摘要
颗粒肥撒施过程本质上是颗粒从撒施装置释放,经空中扩散最终沉积在地面的过程,能够在地面形成多样化的颗粒沉积分布状态。颗粒的沉积分布模式能够反映颗粒在地面的沉积分布状态,广泛应用在撒施机具的校准、性能测试与优化、作业质量评估与颗粒沉积分布预测中,在促进精准施肥作业中发挥积极作用。颗粒沉积分布模式的影响因素复杂,撒施作业平台及其作业参数、颗粒物理特性、作业环境甚至测试与收集方法均会对颗粒的沉积分布数据产生影响。在颗粒肥沉积分布模式的测试方法、数据获取、模式应用和影响因素分析方面已取得的研究进展为撒施机具作业性能优化、田间肥料空间分布特性评估和精准施肥决策提供了重要基础。该研究通过综述现有颗粒沉积分布研究进展,分析了颗粒沉积分布模式的实测方法和预测方法的优劣,指出了现有研究中获得的颗粒沉积分布模式的局限性和关键问题,总结了影响颗粒扩散与沉积过程的各种因素及其对颗粒沉积分布效果的影响,通过梳理现有的颗粒沉积分布模式应用研究,指出了开展不同田间条件下颗粒撒施特性以及颗粒沉积分布模式形成机理的深入研究的重要意义,以期为精准施肥处方决策模型积累基础数据,为实现智能化施肥管理提供科学指导与参考依据,以进一步推动颗粒沉积分布模式在田间精准施肥应用的发展。
Intelligent and precise fertilization has been a promising potential trend in agricultural production in recent years. It is very necessary to master the particle diffusion and deposition distribution under different conditions during the fertilization process. As such, the operating parameters can be adjusted, according to the operating environment and requirements.Therefore, it is also a high demand for accurate fertilization with fixed-point positioning. In the spreading process of granular fertilizer, the particles are firstly released from the spreader, and then deposited on the ground through air diffusion, finally forming a process of diverse particle deposition distribution on the ground. The actual fertilization performance in the field can be evaluated on the instantaneous deposition area formed by particle spreading, according to the particle deposition caused by the overlapping of the operation trajectories. Thus, it is necessary to determine the instantaneous deposition distribution area of the particles, in order to obtain accurate fertilization. The distribution pattern of particle deposition consists of the shape of the deposition area, boundary characteristics, effective width, and the uniformity of spatial distribution. The instantaneous deposition distribution state of particles on the ground can also be obtained to provide a strong reference for the calibration,performance testing, and structure optimization of spreaders. A field operation quality can be assessed to predict the particle deposition distribution during precise fertilization. This review aims to summarize the research progress of depositional distribution patterns from the aspects of formation mechanism, acquisition, characteristic analysis, and influencing factors. The deposition distribution pattern was also clarified in the performance optimization of spreaders, the evaluation and prediction of field fertilization particle deposition. The achievements of previous research were analyzed to locate the new challenge in recent years. Some suggestions were also proposed for the future development direction. Among them, the experimental test and trajectory prediction were mainly utilized to acquire the deposition distribution pattern. The experiment included the ground fixed-point, single-line, and multi-line superposition tests. The deposition distribution state of particles on the ground was directly obtained using these tests. The trajectory prediction included the particle trajectory model, simulation, and image recognition. The trajectory of a single particle was used to calculate the landing position of the particle, and then to accumulate the deposition range of the particle group and the deposition amount at different locations. The experimental test was often time-consuming and labor-intensive, especially for a large workload during the multi-route test on the ground. But the real depositional distribution data was collected in this case. By contrast, the trajectory prediction was difficult to directly apply for the field fertilization, although there was a small workload and the depositional distribution data in an ideal environment. Two types of methods can be combined to verify each other in practical research. In terms of the influencing factors of the particle deposition distribution pattern, the structure of the spreading operation platform and the operating parameters, particle physical properties, operating environment, and even the testing and collection methods all posed an impact on the particle deposition distribution data pattern. In general, the main influencing factors were analyzed, while less considering the influence of multi-factor interaction and the optimal suitable range of operating parameters. The differences were often ignored between the test and the field environment of particle deposition distribution. Therefore, it is urgent to clarify the distribution characteristics of particle deposition under the interaction of multiple factors, and further determine the relationship with the influencing factors of different applicators. As such, reliable guidance can be gained for structure optimization and field fertilization operations. Since the particle deposition in the field fertilization belonged to the spatial distribution, it is not enough to evaluate the particle deposition only from the uniformity and width of a single route. The uneven fertilization often occurred in the overlapping areas between routes. The amount of particle deposition in the overlapping areas was an important factor affecting the overall uniformity of deposition. The consistency of particle deposition in the flight direction can also indirectly reflect the stability of particle applicators, which can be used as indicators to evaluate the performance of field fertilization. Furthermore,the deposition database can be established in a complex environment using big data and artificial intelligence. A multi-factor fusion prediction model of particle deposition distribution can greatly contribute to the intelligent decision-making for the operation parameters before the actual operation, in order to improve the quality of precise work in smart agriculture.
作者
宋灿灿
王国宾
赵静
王家辉
闫瑜
王猛
周志艳
兰玉彬
Song Cancan;Wang Guobin;Zhao Jing;Wang Jiahui;Yan Yu;Wang Meng;Zhou Zhiyan;Lan Yubin(College of Agricultural Engineering and Food Science,Shandong University of Technology,Zibo 255049,China;Shandong Provincial Engineering Technology Research Center for Agricultural Aviation Intelligent Equipment,Zibo 255049,China;Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology,Zibo 255049,China;College of Engineering,South China Agricultural University and Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence(GDKL-AAI),Guangzhou 510642,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第14期59-70,共12页
Transactions of the Chinese Society of Agricultural Engineering
基金
山东省自然科学基金项目(ZR2021QC154)
山东省引进顶尖人才“一事一议”专项经费资助项目(鲁政办字[2018]27号)
淄博市重点研发计划(校城融合类)生态无人农场研究院项目(2019ZBXC200)
广东省乡村振兴战略专项(2020KJ261)。
关键词
施肥机械
精准农业
颗粒肥料
沉积分布模式
模型预测
分布均匀性
fertilizer spreader
precision agriculture
granular fertilizer
deposition distribution pattern
model prediction
distribution uniformity
