Multi-sensor data fusion in the nondestructive measurement of kiwifruit texture

Abstract Three mechanical-based techniques, including falling impact (FAI), forced impact (FOI), and acoustic impulse-response (AIR), were implemented for the nondestructive prediction of the apparent modulus of elasticity (E a ) and Magness-Taylor firmness (MTf) of kiwifruit, cv. Hayward . Considering the merits and limitations of each method in estimating the texture parameters, this study tried to improve the performance of the predictive models by using the concept of multi-sensor data fusion. Two different fusion strategies, including low-level and mid-level fusions, were accordingly applied using partial least square regression (PLSR) and principal component analysis combined with artificial neural network (PCA-ANN), respectively. Better predictions of E a were obtained, as compared to those of MTf, in using each method, as well as their combinations in both fusion strategies, thereby demonstrating the better fitness of E a with the nondestructive data. Moreover, both fusion strategies enhanced the performance of E a and, in most cases, MTf predictive models, as compared with using each technique individually. Comparing two fused systems showed that the mid-level fusion was more effective than the low-level one, where in the best fused systems (integration of all three sensors), the standard deviation ratio (SDR) values for E a and MTf were improved by 11.2% and 9.1%, respectively, and the satisfactory results for both E a ( R 2 p  = 0.926, SDR = 3.07) and MTf ( R 2 p  = 0.841, SDR = 2.51) were obtained. This study revealed that compared to the implementation of mechanical-based methods individually, and also the low-level fusion of them, their mid-level fusion using PCA-ANN algorithm could be an effective approach for providing more detailed and complementary information about kiwifruit texture.