Comparative identification of citrus huanglongbing by analyzing leaves using laser-induced breakdown spectroscopy and near-infrared spectroscopy

Xu, Fanghao; Zeng, Hao; Huang, Lin; Liu, Muhua; Chen, Tianbing; Chen, Jinyin; Zhang, Linying; Zhou, Huamao; Yao, Mingyin

doi:10.1007/s00340-020-7392-8

Cited by 11 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the early stages of HLB infection, leaves show few suspicious symptoms and are indistinguishable from healthy branches. As HLB spreads to an advanced stage, the leaves of some branches begin to yellow unevenly due to bacterial multiplication and aggregation [25]. Fujian Academy of Agricultural Sciences provided citrus field samples for this paper, which were categorized into three classes (figure 1): healthy (HE), HLB-asymptomatic (HA), and HLB-symptomatic (HS).…”

Section: Citrus Leaf Samplesmentioning

confidence: 99%

Early diagnosis of citrus Huanglongbing by Raman spectroscopy and machine learning

Kong,

Liu,

Qiu

et al. 2023

Laser Phys. Lett.

View full text Add to dashboard Cite

Timely diagnosis of citrus Huanglongbing (HLB) is fundamental to suppressing disease spread and reducing economic losses. This paper explores the combination of Raman spectroscopy and machine learning for on-site, accurate and early diagnosis of citrus HLB. The tissue lesion characteristics of citrus leaves at different stages of HLB infection was explored by Raman spectroscopy, and a scientific spectral acquisition strategy was proposed. Combined with machine learning for feature extraction, modeling learning, and predictive analysis, the diagnostic accuracies of principal component analysis (PCA)-Partial least-square and PCA-support vector machine models for the prediction set were 94.07% and 95.56%, respectively. Compared with conventional random detection method, the detection strategy proposed in this paper shows higher accuracy, especially in early HLB diagnosis with significant advantages.

show abstract

Section: Citrus Leaf Samplesmentioning

confidence: 99%

Early diagnosis of citrus Huanglongbing by Raman spectroscopy and machine learning

Kong,

Liu,

Qiu

et al. 2023

Laser Phys. Lett.

View full text Add to dashboard Cite

show abstract

“…4,5 LIBS has many advantages, such as simple sample pretreatment, rapid analysis, simultaneous detection of multiple elements, and the absence of secondary contamination. It has been widely applied in various fields such as coal analysis, 6,7 water monitoring, 8 metallurgy, 9,10 food, 11 biomedicine, 12 deep-sea exploration 13 and other fields. 14,15 Additionally, LIBS is a very promising multi-element online analysis method and has been used in many studies for industrial applications, especially for the inspection of materials on a conveyer belt.…”

Section: Introductionmentioning

confidence: 99%

Online identification and classification of Gannan navel oranges with Cu contamination by LIBS with IGA-optimized SVM

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compared with traditional agricultural heavy metal detection methods, such as atomic fluorescence spectrometry (AFS), atomic absorption spectrometry (AAS), X-ray fluorescence spectroscopy (XRFS), and inductively coupled plasma atomic emission spectrometry (ICP-AES), LIBS has the advantages of a fast detection speed, simultaneous detection of multiple elements, and avoiding secondary contamination, which are unique advantages [ 2 , 3 , 4 , 5 , 6 , 7 ]. These advantages can make the LIBS technique shine in the fields of agriculture, industry, food, biology, medicine, and aerospace [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review

Yang

Ren

et al. 2022

Sensors

View full text Add to dashboard Cite

Heavy metal pollution in agriculture is a significant problem that endangers human health. Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for material and elemental analysis, especially heavy metals, based on atomic emission spectroscopy. The LIBS technique has been widely used for rapid detection of heavy metals with its advantages of convenient operation, simultaneous detection of multi-elements, wide range of elements, and no requirement for the state and quantity of samples. However, the development of LIBS is limited by its detection sensitivity and limit of detection (LOD). Therefore, in order to improve the detection sensitivity and LOD of LIBS, it is necessary to enhance the LIBS signal to achieve the purpose of detecting heavy metal elements in agriculture. This review mainly introduces the basic instruments and principles of LIBS and summarizes the methods of enhanced LIBS signal detection of heavy metal elements in agriculture over the past 10 years. The three main approaches to enhancing LIBS are sample pretreatment, adding laser pulses, and using auxiliary devices. An enhanced LIBS signal may improve the LOD of heavy metal elements in agriculture and the sensitivity and stability of the LIBS technique. The enhanced LIBS technique will have a broader prospect in agricultural heavy metal monitoring and can provide technical support for developing heavy metal detection instruments.

show abstract

Comparative identification of citrus huanglongbing by analyzing leaves using laser-induced breakdown spectroscopy and near-infrared spectroscopy

Cited by 11 publications

References 28 publications

Early diagnosis of citrus Huanglongbing by Raman spectroscopy and machine learning

Early diagnosis of citrus Huanglongbing by Raman spectroscopy and machine learning

Online identification and classification of Gannan navel oranges with Cu contamination by LIBS with IGA-optimized SVM

Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review

Contact Info

Product

Resources

About