Song Cancan, Wang Guobin, Zhao Jing, Wang Jiahui, Yan Yu, Wang Meng, Zhou Zhiyan, Lan Yubin. Research progress on the particle deposition and distribution characteristics of granular fertilizer application[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(14): 59-70. DOI: 10.11975/j.issn.1002-6819.2022.14.008
    Citation: Song Cancan, Wang Guobin, Zhao Jing, Wang Jiahui, Yan Yu, Wang Meng, Zhou Zhiyan, Lan Yubin. Research progress on the particle deposition and distribution characteristics of granular fertilizer application[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(14): 59-70. DOI: 10.11975/j.issn.1002-6819.2022.14.008

    Research progress on the particle deposition and distribution characteristics of granular fertilizer application

    • Abstract: 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.
    • loading

    Catalog

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return