中文摘要：

为了将生物特征识别技术应用于追溯系统中的果品标识,该文提出了一种采用西瓜蒂外围纹理信息标识果品个体的方法。该方法首先采集西瓜图像并在瓜蒂外围构造一个环形区域;将环形区域归一化后利用Gabor滤波器对图像纹理进行特征提取及编码;然后通过计算码间的Hamming距进行纹理编码的匹配。在试验中,为100个西瓜在采后贮藏初始、第7天和第14天各采集一幅图像并计算出3组每组100个纹理编码,将第7天、第14天的每一个编码与初始阶段的每一个编码两两比对,累计完成20 000次比对证实西瓜纹理具有唯一性,不同个体的纹理特征各不相同。使用成对数据的假设检验证实在采收后14 d内纹理特征不随时间的推移产生显著变化（P〉0.05）。通过理论和试验证实算法对平移、尺度和旋转具有适应性,即西瓜在图像中的平移、尺度和旋转对Hamming距的计算不会产生显著影响（P〉0.05）。使用最大类间方差法在50个西瓜的纹理编码内训练出判别阈值,并用该阈值判别另外50个西瓜3个时间点间相互比对生成的100个来自同一个西瓜的Hamming距和4 900个来自不同西瓜的Hamming距,结果表明上述Hamming距均能正确判别,准确率与召回率为100%。该研究为农产品追溯系统的标识技术提供新的思路。

英文摘要：

A traceability system is able to provide an opportunity of obtaining quality and safety information of agricultural products from farm to table. Over the past years, many researchers have engaged in it and continuously made significant progresses, however, the identification of fruit has not been resolved perfectly. Traditional identification technologies include barcode, QR（quick response） code and RFID（radio frequency identification） card. Unfortunately, the barcode and QR code paper tags are easily stained in fruit storage and transportation with high humidity, while RFID is rather expensive for low value products like fruits. Biometric identification is distinctive, and measurable characteristics are used to label and describe individuals. The physical characteristics and traits used in biometric identification include but are not limited to fingerprint, iris, voice and face. Since biometric identifiers are unique to individuals, they are more reliable in identification than traditional methods. As a newer and safer technology, biometrics is being extensively studied and widely used in human identification currently. In recent years, it is also used on livestock such as cattle and sheep, yet it has not been reported so far on biometric identification of fresh fruit. This study tried to introduce biometric technology into fruit identification of traceability system to fill in this gap. Based on the algorithm of iris recognition, a watermelon identification method was proposed to exploit texture information of the area around the fruit pedicel. The method could be briefed as the following procedures. The color image of watermelon was firstly transformed into gray image to reduce computational complexity and improve calculation efficiency. Then 2 concentric circles were constructed to center the watermelon pedicel, which would be the annulus area for extracting texture information. To ensure the invariance of translation and scaling, every original annulus area image was normalized to the same size using