School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing, China.
The National Engineering Laboratory of Grain Storage and Logistics, Academy of National Food and Strategic Reserves Administration, Academy of National Food and Strategic Reserves Administration, Beijing, China.
J Sci Food Agric. 2023 Oct;103(13):6553-6565. doi: 10.1002/jsfa.12735. Epub 2023 Jun 14.
Post-harvest quality assurance is a crucial link between grain production and end users. It is essential to ensure that grain does not deteriorate due to heating during storage. To visualize the temperature distribution of a grain pile, the present study proposed a three-dimensional (3D) temperature field visualization method based on an adaptive neighborhood clustering algorithm (ANCA). The ANCA-based visualization method contains four calculation modules. First, discrete grain temperature data, obtained by sensors, are collected and interpolated using back propagation (BP) neural networks to model the temperature field. Then a new adaptive neighborhood clustering algorithm is applied to divide interpolation data into different categories by combining spatial characteristics and spatiotemporal information. Next, the Quickhull algorithm is used to compute the boundary points of each cluster. Finally, the polyhedrons determined by boundary points are rendered into different colors and are constructed in a 3D temperature model of the grain pile.
The experimental results show that ANCA is much better than the DBSCAN and MeanShift algorithms on compactness (around 95.7% of tested cases) and separation (approximately 91.3% of tested cases). Moreover, the ANCA-based visualization method for grain pile temperatures has a shorter rendering time and better visual effects.
This research provides an efficient 3D visualization method that allows managers of grain depots to obtain temperature field information for bulk grain visually in real time to help them protect grain quality during storage. © 2023 Society of Chemical Industry.
收获后质量保证是粮食生产和最终用户之间的关键环节。必须确保粮食在储存过程中不会因发热而变质。为了可视化粮堆的温度分布，本研究提出了一种基于自适应邻域聚类算法（ANCA）的三维（3D）温度场可视化方法。基于 ANCA 的可视化方法包含四个计算模块。首先，通过传感器收集离散的粮温数据，并使用反向传播（BP）神经网络进行插值，以模拟温度场。然后，应用新的自适应邻域聚类算法，结合空间特征和时空信息，将插值数据分为不同类别。接下来，使用 Quickhull 算法计算每个聚类的边界点。最后，通过边界点确定的多面体被渲染成不同的颜色，并在粮堆的 3D 温度模型中构建。
实验结果表明，ANCA 在紧凑性（测试案例的约 95.7%）和分离性（测试案例的约 91.3%）方面明显优于 DBSCAN 和 MeanShift 算法。此外，粮堆温度的基于 ANCA 的可视化方法具有更短的渲染时间和更好的视觉效果。
本研究提供了一种高效的 3D 可视化方法，使粮库管理人员能够实时直观地获得散装粮的温度场信息，有助于在储存过程中保护粮食质量。© 2023 化学工业协会。
