Field experiments to evaluate four different colored sticky cards for trapping non-target insects were conducted in an organic maize field in the Heinigou region of China. Yellow, blue, green, and red sticky cards wer...Field experiments to evaluate four different colored sticky cards for trapping non-target insects were conducted in an organic maize field in the Heinigou region of China. Yellow, blue, green, and red sticky cards were used to trap insects in the field. The total number of insects species caught was 54, with 3,862 individuals recorded. Over half of the specimens caught were non-target insects, including phytophagous insects, particularly dipteran species(including many mosquitoes)(50.3%), followed by target pests(37.0%), and beneficial insects(12.7%). Statistical analysis revealed a significant difference in attraction to target pests, non-target pests, and beneficial insects among treatment groups. The results showed that higher numbers of target pests(Myzus persicae Sulzer, Empoasca flavescens Fabricius, Nysius ericaecshinly Schilling) were caught on yellow sticky card traps compared with blue, green, or red sticky card traps, indicating that yellow was the best trap color for target pests, with green and blue being progressively less attractive. For non-target insects, including phytophagous insects, flies, and mosquitoes,higher numbers of were caught on blue sticky card traps compared with yellow,green, or red sticky card traps. Our study indicated that blue was the most attractive color for flies, especially for the housefly, Musca domestica Linnaeus. Our study also showed that most beneficial insects exhibited preferences to particular trap color characteristics: yellow was the most attractive color for parasitic wasps and lady beetles; blue was the most attractive color for hoverflies and honeybees. In contrast,green and red had no significant attraction to beneficial insects.展开更多
Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of vis...Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.展开更多
基金Supported by the Misereor Foundation(grant ref:335-031-1028 Z)
文摘Field experiments to evaluate four different colored sticky cards for trapping non-target insects were conducted in an organic maize field in the Heinigou region of China. Yellow, blue, green, and red sticky cards were used to trap insects in the field. The total number of insects species caught was 54, with 3,862 individuals recorded. Over half of the specimens caught were non-target insects, including phytophagous insects, particularly dipteran species(including many mosquitoes)(50.3%), followed by target pests(37.0%), and beneficial insects(12.7%). Statistical analysis revealed a significant difference in attraction to target pests, non-target pests, and beneficial insects among treatment groups. The results showed that higher numbers of target pests(Myzus persicae Sulzer, Empoasca flavescens Fabricius, Nysius ericaecshinly Schilling) were caught on yellow sticky card traps compared with blue, green, or red sticky card traps, indicating that yellow was the best trap color for target pests, with green and blue being progressively less attractive. For non-target insects, including phytophagous insects, flies, and mosquitoes,higher numbers of were caught on blue sticky card traps compared with yellow,green, or red sticky card traps. Our study indicated that blue was the most attractive color for flies, especially for the housefly, Musca domestica Linnaeus. Our study also showed that most beneficial insects exhibited preferences to particular trap color characteristics: yellow was the most attractive color for parasitic wasps and lady beetles; blue was the most attractive color for hoverflies and honeybees. In contrast,green and red had no significant attraction to beneficial insects.
基金Supported by the National Natural Science Foundation of China (No. 61032001, No.61002045)
文摘Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.
