With a rewritten chapter on UAS safety evaluation including extra models, this fully updated edition remains a comprehensive examination of the myriad issues surrounding the integration of unmanned ...aircraft systems into the US national airspace system.
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The violent use of armed, unmanned aircraft ('drones') is increasing worldwide, but uncertainty persists about the moral status of remote-control killing and why it should be restrained. ...Practitioners, observers and potential victims of such violence often struggle to reconcile it with traditional expectations about the nature of war and the risk to combatants. Addressing the ongoing policy concern that state use of drone violence is sometimes poorly understood and inadequately governed, the book's ethical assessments are not restricted to the application of traditional Just War principles, but also consider the ethics of artificial intelligence (AI), virtue ethics, and guiding principles for forceful law-enforcement. This edited collection brings together nine original contributions by established and emerging scholars, incorporating expertise in military ethics, critical military studies, gender, history, international law and international relations, in order to better assess the multi-faceted relationship between drone violence and justice.
Maritime Unmanned Snowden, Ernest M; Wood, Robert F
2021, 2021-11-15
eBook
Maritime Unmanned recounts the promising beginning, demoralizing setbacks, and ultimate success of the visionaries who championed unmanned aircraft systems (UAS) technology for the U.S. Navy.
The growing possibilities offered by unmanned aerial vehicles (UAV) in many areas of life, in particular in automatic data acquisition, spur the search for new methods to improve the accuracy and ...effectiveness of the acquired information. This study was undertaken on the assumption that modern navigation receivers equipped with real-time kinematic positioning software and integrated with UAVs can considerably improve the accuracy of photogrammetric measurements. The research hypothesis was verified during field measurements with the use of a popular Enterprise series drone. The problems associated with accurate UAV pose estimation were identified. The main aim of the study was to perform a qualitative assessment of the pose estimation accuracy of a UAV equipped with a GNSS RTK receiver. A test procedure comprising three field experiments was designed to achieve the above research goal: an analysis of the stability of absolute pose estimation when the UAV is hovering over a point, and analyses of UAV pose estimation during flight along a predefined trajectory and during continuous flight without waypoints. The tests were conducted in a designated research area. The results were verified based on direct tachometric measurements. The qualitative assessment was performed with the use of statistical methods. The study demonstrated that in a state of apparent stability, horizontal deviations of around 0.02 m occurred at low altitudes and increased with a rise in altitude. Mission type significantly influences pose estimation accuracy over waypoints. The results were used to verify the accuracy of the UAV's pose estimation and to identify factors that affect the pose estimation accuracy of an UAV equipped with a GNSS RTK receiver. The present findings provide valuable input for developing a new method to improve the accuracy of measurements performed with the use of UAVs.
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Core Ideas
A deep learning model identified plant disease in UAV images with 95% accuracy.
Transfer learning allowed for faster model optimization.
This method detected plant disease symptoms at a ...very fine spatial scale.
The detection, diagnosis and quantification of plant diseases using digital technologies is an important research frontier. New and accurate methods would be an asset to growers, for whom early disease detection can mean the difference between successful intervention and massive losses, and plant breeders, who often must rely on time‐consuming phenotyping by eye. We have developed such a method for detecting an important maize (
Zea mays
L.) disease. Northern leaf blight NLB; causal agent
Setosphaeria turcica
(Luttrell) Leonard & Suggs is a foliar disease of maize that causes significant yield losses. Accurately measuring NLB infection is necessary both for breeding more resistant maize lines and for guiding crop management decisions. Visual disease scoring in a large area is time‐consuming and human evaluations are subjective and prone to error. In this work, we demonstrate an automated, high‐throughput system for the detection of NLB in field images of maize plants. Through the use of an unmanned aerial vehicle (UAV) to acquire high resolution images, we trained a convolutional neural network (CNN) model on lower resolution sub‐images, achieving 95.1% accuracy on a separate test set of sub‐images. The CNN model was used to create interpretable heat maps of the original images, indicating the locations of putative lesions. Detecting lesions at a fine spatial scale allows for the potential of unprecedented high‐resolution disease detection for plant breeding and crop management strategies.
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Coffee production and marketing is one of the main global commercial activities, but crop yields depend on several factors, among which plant health. The objective of this study was to evaluate the ...efficiency of spray droplet deposition in coffee crops grown in a mountain region, associated to the efficacy of the control of fungal diseases. The application efficiency, using an unmanned aerial vehicle (UAV), and the efficacy of the products applied were tested. Water-sensitive paper tags were used to analyze the application efficiency; agronomic efficiency, vegetative vigor, yield, and physiological parameters were used to determine the fungicide efficacy. Droplet coverage in the upper canopy layer using a pneumatic sprayer (28.70%) was 4.11-fold higher than that found in the same layer for application using a UAV (6.98%) at the rate of 15 L hasup.−1. The highest droplet depositions by using a UAV were found for the rate of 15 L hasup.−1: 1.60, 1.04, and 0.43 µL cmsup.−2 in the upper, middle, and lower layers, respectively; the deposition in the upper layer with application using a pneumatic sprayer was 42.67 µL cmsup.−2, and therefore, a 26.7-fold higher deposition. The results denote that the control of fungal diseases through fungicide applications using a UAV is efficient for mountain coffee crops.
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