| 叶绿素荧光成像技术下的柑橘黄龙病快速诊断 |
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| 引用本文： | 翁海勇,何城城,许金钗,刘浪,青家兴,万亮,叶大鹏.叶绿素荧光成像技术下的柑橘黄龙病快速诊断[J].农业工程学报,2020,36(12):196-203. |
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| 作者姓名： | 翁海勇 何城城 许金钗 刘浪 青家兴 万亮 叶大鹏 |
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| 作者单位： | 福建农林大学机电工程学院,福州 350002;现代农业装备福建省高校工程研究中心,福州 310002;浙江大学生物系统工程与食品科学学院,杭州 310058 |
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| 基金项目： | 福建省高峰高原学科项目（712018014）；福建省教育厅中青年教师教育科研项目（107/KLA19043A） |
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| 摘 要： | 柑橘黄龙病被称为柑橘的"癌症",具有极强的传染性,造成柑橘产业巨大的经济损失。为探究病原菌对宿主光合作用进程中对光能吸收、分配和利用的影响,并实现柑橘黄龙病的快速诊断,该研究利用叶绿素荧光成像技术对感染黄龙病不同程度柑橘叶片的叶绿素荧光特性和相应淀粉、蔗糖、葡萄糖和果糖含量进行研究,分析了叶片的叶绿素荧光图像与淀粉、蔗糖、葡萄糖和果糖含量之间的关系,并构建了柑橘黄龙病快速诊断模型。结果表明,染病叶片中的淀粉、蔗糖、葡萄糖和果糖出现异常累积,糖代谢异常与病原菌的侵染有关;宿主的叶绿体光系统Ⅱ(Photosystem Ⅱ, PSⅡ)反应中心遭受破坏,导致最小荧光产量上升,PSⅡ的最大光量子效率下降及PSⅡ中有活性的光反应中心数量减少,染病叶片的光化学反应的能力降低,激发能被转换成不可调制荧光淬灭的比例上升;叶绿素荧光参数能够精确地反演出叶片的淀粉、蔗糖、葡萄糖和果糖含量,两者具有很强的相关性。利用叶绿素荧光参数构建的随机森林模型对柑橘黄龙病诊断的总体识别正确率为97.50%。采用叶绿素荧光成像技术能够实现柑橘黄龙病快速无损检测,可为柑橘黄龙病的早期预警提供新方法。
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| 关 键 词： | 光谱 随机森林算法 叶绿素 荧光 柑橘 黄龙病 光合作用 |
| 收稿时间： | 2020/4/4 0:00:00 |
| 修稿时间： | 2020/5/6 0:00:00 |
Rapid detection of citrus Huanglongbing based on chlorophyll fluorescence imaging technology |
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| Weng Haiyong,He Chengcheng,Xu Jinchai,Liu Lang,Qing Jiaxing,Wan Liang,Ye Dapeng.Rapid detection of citrus Huanglongbing based on chlorophyll fluorescence imaging technology[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(12):196-203. |
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| Authors: | Weng Haiyong He Chengcheng Xu Jinchai Liu Lang Qing Jiaxing Wan Liang Ye Dapeng |
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| Institution: | 1. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 310002, China; 2. Fujian Colleges and Universities Engineering Research Center of Modern Agricultural Equipment, Fuzhou 310002, China;;3. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; |
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| Abstract: | Citrus Huanglongbing (HLB) was considered as the ''cancer'' of citrus trees with highly contagious that had caused a significant economic loss to the citrus industry. The research was aimed to investigate the changes of the photosynthetic response of the absorption, partition, and utilization of excited energy to the HLB infection as well as to develop a method to rapidly detect HLB disease. Chlorophyll fluorescence images of citrus leaves with different infected stages collected from a commercial orchard were measured using a Pulse-Amplitude-Modulation (PAM) chlorophyll fluorescence imaging system. The starch, sucrose, glucose, and fructose within leaves in different infected stages were also determined for carbohydrate metabolic analysis. Results showed an abnormal carbohydrate metabolism with an accumulation of starch, sucrose, glucose, and fructose in HLB infected leaves. The concentration of starch, sucrose, glucose, and fructose in HLB infected leaves was 2.17, 1.60, 1.65, and 1.86 times higher than those in healthy ones, respectively. It showed a high correlation between the abnormal carbohydrate metabolism and the infection of HLB disease based on Pearson correlation analysis. Among 98 different chlorophyll fluorescence parameters related to the functional and structural information of Photosystem II (PSII), the parameters of the minimum fluorescence, the ratio of variable fluorescence to minimum fluorescence, the maximum quantum yield of PSII and the quantum yield of non-regulated energy dissipation presented relatively high sensitivity to HLB pathogen infection by analyzing the loading coefficient of principle component1 based on the Principal Component Analysis (PCA). Compared with healthy leaves, the minimum fluorescence increased in HLB infected ones, while the ratio of variable fluorescence to minimum fluorescence and the maximum quantum yield of PSII decreased. The increasing value of minimum fluorescence in HLB infected leaves indicted a structural alternation at the PSII pigment level, while the decreasing values of the ratio of variable fluorescence to minimum fluorescence and the maximum quantum yield of PSII in HLB infected leaves implied a decreasing number of active photosynthetic centers in the chloroplasts and lower photosynthesis efficiency of PSII. The pathogen of Huanglongbing changed the photosynthetic energy partitioning in citrus leaves due to decreasing the quantum yield of PSII but increasing the quantum yield of non-regulated energy dissipation. The increasing value of the quantum yield of non-regulated energy dissipation in HLB infected leaves demonstrated irreversible damage to PSII after suffering from the HLB pathogen attack. The modification of chlorophyll fluorescence kinetics indicated a disfunction of PSII in HLB infected leaves. Additionally, the chlorophyll parameters provided a good ability to predict starch, sucrose, glucose, and fructose content in citrus leaves based on the Random Forest regression model. The highest values of coefficient of determination of prediction set were 0.90, 0.84, 0.85 and 0.82 with the Residual Prediction Deviation (RPD) of 3.43, 2.50, 2.57 and 2.39 for starch, sucrose, glucose, and fructose, respectively, after optimizing the parameters (the number of trees and the number of trees) of the random forest regression model. The chlorophyll parameters were then used to build a random forest discriminant model to identify HLB disease, which achieved a good detecting performance with the overall accuracies of 100% and 97.50% for calibration set and prediction set, respectively. The detecting performance based on the machine learning method using chlorophyll fluorescence parameters was equivalent to that using carbohydrate metabolic fingerprints (the contents of starch, sucrose, glucose, and fructose) in citrus leaves with the overall accuracies of 100% and 98.75% for calibration set and prediction set, respectively. The results demonstrated that the chlorophyll fluorescence imaging could be used for nondestructive citrus Huanglongbing disease detection, and could provide a guideline for an early warning of citrus Huanglongbing in the orchard. |
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| Keywords: | spectrum random forest chlorophyll fluorescence citrus Huanglongbing photosynthesis |
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