Monitoring of parasite Orobanche cumana using Vis-NIR hyperspectral imaging combining with physio-biochemical parameters on host crop Helianthus annuus
文献类型： 外文期刊
作者： Li, Juanjuan 1 ; Pan, Tiantian 2 ; Xu, Ling 3 ; Najeeb, Ullah 4 ; Farooq, Muhammad Ahsan 1 ; Huang, Qian 1 ; Yun, Xiaopeng 5 ; Liu, Fei 2 ; Zhou, Weijun 1 ;
作者机构： 1.Zhejiang Univ, Inst Crop Sci, Zhejiang Key Lab Crop Germplasm, Hangzhou 310058, Peoples R China
2.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China
3.Zhejiang Sci Tech Univ, Coll Life Sci & Med, Key Lab Plant Secondary Metab & Regulat Zhejiang P, Hangzhou 310018, Peoples R China
4.Qatar Univ, Off VP Res & Grad Studies, Agr Res Stn, Doha 2713, Qatar
5.Inner Mongolia Acad Agr & Anim Husb Sci, Inst Plant Protect, Hohhot 010031, Peoples R China
关键词： Orobanche cumana; Sunflower; Vis-NIR hyperspectral imaging; Physio-biochemical parameters; Gene expression analysis
期刊名称：PLANT CELL REPORTS （ 影响因子：5.3； 五年影响因子：5.3 ）
ISSN： 0721-7714
年卷期： 2024 年 43 卷 9 期
页码：
收录情况： SCI
摘要： 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-2(-), 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.
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