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Modern horticultural crop production systems target the exploitation of sustainable techniques for crop improvement while maintaining balance with the environment. Several beneficial microbes have been investigated and have yielded remarkable results. The endophytic fungus, Piriformospora indica, is one microbe group with the potential to offer numerous benefits and opportunities in modern horticultural crop production. This plant growth-promoting mycorrhizal fungus is of particular interest due to its beneficial implications in plant growth and development. The fungus has been experimentally proven to significantly improve water and nutrient/mineral absorption, early flowering, seed production/germination and plant photosynthetic capability, growth rates; especially in nutrient-deprived soils, alter the production of secondary metabolites, and promote adaptation, tolerance and/or resistance to biotic and abiotic stressors. Its unique capability to be axenically cultured makes it feasible for research and application. These attributes in combinations with its broad host range, offer immense potential for this fungus for research in horticultural plant improvement. Thus, this review highlights the potential applications of P. indica in horticultural crops research and production. Keywords: Piriformospora indica, root endophyte, root colonization, stress response, plant growth and development

The dynamic foreground target analysis of surveillance video is an important basis for security construction in safe cities and smart campuses. The dynamic attributes can be assigned to static geographic data by integrating surveillance video with geospatial data. For the integration of traditional surveillance video and geographic information data, only video data is projected into geospatial, which causes storage issue and difficult in understanding video content. The model that integrates geographic information and foreground dynamic targets, and integrates surveillance video with geographic information is proposed in this paper. And the dynamic foreground target and the tracking trajectory in the image are projected into the geospatial by the derived mapping model. The multi-layer fusion mode is utilized to distribute the video dynamic summary content in geospatial space.

Serendipita indica is a root-colonizing basidiomycete that has received considerable attention in recent decades on account of its wide host range and the diverse benefits it imparts to host plants, including banana (Musa spp.). Banana is globally threatened by the fungal pathogen Fusarium oxysporum f. sp. cubense (Foc). In this study, to examine whether S. indica can improve the Foc resistance of banana, S. indica colonized (S+) and non-colonized (CK) ‘Tianbaojiao’ banana plants were inoculated with Foc tropical race 4 (TR4). Chlorophyll fluorescence parameters in leaves and antioxidant enzyme activities in roots of S+, CK, FocTR4-inoculated S+ plants (SF), and FocTR4-inoculated CK plants (F+) were determined and symptom appearance in corms of SF and F+ plants was observed. An analysis of antagonistic activity between S. indica and FocTR4 was performed and results showed that no significant differences in chlorophyll fluorescence parameters were displayed between CK and S+. No significant differences in Y(II) (effective quantum yield of photochemical energy in PSII) and Y(NPQ) (quantum yield of regulated energy dissipation in PSII) were exhibited among the four groups. However, the Fv/fm (ratio of variable to maximal fluorescence) values were significantly decreased (p

In dynamic environments,the moving landmarks can make the accuracy of traditional vision-based localization worse or even failure.To solve this problem,a robust Gaussian mixture model for vision-based localization with dynamic landmarks is proposed.The motion index is added to the traditional graph-based vision-based localization model to describe landmarks' moving probability,changing the classic Gaussian model to Gaussian mixture model,which can reduce the influence of moving landmarks for optimization results.To improve the algorithm's robustness to noise,the covariance inflation model is employed in residual equations.The expectation maximization method for solving the Gaussian mixture problem is derived in detail,transforming the problem into classic iterative least square problem.Experimental results demonstrate that in dynamic environments,the proposed algorithm outperforms the traditional method both in absolute accuracy and relative accuracy,while maintains high accuracy in static environments.The proposed method can effectively reduce the influence of the moving landmarks in dynamic environments,which is more suitable for the autonomous localization of mobile robots.

With the shortcomings of traditional algorithm in video surveillance on low accuracy, poor robustness and unable achieved real-time tracking for multi-targets, this paper presents a Multi-target tracking algorithm, DeepSort, on the base of deep neural network to achieve the end-to-end surveillance video multi-personal target real-time detection and tracking. The high accuracy of target detection by YOLO algorithm provides DeepSort with weaker dependence on detection results, lower interference of occlusion and illumination and improved tracking robustness. Moreover, due to the high redundancy of the surveillance video itself, the difference filter is used to screen the video frames with no foreground targets and small changes, so as to reduce the detection cost and improve the detection and tracking speed. The experimental evaluation of the video surveillance dataset NPLR, the average MOTA of this algorithm is 68.7, the highest value is 86.8; the average speed is 81.6Hz, the highest value is 140Hz. It shows that the end-to-end algorithm is feasible and effective.

To solve the problem of traditional Pyramid LK optical flow algorithm's poor accuracy and adaptability for rotor UAV to detect obstacle in complex outdoor environment, a monocular autonomous real-time obstacle detection method based on radial optical flow is proposed. In the optical flow, the radial optical flow is computed by fusing Pyramid LK optical flow with tangential optical flow, and a new obstacles decision strategy to detect obstacles based on the radial optical flow is put forward. Experimental results show that without increasing the complexity of algorithm, the proposed method can get a higher accuracy and better adaptability than traditional Pyramid LK algorithm, which can meet the requirements of UAV autonomous obstacle avoidance.