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- W4383670023 abstract "Precision agriculture has recently become the need of the hour due to an unprecedented rate of world population increment. Through fine-tuned crop activities, the crop yield can be significantly increased for a given crop season from the same field. However, these fine-tuned crop activities majorly rely on the information received from the field through various sensing nodes. With proper integration and placement of sensing nodes in crop fields, a large proportion of pre-harvest activities can be dealt in a better manner. As the area of agricultural fields can be quite large, thereby identification of the optimal positioning of sensing nodes can become a cumbersome task. Improper placement of sensing nodes may result in suboptimal usage of available resources. To overcome this issue, the current work investigates the use of metaheuristic optimization algorithms to optimally place the sensing nodes on the field. This is done by minimizing an innovatively designed cost function which is an indication of the un-scrutinized field area and overlapping coverage area of the sensor nodes. Five different nature-inspired optimization algorithms are applied in this paper for the said task, and it was found that Particle Swarm Optimization provided the most optimal sensing node placement in comparison to the Cuckoo search algorithm, Salp swarm algorithm, Water cycle algorithm, and Life choice-based optimization algorithm." @default.
- W4383670023 created "2023-07-09" @default.
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- W4383670023 date "2023-01-01" @default.
- W4383670023 modified "2023-09-25" @default.
- W4383670023 title "Nature-Inspired Optimization Algorithm-Assisted Optimal Sensor Node Positioning for Precision Agriculture Applications in Asymmetric Fields" @default.
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- W4383670023 doi "https://doi.org/10.1007/978-981-99-0483-9_7" @default.
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