In sustainable agriculture, intercropping systems represent a valuable approach. These systems involve placing mutually beneficial plant types in close proximity to each other, with the goal of exploiting biodiversity to reduce pesticide and water usage, as well as improve soil nutrient utilization. Despite its potential, the optimization of intercropping systems has received limited attention in previous studies. One of the first steps in the design of an intercropping system is the solution of the crop planting layout problem, which involves meeting crop demand while maximizing positive interactions between adjacent plants. We perform a complexity analysis of this problem and solve it through constraint programming, an artificial intelligence technique, which relies on automated reasoning, constraint propagation and search heuristics. To this aim, we present two constraint programming models based on integer variables and interval variables, respectively. Through a computational study on real-life instances, we examine the impact of different modelling approaches on the difficulty of solving the crop planting layout problem with standard constraint programming solvers. This research work has also provided the groundwork for a sowing robotic arm (under development), aiming to automate intercropping systems and assist farm workers.
Crop planting layout optimization in sustainable agriculture: A constraint programming approach
Colizzi L.
;Dimauro G.;
2024-01-01
Abstract
In sustainable agriculture, intercropping systems represent a valuable approach. These systems involve placing mutually beneficial plant types in close proximity to each other, with the goal of exploiting biodiversity to reduce pesticide and water usage, as well as improve soil nutrient utilization. Despite its potential, the optimization of intercropping systems has received limited attention in previous studies. One of the first steps in the design of an intercropping system is the solution of the crop planting layout problem, which involves meeting crop demand while maximizing positive interactions between adjacent plants. We perform a complexity analysis of this problem and solve it through constraint programming, an artificial intelligence technique, which relies on automated reasoning, constraint propagation and search heuristics. To this aim, we present two constraint programming models based on integer variables and interval variables, respectively. Through a computational study on real-life instances, we examine the impact of different modelling approaches on the difficulty of solving the crop planting layout problem with standard constraint programming solvers. This research work has also provided the groundwork for a sowing robotic arm (under development), aiming to automate intercropping systems and assist farm workers.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
