Plant growth promoting (PGP) bacteria are known to enhance plant growth and protect them from environmental stresses through different pathways. The rhizosphere of perennial plants, including olive, may represent a relevant reservoir of PGP bacteria. Here, seven bacterial strains isolated from olive rhizosphere have been characterized taxonomically by 16S sequencing and biochemically, to evaluate their PGP potential. Most strains were identified as Pseudomonas or Bacillus spp., while the most promising ones belonged to genera Pseudomonas and Curtobacterium. Those strains have been tested for their capacity to grow under osmotic or salinity stress and to improve the germination and early development of Triticum durum subjected or not to those stresses. The selected strains had the ability to grow under severe stress, and a positive effect has been observed in non-stressed seedlings inoculated with one of the Pseudomonas strains, which showed promising characteristics that should be further evaluated. The biochemical and taxonomical characterization of bacterial strains isolated from different niches and the evaluation of their interaction with plants under varying conditions will help to increase our knowledge on PGP microorganisms and their use in agriculture.

The present study emphasizes the efficacy of a biosurfactant-producing bacterial strain Klebsiella sp. KOD36 in biodegradation of azo dyes and hexavalent chromium individually and in a simultaneous system. The bacterial strain has exhibited a considerable potential for biodegradation of chromium and azo dyes in single and combination systems (maximum 97%, 94% in an individual and combined system, respectively). Simultaneous aerobic biodegradation of azo dyes and hexavalent chromium (SBAHC) was modeled using machine learning programming, which includes gene expression programming, random forest, support vector regression, and support vector regression-fruit fly optimization algorithm. The correlation coefficient includes the dispersion index, and the Willmott agreement index was employed as statistical metrics to assess the performance of each model separately. In addition, the Taylor diagram was used to further investigate the methods used. The findings of the present study were that the support vector regression-fruitfly optimization algorithm (SVR-FOA) with correlation coefficient (CC) of 0.644, (scattered index) SI of 0.374, and (Willmott’s index of agreement) WI of 0.607 performed better than the autonomous support vector regression (SVR), gene expression programming (GEP), and random forest (RF) methods. In addition, the standalone SVR model with CC of 0.146, SI of 0.473, and WI of 0.408 ranked the second best. In summary, the SBAHC can be accurately estimated using the hybrid SVR-FOA method. In other words, FOA has proven to be a powerful optimization algorithm for increasing the accuracy of the SVR method.