Quantum Harvests: Optimizing Agricultural Yield through QUBO-Driven Crop Selection and Allocation

Abstract

Crop selection and allocation play crucial roles in maximizing agricultural yield and profitability. In this study, we propose a novel approach using a Quadratic Unconstrained Binary Optimization (QUBO) model to optimize crop selection and allocation in agricultural systems. The objective is to determine the optimal combination of crops that maximizes yield or profit while considering the interactions and costs associated with different crops. The QUBO model incorporates negative impacts or costs associated with selecting each crop and positive effects or synergies between different crop combinations. By formulating the problem as a QUBO model, we enable the use of quantum annealing or classical optimization techniques to find the optimal solution. The model's effectiveness is demonstrated through numerical experiments and case studies. Results show that the QUBO-based approach provides significant improvements in crop selection and allocation decisions compared to traditional methods. It takes into account the complex interactions between crops and considers the trade-offs between costs and synergies, resulting in more efficient and profitable agricultural systems. The proposed model offers a flexible and adaptable framework that can accommodate various crop types, growth requirements, market conditions, and constraints. It provides decision-makers in the agricultural sector with a powerful tool to optimize crop selection and allocation, ultimately increasing agricultural yield and profitability while promoting resource sustainability.