The Impact of Automation Failure on Unmanned Aircraft System Operators’ Performance, Workload, and Trust in Automation

Drones, Journal Year: 2025, Volume and Issue: 9(3), P. 165 - 165

Published: Feb. 23, 2025

Automation failures in Unmanned Aircraft Systems (UASs) significantly lead to a decrease overall system performance, an increase operator workload, and deterioration automation trust. This study investigates how the frequency intensity of differ multi-subsystem environments. An improved automated MATB (Multi-Attribute Task Battery) paradigm was used quantify failure at four levels. Through operational experiments incorporating eye-tracking technology, we examined effects different levels on dependent variables. Data were analyzed using descriptive statistics, ANOVA, nonparametric tests, revealing that while deteriorated trust, task not all variables showed consistent changes across levels, indicating presence plateau effect certain cases. Trust negatively mediated participants’ perceptions workload context failure. These results suggest contexts can have differing operators, especially complex socio-technical systems involving multiple subsystems, which should be generalized regarding whether they fail or not. In practical applications, designers could consider trust (through personnel training, design, etc.) reduce negative impact performance workload.

Language: Английский

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UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 159 - 159

Published: Jan. 10, 2025

The quality of the image data and potential to invert crop growth parameters are essential for effectively using unmanned aerial vehicle (UAV)-based sensor systems in precision agriculture (PA). However, existing research falls short providing a comprehensive examination inversion parameters, there is still ambiguity regarding how affects potential. Therefore, this study explored application RGB multispectral (MS) images acquired from three lightweight UAV platforms realm PA: DJI Mavic 2 Pro (M2P), Phantom 4 Multispectral (P4M), 3 (M3M). reliability pixel-scale was evaluated based on assessment metrics, winter wheat above-ground biomass (AGB), plant nitrogen content (PNC) soil analysis development (SPAD), were inverted machine learning models multi-source features at plot scale. results indicated that M3M outperformed M2P, while MS marginally superior P4M. Nevertheless, these advantages did not improve accuracy Spectral (SFs) derived P4M-based demonstrated significant AGB (R2 = 0.86, rRMSE 27.47%), SFs M2P-based camera exhibited best performance SPAD 0.60, 7.67%). Additionally, combining spectral textural yielded highest PNC 0.82, 14.62%). This clarified prevalent mounted PA their influence parameter potential, offering guidance selecting appropriate sensors monitoring key parameters.

Language: Английский

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Improvement of Citrus Yield Prediction Using UAV Multispectral Images and the CPSO Algorithm

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 171 - 171

Published: Jan. 12, 2025

Achieving timely and non-destructive assessments of crop yields is a key challenge in the agricultural field, as it important for optimizing field management measures improving productivity. To accurately quickly predict citrus yield, this study obtained multispectral images fruit maturity through an unmanned aerial vehicle (UAV) extracted vegetation indices (VIs) texture features (T) from feature variables. Extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), gaussian process regression (GPR), multiple stepwise (MSR) models were used to construct number quality prediction models. The results show that, prediction, XGB model performed best under combined input VIs T, with R2 = 0.792 RMSE 462 fruits. However, RF when only used, 0.787 20.0 kg. Although accuracy was acceptable, variables large. further improve performance, we explored method that utilizes hybrid coding particle swarm optimization algorithm (CPSO) coupled SVM had significant improvement predicting fruits, especially CPSO (CPSO-XGB). CPSO-XGB fewer higher accuracy, > 0.85. Finally, Shapley additive explanations (SHAP) reveal importance normalized difference chlorophyll index (NDCI) red band mean (MEA_R) constructing model. provide application reference theoretical basis research on UAV remote sensing relation yield.

Language: Английский

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Enhancing UAV Security Against GPS Spoofing Attacks Through a Genetic Algorithm-Driven Deep Learning Framework

Information, Journal Year: 2025, Volume and Issue: 16(2), P. 115 - 115

Published: Feb. 7, 2025

Unmanned Aerial Vehicles (UAVs) are increasingly employed across various domains, including communication, military, and delivery operations. Their reliance on the Global Positioning System (GPS) renders them vulnerable to GPS spoofing attacks, in which adversaries transmit false signals manipulate UAVs’ navigation, potentially leading severe security risks. This paper presents an enhanced integration of Long Short-Term Memory (LSTM) networks with a Genetic Algorithm (GA) for detection. Although GA–neural network combinations have existed decades, our method expands GA’s search space optimize wider range hyperparameters, thereby improving adaptability dynamic operational scenarios. The framework is evaluated using real-world dataset that includes authentic malicious under multiple attack conditions. While we discuss strategies mitigating CPU resource demands computational overhead, acknowledge direct measurements energy consumption or inference latency not included present work. Experimental results show proposed LSTM–GA approach achieved notable increase classification accuracy (from 88.42% 93.12%) F1 score 87.63% 93.39%). These findings highlight system’s potential strengthen UAV against provided hardware constraints other limitations carefully managed real deployments.

Language: Английский

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Validation of the spray drift modeling software AGDISPpro applied to remotely piloted aerial application systems

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 966, P. 178725 - 178725

Published: Feb. 1, 2025

Regulatory bodies worldwide are currently developing modeling frameworks to simulate pesticide drift following applications from remotely piloted aerial application systems (RPAAS). Unfortunately, there no validated mechanistic models that off-target droplet movement these systems. To respond this gap, we evaluated AGDISPpro, an established Lagrangian-based and deposition model by fixed rotary wing aircraft. Specifically, two of the nine RPAAS available in i.e., PV22 quadcopter PV35X hexacopter models. Our detailed evaluation relied on sets field studies: a series single-swath using medium extremely coarse spray nozzles, four-swath fine ultra nozzles. AGDISPpro predictions were compared in-swath downwind measurements. The r index agreement ranged 0.47 0.92 for 0.61-0.94 0.86 0.93 0.48-0.55 ultra-coarse nozzles respectively. There is uncertainty regarding how swath width displacement behavior affect location, width, magnitude peak plume. Thus, further research required reduce uncertainty. Overall, study demonstrates promising tool effects RPAAS.

Language: Английский

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ADVANCING PRECISION AGRICULTURE WITH UAV’S: INNOVATIONS IN FERTILIZATION

INMATEH Agricultural Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1057 - 1072

Published: Feb. 12, 2025

Unmanned Aerial Vehicles (UAVs) are revolutionizing precision agriculture, particularly in the domain of fertilization. Equipped with advanced sensors, mapping tools, and variable-rate application systems, drones enable farmers to precisely distribute fertilizers based on field variability. This targeted approach reduces waste, minimizes environmental impact, optimizes crop yield. The integration technologies such as multispectral imaging AI-driven decision-making systems further enhances efficiency by allowing real-time assessment soil conditions. Despite their numerous advantages, challenges high costs, regulatory limitations, technical scalability remain key barriers widespread adoption. article explores innovations UAVs bring fertilization, benefits, obstacles hindering broader agriculture

Language: Английский

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Sustainable Weed Management in Rice to Ensure Food Security: A Review

ACS Agricultural Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Globally, food security has become a severe issue with the increase in world population. Infestations of weeds are well acknowledged as significant biological constraint to crop yield across agroecosystems and seasons. However, high labor costs have led decline use conventional manual weeding methods, this trend been mirrored worldwide by an synthetic herbicides. Continuous herbicides increases possibility herbicide resistance, contaminated agricultural goods, adverse impacts on environment human health. Because these issues, researchers now interested finding alternatives There is no single effective solution for combating weeds; therefore, review focuses developing implementing more sustainable weed management that involves cultural, mechanical, control, efficient chemical artificial intelligence. The synthesizes findings from wide range peer-reviewed studies, case reports, extension documents. By examining current state offers valuable insights both organic growers seeking manage populations while minimizing environmental impact. Ultimately, it aims contribute global promoting resilient practices.

Language: Английский

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Challenges of drone development in Iran’s agricultural sector: The application of the TOWS analysis

Cleaner Engineering and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100950 - 100950

Published: March 1, 2025

Language: Английский

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Field Rice Growth Monitoring and Fertilization Management Based on UAV Spectral and Deep Image Feature Fusion

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 886 - 886

Published: April 1, 2025

Rice, as a globally vital staple crop, requires efficient field monitoring to ensure optimal growth conditions. This study proposed novel framework for classifying nutrient deficiencies and formulating fertilization strategies in field-grown rice by fusing UAV-derived vegetation indices (VIs) with deep image features extracted via neural networks. The integrated visible light VIs, spectral provide comprehensive reflection of crop nutritional conditions, aligning closely practical production needs. achieved nutrition classification accuracies 88.78% 84.56% spikelet protection fertilizer application stage (S1) bud-promoting (S2), while the fusion VIs significantly enhanced accuracy classification, RF model achieving highest (97.50% S1 96.56% S2). strategy effectively improved traits, demonstrating potential UAV-based remote sensing precision agriculture, which would scalable solution optimizing cultivation ensuring food security.

Language: Английский

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Investigation of Emerging Technologies in Agriculture: An In-depth Look at Smart Farming, Nano-agriculture, AI, and Big Data

Journal of Biosystems Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

Language: Английский

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