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基于高光谱成像技术的大豆野火病预测评估

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging.

作者信息

Lay Liny, Lee Hong Seok, Tayade Rupesh, Ghimire Amit, Chung Yong Suk, Yoon Youngnam, Kim Yoonha

机构信息

Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea.

Crop Production Technology Research Division, National Institute of Crop Science, Rural Development Administration, Miryang 50424, Republic of Korea.

出版信息

Plants (Basel). 2023 Feb 16;12(4):901. doi: 10.3390/plants12040901.

Abstract

Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as pv. , are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible.

摘要

影响作物产量和生产力的植物病害会损害作物的质量和数量。为了将病害造成的损失降至最低,早期检测是前提条件。最近,已开发出不同的植物病害检测技术。高光谱成像(HSI)是一种用于作物病害早期检测的无损方法,它基于图像的空间和光谱信息。关于植物病害检测,高光谱成像可以预测植物中病害引起的生化和物理变化。诸如丁香假单胞菌大豆致病变种等细菌感染是大豆种植区最常见的植物病害之一,并被认为会大幅降低大豆产量。因此,在本研究中,我们使用了一种基于高光谱成像分析的新方法来早期检测这种病害。我们在大豆生长早期阶段对受细菌性野火病感染的大豆叶片进行了光谱反射率测定。本研究旨在对大豆叶片细菌性野火病早期检测的准确性进行分类。实验使用了两个大豆品种,청자 3호和대찬,分别作为对照(未接种)和处理组(细菌性野火病)。种植18天后进行细菌接种,并在接种细菌24小时后收集图像数据。叶片反射特征显示,患病叶片和健康叶片在绿色和近红外区域存在显著差异。使用Python包算法获得的双向方差分析结果表明,在接种后的第二天和第三天,可以对대찬和청자 3호这两个大豆品种的发病率进行分类,准确率分别为97.19%和95.69%,从而证明这是一种用于该病害早期检测的有用技术。因此,利用高光谱成像这种无损方法创建广泛的各种病害早期检测研究平台是可行的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b641/9967622/850ce03213ba/plants-12-00901-g001a.jpg

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