郑永军, 吴 刚, 王一鸣, 毛文华. 基于模糊模式的蝗虫图像识别方法[J]. 农业工程学报, 2010, 26(14): 21-25.
    引用本文: 郑永军, 吴 刚, 王一鸣, 毛文华. 基于模糊模式的蝗虫图像识别方法[J]. 农业工程学报, 2010, 26(14): 21-25.
    Zheng Yongjun, Wu Gang, Wang Yiming, Mao Wenhua. Locust images detection based on fuzzy pattern recognition[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(14): 21-25.
    Citation: Zheng Yongjun, Wu Gang, Wang Yiming, Mao Wenhua. Locust images detection based on fuzzy pattern recognition[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(14): 21-25.

    基于模糊模式的蝗虫图像识别方法

    Locust images detection based on fuzzy pattern recognition

    • 摘要: 蝗灾是中国农业病虫害防治的重点,采用低空机载蝗虫预警系统技术,对拍摄图像中蝗虫识别和计数,实现蝗虫监测,可以弥补蝗虫地面人工监测的不足。以广东省清远市英德区域农田为试验区,采用数码相机采集蝗虫图像,对蝗虫区域和背景的RGB分量平均值进行对比分析,选用超G绝对值法进行灰度转换,实现蝗虫与背景分离。通过面积统计对比,确定单个蝗虫的面积和周长特征,建立单个蝗虫模糊集和粘连重叠蝗虫区域模糊集,采用最大隶属度原则可以判定蝗虫连通区域为单个蝗虫或是存在图像粘连重叠。用模糊识别方法对单个和粘连重叠的区域分别计算数量,准确率达89%,可以满足蝗虫灾害的测报要求。

       

      Abstract: Locust control is the focus of agricultural pest management. As a complement of manually monitoring, low-attitude airborne early warning system can be used to monitor locusts by identifying and counting them from the captured locust image. The experimental field was at Qingyuan, Guangdong. Locust images were captured by digital camera. By contrastive analysis of the average of R, G and B value of locust area and background, a method of extra-green absolute value was adopted to segment locusts from the background. The area and perimeter of each locust were obtained by comparing area statistical value. Fuzzy sets of individual locust object and connected locust regions were established respectively. Individual locust or connected locust regions were determined by the maximum membership degree principle. The accuracy of the fuzzy recognition of individual and connected locust region was 89%, which could satisfy the requirement of locust pre-warning.

       

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