Abstract
This work presents advances in predictive modeling of weed growth, as well as an improved planning index to be used in conjunction with these techniques, for the purpose of improving the performance of coordinated weeding algorithms being developed for industrial agriculture. We demonstrate that the evolving Gaussian process (E-GP) method applied to measurements from the agents can predict the evolution of the field within the realistic simulation environment, Weed World. This method also provides physical insight into the seed bank distribution of the field. In this work, we extend the E-GP model in two important ways. First, we have developed a model that has a bias term, and we show how it is connected to the seed bank distribution. Second, we show that one may decouple the component of the model representing weed growth from the component, which varies with the seed bank distribution, and adapt the latter online. We compare this predictive approach with one that relies on known properties of the weed growth model and show that the E-GP method can drive down the total weed biomass for fields with high seed bank densities using less agents, without assuming this model information. We use an improved planning index, the Whittle index, which allows a balanced tradeoff between exploiting a row or allowing it to accrue reward and conforms to what we show is the theoretical limit for the fewest number of agents, which can be used in this domain.
Highlights
C ONSIDER a team of mechanical weeding robots managing herbicide-resistant weeds on any row-crop farm in the U.S This team of robots needs to predict the weed growth across the whole farm in order to make intelligent decisions on robot coordination [1]
To benchmark the performance of the evolving Gaussian process (E-Gaussian process (GP)) method, we developed another prediction scheme, Gaussian seed bank (G-SB), which uses a single timeinvariant GP in conjunction with statistics about weed growth to make weed density predictions based directly on an online estimate of the underlying seed bank density
In [2], we showed that the kernel matrix of the E-GP for a given set of measurements, represented by M, is just the observation matrix of the Kalman filter, H
Summary
C ONSIDER a team of mechanical weeding robots managing herbicide-resistant weeds on any row-crop farm in the U.S This team of robots needs to predict the weed growth across the whole farm in order to make intelligent decisions on robot coordination [1].
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