To realize quick localization of plant maize, a new real-time localization approach is proposed for maize cores at the seedling stage, which can meet the basic demands for localization and quantitative fertilization in precision agriculture and reduce environmental pollution and the use of chemical fertilizers. In the first stage, by taking pictures of maize at the seedling stage in a field with a monocular camera, the maize is segmented from the weed background of the picture. And then the three most-effective methods (i.e., minimum cross entropy, ISODATA, and the Otsu algorithm) are found from six common segmentation algorithms after comparing the accuracy rate of extracting maize and the time efficiency of segmentation. In the second stage, plant core from segmented maize image is recognized, and localized, based on different brightness of the rest part of maize core and plant. Then the geometric center of maize core is considered as localization point. the best effect of extracting maize core was found from the minimum cross entropy method based on gray level. According to experimental validation using many field pictures, under weedy conditions on sunny days, the proposed method has a minimum recognition rate of 88.37% for maize cores and is more robust at excluding weeds.

中文翻译：

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基于机器视觉的玉米芯苗期实时定位方法

为了实现植物玉米的快速本地化，提出了一种在苗期玉米芯实时定位的新方法，可以满足精准农业对本地化和定量施肥的基本要求，减少环境污染和化学肥料的使用。在第一阶段，通过用单眼相机在田间的幼苗期拍摄玉米的图像，将玉米从图像的杂草背景中分割出来。在比较了玉米提取的准确率和分割的时间效率之后，从六种常见的分割算法中找到了三种最有效的方法（即最小交叉熵，ISODATA和Otsu算法）。在第二阶段，从分割的玉米图像中识别并定位植物核心，根据玉米芯和植物其余部分的亮度不同。然后将玉米芯的几何中心视为定位点。通过基于灰度的最小交叉熵方法，发现提取玉米芯的最佳效果。根据使用许多野外图片进行的实验验证，在晴天的杂草条件下，该方法对玉米芯的最低识别率为88.37％，并且在排除杂草方面更可靠。