Efficient multitask learning analyses on grain silo measurement

Abstract

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.