Detecting pink bollworm in cotton bolls using a multiple acoustic sensor system

1994 ◽  
Vol 95 (5) ◽  
pp. 2882-2882 ◽  
Author(s):  
Robert Hickling ◽  
Peter Lee ◽  
Wei Wei ◽  
Michelle Walters Geyer ◽  
David Pierce ◽  
...  
1997 ◽  
Vol 87 (9) ◽  
pp. 940-945 ◽  
Author(s):  
R. K. Garber ◽  
P. J. Cotty

Aspergillus flavus can be divided into the S and L strains on the basis of sclerotial morphology. On average, S strain isolates produce greater quantities of aflatoxins than do L strain isolates. Sclerotia of the S strain were observed in commercial seed cotton from western Arizona. Greenhouse tests were performed to better define sclerotial formation in developing bolls. Eight S strain isolates were inoculated into developing bolls via simulated pink bollworm exit holes. All eight isolates formed sclerotia on locule surfaces, and seven of eight isolates produced sclerotia within developing seed. Boll age at inoculation influences formation of sclerotia. More sclerotia formed within bolls that were less than 31 days old at inoculation than in bolls older than 30 days at inoculation. Frequent formation of sclerotia during boll infection may both favor S strain success within cotton fields and increase toxicity of A. flavus-infected cottonseed. Atoxigenic A. flavus L strain isolate AF36 reduced formation of both sclerotia and aflatoxin when coinoculated with S strain isolates. AF36 formed no sclerotia in developing bolls and was more effective at preventing S strain isolates than L strain isolates from contaminating developing cottonseed with aflatoxins. The use of atoxigenic L strain isolates to prevent contamination through competitive exclusion may be particularly effective where S strain isolates are common. In addition to aflatoxin reduction, competitive exclusion of S strain isolates by L strain isolates may result in reduced overwintering by S strain isolates and lower toxicity resulting from sclerotial metabolites.


2016 ◽  
Vol 67 (1) ◽  
pp. 125 ◽  
Author(s):  
Taejun Moh ◽  
Namdo Jang ◽  
Seok Jang ◽  
Jin Hyung Cho

Although many countries have focused on anti-submarine warfare for several decades, underwater submarines can hardly be detected by current assets such as patrol aircraft, surface ships and fixed underwater surveillance systems. Due to the difficult conditions of the oceanic environment and the relative quietness of submarines, existing acoustic surveillance platforms are not able to fully cover their mission areas. To fill in the gaps, a winch-type towed acoustic sensor system was developed and integrated into a wave-powered unmanned surface vehicle by the Korea Institute of Ocean Science and Technology. In June 2015, sea trial tests were conducted to verify maneuvering, acoustic signal detection, and communication capabilities. During the maneuvering test, the wave-powered glider successfully moved along programmed waypoints. Despite towing the acoustic sensor system, only 20% of initial electricity was consumed in 20 days. The acoustic sensor was lowered to depths of 100–150 m by the winch system, and received signals from an acoustic simulator lowered to depths of 50–100 m by RV Jangmok. Simulated submarine noises that were refracted downward could be clearly received and classified by the hydrophone system, from distances of 2–8 km, while it was being towed silently and deeply. In addition, an optical camera provided high-resolution images of surface vessels, allowing integration with acoustic detection of underwater objects. In conclusion, this new platform using a deeply towed hydrophone system is worthy of consideration as an underwater surveillance asset. Future work is required to strengthen inter-asset communication and obstacle avoidance, and to overcome strong currents to make this technology a reliable part of the underwater surveillance network.


2020 ◽  
Vol 56 (5) ◽  
pp. 604-609
Author(s):  
O. I. Guliy ◽  
B. D. Zaitsev ◽  
O. A. Karavaeva ◽  
A. K. M. Alsowaidi ◽  
O. S. Larionova ◽  
...  

2016 ◽  
Vol 16 (3) ◽  
pp. 806-816 ◽  
Author(s):  
Yin Bi ◽  
Mingsong Lv ◽  
Chen Song ◽  
Wenyao Xu ◽  
Nan Guan ◽  
...  

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