Yang Manxian, Chen Yanhong, Li Yongke, Zhang Taihong, Wu Tianlun
College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi, P.R. China.
Engineering Research Center of Intelligent Agriculture Ministry of Education, Urumqi, P.R. China.
PLoS One. 2025 Jan 7;20(1):e0315670. doi: 10.1371/journal.pone.0315670. eCollection 2025.
Automated large-scale farmland preparation operations face significant challenges related to path planning efficiency and uniformity in resource allocation. To improve agricultural production efficiency and reduce operational costs, an enhanced method for planning land preparation paths is proposed. In the initial stage, unmanned aerial vehicles (UAVs) are employed to collect data from the field, which is then used to construct accurate farm models. For single-field operations, a path planning approach is developed that minimizes energy consumption. The approach combines the selection of optimal operational angles with the implementation of efficient turning strategies, aiming to achieve full coverage. In addressing the issue of scheduling multiple machines across multiple fields, a two-stage optimization method, referred to as the BNSGA-III algorithm, is introduced. This algorithm integrates the NSGA-III algorithm with Beluga Whale Optimization (BWO), adaptive parameter adjustment, and Adaptive Inversion Crossover (AIC). The proposed method tackles the inherent complexity of agricultural environments, balancing operational efficiency and resource allocation through multi-objective optimization. Experimental results demonstrate that, compared to random operation directions, the proposed method reduces the path length by 1.9% to 3.1%, decreases the turning frequency by 19.5% to 24.0%, and improves coverage by 1.0% to 1.4%. In the context of multi-machine scheduling, the BNSGA-III algorithm outperforms the NSGA-II, NSGA-III, and MOEA/D algorithms, achieving improvements in total travel distance (12.3% to 34.4%), path balance (60.9% to 66.2%), and workload distribution (78.7% to 92.9%). Further evaluation shows that BNSGA-III excels in key metrics such as convergence (IGD), solution quality (HV), and diversity (Spread), thereby confirming its superiority in solution quality, convergence, and diversity. The findings of this study provide strong support for the advancement of intelligent agriculture.
自动化大规模农田整地作业在路径规划效率和资源分配均匀性方面面临重大挑战。为提高农业生产效率并降低运营成本，提出了一种改进的土地整备路径规划方法。在初始阶段，使用无人机从田间收集数据，然后用于构建精确的农田模型。对于单块农田作业，开发了一种使能耗最小化的路径规划方法。该方法将最佳作业角度的选择与高效转向策略的实施相结合，旨在实现全面覆盖。在解决多台机器跨多块农田的调度问题时，引入了一种两阶段优化方法，即BNSGA-III算法。该算法将NSGA-III算法与白鲸优化（BWO）、自适应参数调整和自适应逆交叉（AIC）相结合。所提出的方法应对了农业环境的固有复杂性，通过多目标优化平衡了作业效率和资源分配。实验结果表明，与随机作业方向相比，所提出的方法使路径长度减少了1.9%至3.1%，转向频率降低了19.5%至24.0%，覆盖率提高了1.0%至1.4%。在多机调度的情况下，BNSGA-III算法优于NSGA-II、NSGA-III和MOEA/D算法，在总行驶距离（提高12.3%至34.4%）、路径平衡（提高60.9%至66.2%）和工作量分配（提高78.7%至92.9%）方面取得了改进。进一步评估表明，BNSGA-III在收敛性（IGD）、解质量（HV）和多样性（Spread）等关键指标方面表现出色，从而证实了其在解质量、收敛性和多样性方面的优越性。本研究结果为智能农业的发展提供了有力支持。
