基于地面多光谱成像技术的茄子灰霉病无损检测研究

实时、便捷、可靠的作物病害诊断方法是进行科学的作物喷药管理的基础，也是精细农作的关键技术之一。根据感染灰霉病菌的茄子叶片的光谱反射特征和相应的特征波段的图像信息，利用基于地面的包含绿、红、近红外三波段灰度图的多光谱成像技术对染病茄子叶片进行病斑的无损检测。目的是建立能准确反映植物病害状况的检测模型，实时过滤掉土壤噪声、气候条件等环境干扰，实现对植物健康状况进行快速、准确、非破坏性检测。结果显示，利用绿、红和近红外三通道图像信息算法模型，能够在有干燥的土壤和枯叶等干扰下对灰霉病斑进行较好的识别，为植物生产中病害的在线无损检测提供了新的方法。

Nondestructive Detection of Grey Mold of Eggplant Based on Ground Multi-Spectral Imaging Sensor

Botrytis cinerea Pers. is a worldwide fungus. It is a severe threat to eggplant. Chemistry methods can do an accurate identification, however they are time-consuming, require execution by professionals and are high cost. The present paper presents the development of a ground based multi-spectral imaging sensor for the grey mold detection. Three channels (green, red, near-infrared) of crop images were acquired. Two algorithm systems were developed. The objective of the image processing is to obtain a binary image, which could point out the location of symptoms as accurately as possible. Two image processing methods were developed. It could be seen that method 1 can diagnose the symptoms accurately even if the symptoms are small while method 2 can only diagnose the symptoms with a certain extent area and the detection of symptoms is not very accurate. However, the images processed by method 1 showed some error diagnoses while the method 2 did not. It was concluded that both methods have some advantages and disadvantages. In the agriculture practice, the diagnosis environment will be more complex than in the greenhouse. Some things such as dry soil and perished leaf fragment will disturb the symptom detection by naked eyes when the grower stands away from the leaf. Two image process methods can diagnose the symptoms clearly although the position based on method 2 was a little deviated. It was concluded that the symptoms were well detected using multi-spectra imaging technique even there were some disturbances. Thus, multi-spectral imaging technique is available for the symptoms detection of grey mold on eggplant leaves.