公开(公告)号：US20120123681A1

公开(公告)日：2012-05-17

申请号：US13139969

申请日：2009-10-14

申请人： Debora Marcondes Bastos Pereira Milori ,  Ladislau Martin Neto ,  Ednaldo Jose Ferreira ,  Ana Flavia Zaghi ,  Andre Leonardo Venancio

发明人： Debora Marcondes Bastos Pereira Milori ,  Ladislau Martin Neto ,  Ednaldo Jose Ferreira ,  Ana Flavia Zaghi ,  Andre Leonardo Venancio

IPC分类号： G06F19/00 ,  G01N21/64 ,  G01N21/25

CPC分类号： G01N21/6486 ,  G01N33/0098 ,  G01N2021/6421 ,  G01N2201/129

摘要： The present invention relates to a method, apparatus and a system of fast diagnosis of stresses and diseases in higher plants. The proposed methodology is based on the hypothesis of that when a plant is in imbalance; there are changes in its metabolism that render an alteration of the chemical composition of its organs. This chemical alteration leads to a change in the physical properties, such as the fluorescence of the leaves. Due to the complexity of the material of the leaves, the present method proposes that the signal be treated with statistical methods and that the classification is made through softwares based on machine learning. As an example of the application of the invention, the results are shown for the Greening disease in citrus. Currently, Greening is the most severe citrus disease since there is no treatment available for it and due to its high dissemination rate and the fact that it affects all varieties of orange trees, being the diagnosis performed through visual inspection, which renders high subjectivity, high error percentage and the disease is only diagnosed after the expression of the symptoms (˜8 months). During the asymptomatic phase, the infected tree is a source of dissemination of the disease. The present invention can perform the asymptomatic diagnosis of Greening disease from the leaf with a percentage of correct diagnosis higher than 80%.

摘要翻译： 本发明涉及高等植物中应力和疾病快速诊断的方法，装置和系统。 所提出的方法是基于当植物不平衡时的假设; 其代谢改变会改变其器官的化学成分。 这种化学变化导致物理性质的改变，例如叶子的荧光。 由于叶片材料的复杂性，本方法建议采用统计方法对信号进行处理，并通过基于机器学习的软件进行分类。 作为本发明的应用的实例，结果显示了柑橘中的绿化病。 目前，绿化是最严重的柑橘病，因为没有可用的治疗方法，由于其传播率高，影响所有品种的橙树，是通过目视检查进行的诊断，其主观性高，高 错误百分比，疾病仅在症状表达后诊断（〜8个月）。 在无症状期间，感染的树是疾病的传播来源。 本发明可以从正确诊断率高于80％的叶中进行绿色疾病的无症状诊断。

公开(公告)号：US09116125B2

公开(公告)日：2015-08-25

申请号：US13139969

申请日：2009-10-14

申请人： Debora Marcondes Bastos Pereira Milori ,  Ladislau Martin Neto ,  Ednaldo Jose Ferreira ,  Ana Flavia Zaghi ,  Andre Leonardo Venancio

发明人： Debora Marcondes Bastos Pereira Milori ,  Ladislau Martin Neto ,  Ednaldo Jose Ferreira ,  Ana Flavia Zaghi ,  Andre Leonardo Venancio

IPC分类号： G01N21/64

CPC分类号： G01N21/6486 ,  G01N33/0098 ,  G01N2021/6421 ,  G01N2201/129

摘要： The present invention relates to a method, apparatus and a system of fast diagnosis of stresses and diseases in higher plants. The proposed methodology is based on the hypothesis of that when a plant is in imbalance; there are changes in its metabolism that render an alteration of the chemical composition of its organs. This chemical alteration leads to a change in the physical properties, such as the fluorescence of the leaves. Due to the complexity of the material of the leaves, the present method proposes that the signal be treated with statistical methods and that the classification is made through softwares based on machine learning. As an example of the application of the invention, the results are shown for the Greening disease in citrus. Currently, Greening is the most severe citrus disease since there is no treatment available for it and due to its high dissemination rate and the fact that it affects all varieties of orange trees, being the diagnosis performed through visual inspection, which renders high subjectivity, high error percentage and the disease is only diagnosed after the expression of the symptoms (˜8 months). During the asymptomatic phase, the infected tree is a source of dissemination of the disease. The present invention can perform the asymptomatic diagnosis of Greening disease from the leaf with a percentage of correct diagnosis higher than 80%.

摘要翻译： 本发明涉及高等植物中应力和疾病快速诊断的方法，装置和系统。 所提出的方法是基于当植物不平衡时的假设; 其代谢改变会改变其器官的化学成分。 这种化学变化导致物理性质的改变，例如叶子的荧光。 由于叶片材料的复杂性，本方法建议采用统计方法对信号进行处理，并通过基于机器学习的软件进行分类。 作为本发明的应用的实例，结果显示了柑橘中的绿化病。 目前，绿化是最严重的柑橘病，因为没有可用的治疗方法，由于其传播率高，影响所有品种的橙树，是通过目视检查进行的诊断，其主观性高，高 错误百分比，疾病仅在症状表达后诊断（〜8个月）。 在无症状期间，感染的树是疾病的传播来源。 本发明可以从正确诊断率高于80％的叶中进行绿色疾病的无症状诊断。