Agbots 3.0: Adaptive Weed Growth Prediction for Mechanical Weeding Agbots
نویسندگان
چکیده
This work presents advances in predictive modeling of weed growth, as well an improved planning index to be used conjunction with these techniques, for the purpose improving performance coordinated weeding algorithms being developed industrial agriculture. We demonstrate that evolving Gaussian process (E-GP) method applied measurements from agents can predict evolution field within realistic simulation environment, Weed World. also provides physical insight into seed bank distribution field. In this work, we extend E-GP model two important ways. First, have a has bias term, and show how it is connected distribution. Second, one may decouple component representing growth component, which varies distribution, adapt latter online. compare approach relies on known properties drive down total biomass fields high densities using less agents, without assuming information. use index, Whittle allows balanced tradeoff between exploiting row or allowing accrue reward conforms what theoretical limit fewest number domain.
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ژورنال
عنوان ژورنال: IEEE Transactions on Robotics
سال: 2022
ISSN: ['1552-3098', '1941-0468', '1546-1904']
DOI: https://doi.org/10.1109/tro.2021.3083204