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首页> 外文会议>International Conference of Artificial Intelligence and Information Technology >Detection of Anomalies in Citrus Leaves Using Digital Image Processing and T2 Hotelling Multivariate Control Chart
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Detection of Anomalies in Citrus Leaves Using Digital Image Processing and T2 Hotelling Multivariate Control Chart

机译：使用数字图像处理和T 2 热预热多变量控制图谱检测柑橘叶中的异常

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摘要

Citrus is one of the important horticulture products in Indonesia. Production fluctuations that occurred in several production centers were caused by the attack of the disease. Usually, identification and detection of citrus plant anomalies were carried out by observing symptoms of disease in leaves directly on the field. To confirm anomalies of citrus plants caused by disease, several tests could be carried out, including physiological assessment and testing their genetic properties. To use those techniques as early warning technique to detect citrus plant anomalies is not efficient, because the test takes time and costs. The technology that is currently developing as an alternative to a conventional method for early warning observation is image processing. RGB information extracted from image processing was used by T2 Hotelling multivariate control chart to detect changes in citrus leaf color. T2 Hotelling multivariate control chart uses RGB information as multivariate input to determine occurred anomalies. The application of image processing technique and T2 Hotelling multivariate control chart could help early examination of the signs of citrus plants anomalies that are likely caused by a disease. T2 Hotelling multivariate control chart method was able to determine between healthy and anomalies occured citrus leaves with accuracy 83%.
机译：柑橘是印度尼西亚重要的园艺产品之一。几个生产中心发生的生产波动是由疾病的攻击引起的。通常，通过直接在场上观察叶片的疾病症状来进行鉴定和检测柑橘植物异常。为了确认由疾病引起的柑橘植物的异常，可以进行几种测试，包括生理学评估并测试其遗传性质。使用这些技术作为预警技术来检测柑橘植物异常是不高效的，因为测试需要时间和成本。目前正在开发作为常规预警观察方法的替代方法的技术是图像处理。从图像处理中提取的RGB信息由T使用 2 热性多变量控制图以检测柑橘叶颜色变化。 T. 2 Hotelling Multivariate Control图表使用RGB信息作为多变量输入以确定发生的异常。图像处理技术的应用和T 2 热性多元控制图可能有助于早期检查可能由疾病引起的柑橘植物异常的迹象。 T. 2 Hoteling多变量控制图方法能够在健康和异常之间确定柑橘叶，精度为83％。

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