Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Image Processing for Computer Graphics
Image Processing for Computer Graphics
A cognitive vision approach to early pest detection in greenhouse crops
Computers and Electronics in Agriculture
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
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To realize effective insect counting in pheromone traps set in remote sites, a remote monitoring and image processing system based on a sensor network system of ''Field Servers'' has been developed, and two practical methods based on image analysis using this system has been proposed. This system has been employed to monitor the occurrence of the rice bug, Leptocorisa chinensis, in rice paddy fields as a means of reducing the burden of manual insect counting work. A Field Server with a high-resolution digital camera was installed near the pheromone trap for close monitoring. The image data and other monitoring data such as temperature were sent via wireless LAN and the Internet every 5minutes. A remote management system for the Field Server, located about 7.5km from the experimental field, managed data collection and analyzed the data to provide useful information on insect count. An image analysis algorithm based on a background differencing technique has been developed to support counting L. chinensis by implementing an image-processing module in the remote management system. The image-processing module provides three analysis functions: cropping, subtracting, and binarizing the target image. One method is to filter extraneous image data containing no observed target insects (end-members) on the pheromone trap. In this method, the difference between collected image data and the reference image data was calculated, and the total number of pixels whose value was greater than a threshold value for the difference result (number of white pixels) was used for filtering. This method managed to maintain Sensitivity at 100% during the experiment. Accuracy was observed to be 89.1% on average. Using this method, the time spent looking at extraneous image data without L. chinensis can be reduced by 85%. The other method for reducing labor in counting involves estimating the number of end-members automatically using a partial image area that is cropped to focus on a low-noise area, permitting easy analysis. With this method, the image data was analyzed using the first method, and the entire number of end-members was estimated using the number of white pixels and a pixel value equivalent to one end-member. The results of this method correspond reasonably closely to the results obtained by manual counting. The correlation coefficient for the daily occurrence rate was 0.974 and that for the hourly rate was 0.916.