquired different vegetation HSI data. We obtained classification accuracies of 96.78%, 97.65%, and 95.55% on HS images acquired from 30m, 40m, and 50m flight altitudes respectively.

To address challenges in hybrid rice seed production—specifically labor dependence, low uniformity of pollen distribution, and low operational efficiency—which collectively drive up large-scale production costs, technological innovations are critical. However, despite the demonstrated potential of UAV-assisted pollination, the quantitative relationships between its operational parameters (altitude, speed, flight patterns) and pollen dispersal dynamics remain poorly understood, impeding standardization efforts. In this study, guided by agronomic pollination requirements, we developed an integrated analytical framework linking “pollen density-yield” dynamics to elucidate the governing mechanisms of flight parameters on pollination quality. A DJI T50 UAV was used to carry out the assisted pollination test on two varieties of hybrid rice, Changtian You 405 and Wanxiang You 377, to explore the effects of different flight speeds, altitudes, and trajectories of the UAV on pollination quality and to evaluate the cost-effectiveness ratio, taking the yield and its composition as the evaluation indexes. The experimental results showed that the UAV flight operation parameters had a significant effect on the pollination quality, and the best pollination quality was obtained when the flight altitude was 4 m and the speed was 3 m/s, achieving yields of 2.64 and 3.15 t/hm2; the average yields of the UAV-assisted pollination were 2.10 and 2.61 t/hm2, and the filled grain percentages were 15.76% and 34.2%, respectively. These increased the yields by 21.4% and 11.06%, respectively, and the filled grain percentages by 8.69% and 3.95%, compared with artificial pollination. The results also showed that the cost-effectiveness ratio of UAV-assisted pollination was 28.11% lower than that of artificial operation. The results indicate that UAVs have great application prospects in hybrid rice pollination.

In this study, a performance review on a high altitude long endurance (HALE) drone which can operate at high-altitude as high as 19.81 km (65000 ft) and can fly typically more than 24 hours was done. General methodology for calculating aircraft performance was presented with an example on an unmanned aerial vehicle (UAV) which is in service today. Maximum endurance and range of UAV were calculated in an interval of 9-16 km flight altitude and 0.35-0.75 flight Mach number. A basic mission profile was chosen for the UAV which is one of the most important design inputs in aircraft design. While the aircraft is designed according to the requirements (payload, range, cost, etc.), mission profile parameters such as altitude and cruising speed of the aircraft are the keys for the selection of engine type. A turbofan engine performance model was used to predict the endurance and range of UAV. The engine performance model gives the output for fuel consumption and thrust which are necessary for endurance and range and also for checking if there is enough thrust to overcome the aerodynamic drag at a given flight speed and altitude. The results were presented as a guide for engineering students, performance engineers and UAV designers in a step by step approach .

The development of modern technologies in agriculture, particularly the use of unmanned aerial vehicles (UA Vs), has enabled significant advancements in pesticide application, resulting in greater efficiency and precision in crop protection. This study aims to analyze the effects of various UA V flight parameters, specifically flight altitude, on droplet size and distribution uniformity. Field trials were conducted on the municipality of Zemun-Belgrade, Republic of Serbia, Latitude: North 44° 49' 22.2" and Longitude: East 20° 13' 19,2", with an altitude of 73 m. Different flight heights (1.5 m and 2.5 m) were tested at a constant flight speed of 3 m/s. The results indicated that smaller droplets, generated at higher flight altitudes, contributed to more uniform droplet distribution, while larger droplets at lower flight altitudes ensured better coverage. The most favorable outcomes were observed at a flight altitude of 2,5 m, where droplet distribution was both more uniform and of higher quality.

Based on the power generation model of photovoltaic modules, the effects of flight speed, altitude, time and area in solar aircraft on the performance of photovoltaic modules have been studied. As the flight speed increases, the power generated by the module increases but tends to saturate. When the conversion efficiency of photovoltaic modules is improved, the required power of the solar aircraft and the power generated by the photovoltaic modules are balanced at a faster flight speed. The power generated by the modules increases with the flight altitude but tends to saturate due to the drop of air temperature and the surface temperature of the module. The higher the altitude, the smaller is the atmospheric density, and atmospheric permeability, and the greater is the solar radiation intensity, and thus the power generated by the module increases. The power generated by the components is the strongest at noon. Battery performance is the strongest in summer and the weakest in winter, as the module’s performance is mainly determined by the intensity of solar radiation. Finally, the energy distribution of solar aircraft and long-time space flight has been discussed.
 J. Bangladesh Acad. Sci. 45(1); 73-83: June 2021

The penetration depth of fuel jet normally injected into the high We number hot air flow was investigated. According to former researches, the momentum ratio, q, between air and injected fuel and We number which represent the ratio of inertia to surface tension are dominant factors that determine liquid fuel penetration depth, therefore the dependency of penetration depth on these two parameters was researched experimentally. The momentum ratio, q, ranged from 30 to 210 and the We number from 590 to 2750. Size of the test section was W40mm H40mm, and the jet atomizers which had 0.3~0.7mm diameter were used. Kerosene, JetA, was used as injection liquid, and a shadowgraph technique was utilized for measuring the penetration depth. The trajectory of outer edge of injected fuel could be expressed with the same correlation as Stenzler’s one [4], but the constant and coefficients of exponent were different from his result.

Research shows that the accurate acquisition of flight parameters of the plant protection UAV and accurate evaluation of flight parameter quality have great significance for improving the effect and precision of spraying. In order to further improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, this study conducted an evaluation and experiment of the flight parameter quality of the plant protection UAV using a laser tracker. The experimental results showed that the current plant protection UAV used the average altitude and speed of the onboard sensors to determine whether it reached the preset flight operation parameters, but this interpretation method could not accurately reflect the actual flight situation. Laser trackers could obtain more accurate flight parameters, especially instantaneous flight parameters. Compared with the laser tracker, the flight trajectory, altitude, and speed of the UAV reflected by onboard sensors were erroneous and tended to be smooth and stable. This method can obtain more accurate flight parameters, improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, and provide data support and a reference for the precision spraying and performance improvement of the plant protection UAV.

This paper presents an aircraft modeling method based on flight dynamics. The real-time geographic coordinates, flight altitude, attitude, heading and speed of aircraft are considered, and the control variables of aircraft rudder surface and pilot response model are introduced. The motion attitude of the aircraft was decoupled into vertical direction and horizontal direction, and a 6-DOF dynamics model was built based on the performance constraints of the aircraft itself. The number of flight parameters is given by simulation and the descent rate at different altitudes is obtained. The feasibility of this method is proved by the comparison with the Honeywell official alarm envelope obtained by linear fitting, so as to judge whether there is potential danger for the aircraft.

Numerical investigation of vented plume into a supersonic flow in the early stage of rocket hot separation

In this study, we investigated the effects of flight and processing parameters on the accuracy of UAV-based photogrammetric digital terrain models (DTM) generated from RGB imagery in ageing deciduous and mixed forest stands. Four 100 × 100 m sample plots were selected, for which the reference terrain surface was established using terrestrial laser scanning. Photogrammetric DTMs derived from various parameter combinations were compared against this reference, analysing the magnitude of deviations and the influence of individual parameters through SHAP (SHapley Additive exPlanations) analysis. Based on the identified effects, we provide recommendations for optimal workflows and parameter settings. The processing chain also incorporates a targeted raster-level smoothing procedure developed by the authors, which effectively removes DTM errors caused by point cloud noise left by filtering algorithms, thereby reducing extreme deviations from the reference surface. The results show that the absolute mean elevation error is primarily influenced by flight parameters and ground point classification scale (parameter of the lasground algorithm). Optimal flight parameters were determined at a flight altitude of 100 m, with 80% front and 90% side overlap. Furthermore, a ground classification scale of 9 m proved optimal in forested environments. The proposed targeted smoothing significantly reduced extreme errors, yielding DTMs with a mean error of approximately 6 cm and maximum deviations of about 40 cm. These accuracies demonstrate that UAV-based photogrammetry, when carefully parameterised, provides a reliable basis for surface model normalization and subsequent forest structural analyses.

Abstract. Precise and detailed digital elevation models (DEMs) are essential to accurately predict overland flow in urban areas. Unfortunately, traditional sources of DEM, such as airplane light detection and ranging (lidar) DEMs and point and contour maps, remain a bottleneck for detailed and reliable overland flow models, because the resulting DEMs are too coarse to provide DEMs of sufficient detail to inform urban overland flows. Interestingly, technological developments of unmanned aerial vehicles (UAVs) suggest that they have matured enough to be a competitive alternative to satellites or airplanes. However, this has not been tested so far. In this study we therefore evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch, and (iv) weather conditions. In addition, we compared the best-quality UAV DEM to a conventional lidar-based DEM. To evaluate both the quality of the UAV DEMs and the comparison to lidar-based DEMs, we performed regression analysis on several qualitative and quantitative metrics, such as elevation accuracy, quality of object representation (e.g. buildings, walls and trees) in the DEM, which were specifically tailored to assess overland flow modelling performance, using the flight parameters as explanatory variables. Our results suggested that, first, as expected, flight altitude influenced the DEM quality most, where lower flights produce better DEMs; in a similar fashion, overcast weather conditions are preferable, but weather conditions and other factors influence DEM quality much less. Second, we found that for urban overland flow modelling, the UAV DEMs performed competitively in comparison to a traditional lidar-based DEM. An important advantage of using UAVs to generate DEMs in urban areas is their flexibility that enables more frequent, local, and affordable elevation data updates, allowing, for example, to capture different tree foliage conditions.