Institute of Crop Science, Zhejiang Key Laboratory of Crop Germplasm, Zhejiang University, Hangzhou, 310058, China.
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
Plant Cell Rep. 2024 Aug 19;43(9):220. doi: 10.1007/s00299-024-03298-5.
This study provided a non-destructive detection method with Vis-NIR hyperspectral imaging combining with physio-biochemical parameters in Helianthus annuus in response to Orobanche cumana infection that took insights into the monitoring of sunflower weed. Sunflower broomrape (Orobanche cumana Wallr.) is an obligate weed that attaches to the host roots of sunflower (Helianthus annuus L.) leading to a significant reduction in yield worldwide. The emergence of O. cumana shoots after its underground life-cycle causes irreversible damage to the crop. In this study, a fast visual, non-invasive and precise method for monitoring changes in spectral characteristics using visible and near-infrared (Vis-NIR) hyperspectral imaging (HSI) was developed. By combining the bands sensitive to antioxidant enzymes (SOD, GR), non-antioxidant enzymes (GSH, GSH + GSSG), MDA, ROS (O, OH), PAL, and PPO activities obtained from the host leaves, we sought to establish an accurate means of assessing these changes and conducted imaging acquisition using hyperspectral cameras from both infested and non-infested sunflower cultivars, followed by physio-biochemical parameters measurement as well as analyzed the expression of defense related genes. Extreme learning machine (ELM) and convolutional neural network (CNN) models using 3-band images were built to classify infected or non-infected plants in three sunflower cultivars, achieving accuracies of 95.83% and 95.83% for the discrimination of infestation as well as 97.92% and 95.83% of varieties, respectively, indicating the potential of multi-spectral imaging systems for early detection of O. cumana in weed management.
本研究提供了一种结合生理生化参数的非破坏性检测方法，利用可见近红外（Vis-NIR）高光谱成像技术对向日葵受到列当侵染的情况进行检测，为监测向日葵列当提供了新的思路。向日葵列当（Orobanche cumana Wallr.）是一种专性寄生杂草，它会寄生在向日葵（Helianthus annuus L.）的根部，导致全球向日葵产量显著下降。其地下生命周期结束后，列当幼苗的出现会给作物带来不可逆转的损害。在本研究中，开发了一种快速、可视化、非侵入性和精确的方法，利用可见近红外（Vis-NIR）高光谱成像（HSI）监测光谱特征的变化。通过结合对抗氧化酶（SOD、GR）、非抗氧化酶（GSH、GSH+GSSG）、MDA、ROS（O、OH）、PAL 和 PPO 活性敏感的波段，我们试图建立一种准确的评估这些变化的方法，并对受侵染和未受侵染的向日葵品种进行高光谱成像采集，随后测量生理生化参数并分析防御相关基因的表达。使用三波段图像建立了极限学习机（ELM）和卷积神经网络（CNN）模型，以对三种向日葵品种中的感染或未感染植株进行分类，对感染的判别准确率分别为 95.83%和 95.83%，对品种的判别准确率分别为 97.92%和 95.83%，表明多光谱成像系统在杂草管理中早期检测 O. cumana 具有潜力。
