Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of... more
Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. There have been several attempts to perform automatic background identification. This paper reviews state-of-the-art automatic bacteria identification techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria identification systems and recommends future direction of automatic bacteria identification.
Rice is a staple food to almost one-third population of the world. The crop sustainability must therefore be considered to keep pace with the growing world population. Microorganims are one of the key factors affecting crop productivity... more
Rice is a staple food to almost one-third population of the world. The crop sustainability must therefore be considered to keep pace with the growing world population. Microorganims are one of the key factors affecting crop productivity and quality. Of particular interest is the plant rhiszosphere microbiome which has gained enormous attention in recent years due to their role both as pathogen and plant growth promoting agents. Accordingly, the present study is aimed at identification of bacteria isolated from soil samples collected from rice fields of district Jaffarabad, which belongs to the major rice growing area of Pakistan. Six strains B9, B15, B34, B40, B52 and B53 were identified as genus Bacillus whereas isolate B12 as genus Staphylococcus based on morphological and biochemical properties and 16S rRNA gene sequence analysis. Isolates B9, B40 and B53 showed 99% similarity to Bacillus thuringiensis, Bacillus anthracis and Bacillus endophyticus respectively. Conflicting results were observed for B15, B34 and B52 for sequence analysis based on BLAST and SeqMatch alignment. The isolate B12 showed 99% similarity to Staphylococcus aureus. Our findings indicate that instead of resorting rice bacterial populations through biochemical tests, the validation through 16S rRNA technique provides a more objective view.
Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of... more
Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. There have been several attempts to perform automatic background identification. This paper reviews state-of-the-art automatic bacteria identification techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria identification systems and recommends future direction of automatic bacteria identification.
Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of... more
Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification framework that can classify three famous classes of bacteria namely Cocci, Bacilli and Vibrio from microscopic morphology using the Naïve Bayes classifier. The proposed bacteria identification framework comprises two steps. In the first step, the system is trained using a set of microscopic images containing Cocci, Bacilli, and Vibrio. The input images are normalized to emphasize the diameter and shape features. Edge-based descriptors are then extracted from the input images. In the second step, we use the Naïve Bayes classifier to perform probabilistic inference based on the input descriptors. 64 images for each class of bacteria were used as the training set and 222 images consisting of the three classes of bacteria and other random images such as humans and airplanes were used as the test set. There are no images overlapped between the training set and the test set. The system was found to be able to accurately discriminate the three classes of bacteria. Moreover, the system was also found to be able to reject images that did not belong to any of the three classes of bacteria. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our two-step automatic bacteria identification approach and motivate us to extend this framework to identify a variety of other types of bacteria.
