2021/12/20 by Maurice Günder, Nico Piatkowski, Günder, Maurice +7
Computer Science · #Computational Engineering #Computers and Society (cs.CY) #FOS: Computer and information sciences #Finance #and Science (cs.CE) #cs.CE #cs.CY
paper · pdf · doi:10.48550/arxiv.2112.10712
4 pages, AAAI-2022 Workshop AI for Agriculture and Food Systems (AIAFS)
arxiv created 2021/12/20 · arxiv updated 2021/12/21
In order to avoid disadvantages of monocropping for soil and environment, it is advisable to practice intercropping of various plant species whenever possible. However, intercropping is challenging as it requires a balanced planting schedule due to individual cultivation time frames. Maintaining a continuous harvest reduces logistical costs and related greenhouse gas emissions, and contributes to food waste prevention. In this work, we address these issues and propose an optimization method for a full harvest season of large crop ensembles that complies with given constraints. By using an approach based on an evolutionary algorithm combined with a novel hierarchical loss function and adaptive mutation rate, we transfer the multi-objective into a pseudo-single-objective optimization problem and obtain faster convergence and better solutions than for conventional approaches.