Detection and distinguishing between different species of bacteria using experimental microbiology is an expensive, time-consuming, and risky process. Automatic computer-based methods for accurate detection and classification of bacteria species significantly reduce the cost, time, and avoiding scientists the risk of infection. This paper presents a novel computer-based approach for highly accurate recognition of bacterial species. The proposed method consists of two main stages. First, a novel set of fractional-order orthogonal moments proposed to extract the fine features from the color images of bacteria. Second, a new method for feature selection, SSATLBO, is proposed. In this method, the teaching-based learning optimization (TLBO) as local operators is used to improve the exploitation ability of the Salp Swarm Algorithm (SSA) to avoid the local point. The proposed detection and classification method tested by using the DIBaS dataset (Digital Image of Bacterial Species), which includes 660 images with 33 various genera and classes of bacteria. The proposed method achieved a bacterial species recognition rate, 98.68%. The obtained results ensure the superiority of the proposed method over the traditional SSA and TLBO methods and the other Metaheuristic methods.

使用实验微生物学检测和区分不同种类的细菌是一个昂贵，费时且有风险的过程。基于计算机的自动方法可以准确地检测和分类细菌，从而大大降低了成本，缩短了时间，并避免了科学家感染细菌的风险。本文提出了一种新型的基于计算机的方法，可以高度准确地识别细菌。所提出的方法包括两个主要阶段。首先，提出了一组新颖的分数阶正交矩，以从细菌的彩色图像中提取精细特征。其次，提出了一种新的特征选择方法SSATLBO。用这种方法 基于教学的学习优化（TLBO）作为本地算子被用来提高Salp Swarm算法（SSA）的开发能力来避开本地点。该建议的检测和分类方法通过使用DIBaS数据集（细菌物种的数字图像）进行了测试，该数据集包含660种包含33个不同属和细菌类的图像。所提出的方法实现了细菌种类识别率98.68％。所获得的结果确保了所提出的方法优于传统的SSA和TLBO方法以及其他元启发式方法。98.68％。所获得的结果确保了所提出的方法优于传统的SSA和TLBO方法以及其他元启发式方法。98.68％。所获得的结果确保了所提出的方法优于传统的SSA和TLBO方法以及其他元启发式方法。