Visual discrimination of citrus HLB based on image features

Liu, Yande; Xiao, Huaichun; Xu, Hai Feng; Rao, Yu; Jiang, Xiaogang; Sun, Xudong

doi:10.1016/j.vibspec.2019.04.001

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…The validation of visually observed HLB symptoms was conducted through a molecular examination using a method developed by Himawan et al [10], Liu Y et al [11], Sankaran et al [12]Martinelli et al [13]. Laboratory analysis using PCR yielded results indicating that of the 30 samples collected from 10 orchards displaying HLB symptoms (3 samples per orchard), 4 of the sample trees, or 13.3% indicated positive for HLB infection (Table 2).…”

Section: Pcr Validation Of Visual Symptoms Of Hlbmentioning

confidence: 99%

Climate change mitigation and seasonal infestation patterns of citrus psyllid Diaphorina citri: implications for managing huanglongbing (HLB) disease in tangerine citrus

Endarto,

Wicaksono,

Wuryantini

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The citrus psyllid, Diaphorina citri, is a recognized vector of the devastating Huanglongbing (HLB) disease in citrus plants, posing a significant threat to global citrus production. As climate change continues to influence agroecological conditions, comprehending the population dynamics of this vector is imperative for effective disease management. This study delved into the relationship between climate variations and D. citri infestation patterns in tangerine citrus orchards from January to December 2018. Leveraging a dataset of 12 monthly observation sets, we comprehensively analyzed the abundance of the citrus psyllid population. Our investigation unveiled a clear association between climate variability and the incidence of citrus psyllid infestations and HLB occurrences. Importantly, our findings revealed that D. citri infestations were confined to the budding phase. Contrary to expectations, rainfall and temperature did not correlate positively with the development of the D. citri population or the prevalence of HLB disease in tangerine citrus. These results suggest other factors significantly affect HLB spread, impacting disease management. Further research is needed to identify and understand these factors, providing valuable insights for mitigating climate change’s impact on citrus cultivation.

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Section: Pcr Validation Of Visual Symptoms Of Hlbmentioning

confidence: 99%

Climate change mitigation and seasonal infestation patterns of citrus psyllid Diaphorina citri: implications for managing huanglongbing (HLB) disease in tangerine citrus

Endarto,

Wicaksono,

Wuryantini

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Deng et al used natural light to acquire citrus leaf images, and C-SVC and BPNN were used to perform pattern recognition, with an accuracy of 91.93% and 92%, respectively [ 6 , 12 ]. Liu et al used hyperspectral imaging to acquire citrus leaf images and extracted image features based on GLCM [ 13 ]. The PLS-DA model was used for the identification of HLB-symptomatic leaves, and the results showed that the method was effective for the identification of HLB-symptomatic samples.…”

Section: Introductionmentioning

confidence: 99%

Custom-Developed Reflection–Transmission Integrated Vision System for Rapid Detection of Huanglongbing Based on the Features of Blotchy Mottled Texture and Starch Accumulation in Leaves

Cai

et al. 2023

Plants

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Huanglongbing (HLB) is a highly contagious and devastating citrus disease that causes huge economic losses to the citrus industry. Because it cannot be cured, timely detection of the HLB infection status of plants and removal of diseased trees are effective ways to reduce losses. However, complex HLB symptoms, such as single HLB-symptomatic or zinc deficiency + HLB-positive, cannot be identified by a single reflection imaging method at present. In this study, a vision system with an integrated reflection–transmission image acquisition module, human–computer interaction module, and power supply module was developed for rapid HLB detection in the field. In reflection imaging mode, 660 nm polarized light was used as the illumination source to enhance the contrast of the HLB symptoms in the images based on the differences in the absorption of narrow-band light by the components within the leaves. In transmission imaging mode, polarization images were obtained in four directions, and the polarization angle images were calculated using the Stokes vector to detect the optical activity of starch. A step-by-step classification model with four steps was used for the identification of six classes of samples (healthy, HLB-symptomatic, zinc deficiency, zinc deficiency + HLB-positive, magnesium deficiency, and boron deficiency). The results showed that the model had an accuracy of 96.92% for the full category of samples and 98.08% for the identification of multiple types of HLB (HLB-symptomatic and zinc deficiency + HLB-positive). In addition, the classification model had good recognition of zinc deficiency and zinc deficiency + HLB-positive samples, at 92.86%.

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Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat

et al. 2020

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Visual discrimination of citrus HLB based on image features

Cited by 7 publications

References 14 publications

Climate change mitigation and seasonal infestation patterns of citrus psyllid Diaphorina citri: implications for managing huanglongbing (HLB) disease in tangerine citrus

Climate change mitigation and seasonal infestation patterns of citrus psyllid Diaphorina citri: implications for managing huanglongbing (HLB) disease in tangerine citrus

Custom-Developed Reflection–Transmission Integrated Vision System for Rapid Detection of Huanglongbing Based on the Features of Blotchy Mottled Texture and Starch Accumulation in Leaves

Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat

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