Special symptoms could be observed on rice leaves when exposed to potassium deficiency, and these symptoms usually display differently under different potassium levels, which offer a foundation for rapid nutrition diagnosis. In this research study, two years of hydroponic experiments on rice (providing 5 levels of potassium nutrition from extremely short to normal) were carried out and the leaf images were acquired by optical scanning at four growth periods. To diagnose the potassium nutrition content, the special symptoms including the yellowish brown leaf margin and the necrotic spots were segmented and quantized by the object-oriented method from leaf images, and the 6 further spectral characteristics of leaf were extracted by the image color analyzing function of MATLAB software. Based on the relationship between potassium content and leaf characteristics, the G value (average value of G channel in the RGB color model) calculated from the entire leaf and leaf tip, the area of yellowish leaf margin, and the number of necrotic spots were applied in the establishment of the identification model of potassium stress by using the support vector machine (SVM). The results indicated that the overall identification accuracies of rice potassium nutrition contents were 90%, 94%, 94%, and 96% at four different growth periods (productive tillering stage, invalid tillering stage, jointing stage, and booting stage), respectively. The data obtained from another year were used to validate the model, and the identification accuracies were 94%, 78%, 80%, and 84%, respectively. Generally speaking, the extraction of the specific symptoms by using object-oriented segmentation is an extension of machine vision technology in diagnosing potassium deficiency, and its application in diagnosing plant nutrition is valuable for the quantization of effective characteristics and improvement of identification accuracy.

中文翻译：

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基于机器视觉和面向对象分割的水稻钾营养胁迫快速识别

暴露于缺钾状态的水稻叶片上会观察到特殊症状，这些症状通常在不同钾水平下表现出不同，这为快速营养诊断提供了基础。在这项研究中，对水稻进行了为期两年的水耕试验（从极短到正常水平提供了5种钾营养水平），并通过光学扫描在四个生长期获得了叶片图像。为了诊断钾的营养含量，通过面向对象的方法从叶片图像中分割并量化了黄褐色的叶缘和坏死斑等特殊症状，并通过图像颜色分析功能提取了叶片的其他6种光谱特征MATLAB软件。ģ值（平均值ģ利用支持向量机（SVM）将整个叶和叶尖，叶边缘的黄色区域和坏死斑点的数量计算出的RGB颜色模型中的通道）用于建立钾胁迫的识别模型。结果表明，在四个生育期（生产分till期，无效分er期，拔节期和孕穗期），水稻钾营养含量的总体鉴定准确度分别为90％，94％，94％和96％。从另一年获得的数据用于验证模型，识别准确度分别为94％，78％，80％和84％。一般来说，