Determining the amount of grain stored in silos is very important for accurate commercial inventory planning. A convolutional neural network (CNN) is developed for the first time to determine the amount of the grain using step-frequency continuous wave radar (SFCWR) signals. The radar reflection signal of different grain quantity for different grain surface patterns is gathered by means of a constructed experimental setup. 5681 measurements are performed in the scaled model silo containing different weights (0 to 20 kg) grain stacked as different surface patterns. The dataset is then created using the spectrograms of SFCWR signals. While 1420 data randomly selected from the dataset are used for testing, the remaining 4261 data are used for training. The results are then compared with the pretrained CNNs, demonstrating the superiority of the proposed method. The accuracy of the methods is given with metric parameters for both classification and regression. The proposed multitask CNN model obtained higher performance with 0.2865 MAE, 0.5053 MAPE, 0.8047 MSE, and 0.8971 RMSE for regression task and 99.23% accuracy, 99.09% sensitivity, 99.52% specitivity, 99.42% precision, 99.25% F1-score, and 98.83% MCC for classification. These metric performances are better than the previous study with 3.29 MAPE in the literature. The results obtained reveal that, with proper modeling and successful training, CNNs can be effectively used for the quantity measurement applications of the grain stacks.

中文翻译：

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粮仓测量的高效多任务学习分析

确定储存在筒仓中的谷物数量对于准确的商业库存计划非常重要。首次开发了卷积神经网络 (CNN) 以使用步进频率连续波雷达 (SFCWR) 信号确定谷物数量。通过搭建的实验装置采集不同颗粒表面图案的不同颗粒数量的雷达反射信号。5681 次测量是在包含不同重量（0 至 20 公斤）谷物堆叠成不同表面图案的比例模型筒仓中进行的。然后使用 SFCWR 信号的频谱图创建数据集。从数据集中随机选择的 1420 个数据用于测试，其余 4261 个数据用于训练。然后将结果与预训练的 CNN 进行比较，证明了所提出方法的优越性。这些方法的准确性由分类和回归的度量参数给出。所提出的多任务 CNN 模型获得了更高的性能，回归任务的 MAE 为 0.2865、0.5053 MAPE、0.8047 MSE 和 0.8971 RMSE，准确度为 99.23%，灵敏度为 99.09%，特异性为 99.52%，99.42% 为 99.42%，core 为 93-82%，准确率为 93-82%。用于分类的 MCC。这些指标性能优于之前文献中 3.29 MAPE 的研究。获得的结果表明，通过适当的建模和成功的训练，CNN 可以有效地用于谷物堆的数量测量应用。所提出的多任务 CNN 模型获得了更高的性能，回归任务的 MAE 为 0.2865、0.5053 MAPE、0.8047 MSE 和 0.8971 RMSE，准确度为 99.23%，灵敏度为 99.09%，特异性为 99.52%，99.42% 为 99.42%，core 为 93-82%，准确率为 93-82%。用于分类的 MCC。这些指标性能优于之前文献中 3.29 MAPE 的研究。获得的结果表明，通过适当的建模和成功的训练，CNN 可以有效地用于谷物堆的数量测量应用。所提出的多任务 CNN 模型获得了更高的性能，回归任务的 MAE 为 0.2865、0.5053 MAPE、0.8047 MSE 和 0.8971 RMSE，准确度为 99.23%，灵敏度为 99.09%，特异性为 99.52%，99.42% 为 99.42%，core 为 93-82%，准确率为 93-82%。用于分类的 MCC。这些指标性能优于之前文献中 3.29 MAPE 的研究。获得的结果表明，通过适当的建模和成功的训练，CNN 可以有效地用于谷物堆的数量测量应用。