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Training‐induced neuroplasticity has been described in athletes' population. However, it remains largely unknown how regular training and sports proficiency modifies neuronal circuits in the human brain. In this study, we used voxel‐based morphometry and stepwise functional connectivity (SFC) analyses to uncover connectivity changes in the functional stream architecture in student‐athletes at early stages of sensorimotor skill training. Thirty‐two second‐year student‐athletes whose major was little‐ball sports and thirty‐four nonathlete controls were recruited for the study. We found that athletes showed greater gray matter volume in the right sensorimotor area, the limbic lobe, and the anterior lobe of the cerebellum. Furthermore, SFC analysis demonstrated that athletes displayed significantly smaller optimal connectivity distance from those seed regions to the dorsal attention network (DAN) and larger optimal connectivity distance to the default mode network (DMN) compared to controls. The Attention Network Test showed that the reaction time of the orienting attention subnetwork was positively correlated with SFC between the seeds and the DAN, while negatively correlated with SFC between the seeds and the DMN. Our findings suggest that neuroplastic adaptations on functional connectivity streams after motor skill training may enable novel information flow from specific areas of the cortex toward distributed networks such as the DAN and the DMN. This could potentially regulate the focus of external and internal attention synchronously in athletes, and consequently accelerate the reaction time of orienting attention in athletes.

A B S T R A C TPlants are unavoidable for the existence of most living things on this planet. There are many needs of both humans and animals that are satisfied by materials from plants. These needs include food, shelter, and medicine. The ability to identify plants is highly important in several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds. This paper reviews several applications and works that have been made towards computer-based vision systems for automatic identification of plant species. It shows the various techniques alongside their descriptions. It portrays how future researchers in this field (especially precision agriculture or agricultural informatics) may move the knowledge domain forward.