Towards Intelligent Food Waste Prevention: An Approach Using Scalable and Flexible Harvest Schedule Optimization With Evolutionary Algorithms

In times of climate change, growing world population, and the resulting scarcity of resources, efficient and economical usage of agricultural land is increasingly important and challenging at the same time. 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 throughout the season is important as it reduces logistical costs and related greenhouse gas emissions, and can also help to reduce food waste. Motivated by the prevention of food waste, this work proposes a flexible optimization method for a full harvest season of large crop ensembles that complies with given economical and environmental constraints. Our approach applies evolutionary algorithms and we further combine our evolution strategy with a sophisticated hierarchical loss function and adaptive mutation rate. We thus transfer the multi-objective into a pseudo-single-objective optimization problem, for which we obtain faster and better solutions than those of conventional approaches.

Automated summary

Efficient and economical usage of agricultural land is increasingly important in the face of climate change and resource scarcity. Intercropping of various plant species is recommended to avoid the disadvantages of monocropping, but it poses challenges due to the need for balanced planting schedules. The proposed flexible optimization method aims to address these challenges by combining evolutionary algorithms with a hierarchical loss function and adaptive mutation rate, leading to faster and better solutions for a sustainable crop harvesting season.