Plant diseases are accountable for economic losses in an agricultural country. The manual process of plant diseases diagnosis is a key challenge from last one decade; therefore, researchers in this area introduced automated systems. In this research work, automated system is proposed for citrus fruit diseases recognition using computer vision technique. The proposed method incorporates five fundamental steps such as preprocessing, disease segmentation, feature extraction and reduction, fusion, and classification. The noise is being removed followed by a contrast stretching procedure in the very first phase. Later, watershed method is applied to excerpt the infectious regions. The shape, texture, and color features are subsequently computed from these infection regions. In the fourth step, reduced features are fused using serial-based approach followed by a final step of classification using multiclass support vector machine. For dimensionality reduction, principal component analysis is utilized, which is a statistical procedure that enforces an orthogonal transformation on a set of observations. Three different image data sets (Citrus Image Gallery, Plant Village, and self-collected) are combined in this research to achieving a classification accuracy of 95.5%. From the stats, it is quite clear that our proposed method outperforms several existing methods with greater precision and accuracy.
Misidentifications of Burkholderia pseudomallei as Burkholderia cepacia by Vitek 2 have occurred. Multidimensional scaling ordination of biochemical profiles of 217 Malaysian and Australian B. pseudomallei isolates found clustering of misidentified B. pseudomallei isolates from Malaysian Borneo. Specificity of B. pseudomallei identification in Vitek 2 and potentially other automated identification systems is regionally dependent.
Treponema pallidum infections can have severe complications if not diagnosed and treated at an early stage. Screening and diagnosis of syphilis require assays with high specificity and sensitivity. The Elecsys Syphilis assay is an automated treponemal immunoassay for the detection of antibodies against T. pallidum The performance of this assay was investigated previously in a multicenter study. The current study expands on that evaluation in a variety of diagnostic settings and patient populations, at seven independent laboratories. The samples included routine diagnostic samples, blood donation samples, samples from patients with confirmed HIV infections, samples from living organ or bone marrow donors, and banked samples, including samples previously confirmed as syphilis positive. This study also investigated the seroconversion sensitivity of the assay. With a total of 1,965 syphilis-negative routine diagnostic samples and 5,792 syphilis-negative samples collected from blood donations, the Elecsys Syphilis assay had specificity values of 99.85% and 99.86%, respectively. With 333 samples previously identified as syphilis positive, the sensitivity was 100% regardless of disease stage. The assay also showed 100% sensitivity and specificity with samples from 69 patients coinfected with HIV. The Elecsys Syphilis assay detected infection in the same bleed or earlier, compared with comparator assays, in a set of sequential samples from a patient with primary syphilis. In archived serial blood samples collected from 14 patients with direct diagnoses of primary syphilis, the Elecsys Syphilis assay detected T. pallidum antibodies for 3 patients for whom antibodies were not detected with the Architect Syphilis TP assay, indicating a trend for earlier detection of infection, which may have the potential to shorten the time between infection and reactive screening test results.