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Multi-sensor data fusion for improved prediction of apple fruit firmness and soluble solids content

机译：多传感器数据融合可改善对苹果果实硬度和可溶性固形物含量的预测

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Several nondestructive technologies have been developed for assessing the firmness and soluble solids content (SSC) of apples. Each of these technologies has its merits and limitations in predicting the two quality parameters. With the concept of multi-sensor data fusion, different sensors would work synergistically and complementarily to improve the quality prediction of apples. In this research, four sensing systems (i.e., an acoustic sensor, a bioyield firmness tester, a miniature near-infrared (NIR) spectrometer, and an online hyperspectral scattering system) were evaluated and combined for nondestructive prediction of firmness and SSC of 'Jonagold' (JG), 'Golden Delicious' (GD), and 'Delicious' (RD) apples. A total of 6,535 apples harvested in 2009 and 2010 were used for analysis. Each of the four sensors showed various degrees of ability to predict apple quality. Better predictions of the firmness and, in most cases, of the SSC were obtained using sensors fusion than using individual sensors, as measured by number of latent variables, correlation coefficient, and standard error of prediction (SEP). Results obtained from the two harvest seasons with the multi-sensor fusion approach were quite consistent, confirming the validity and robustness of the proposed approach. The SEPs for firmness measurement of JG, GD and RD using the best combination of two-sensor data were reduced by 13.3, 19.7 and 7.9% for the 2009 data and 16.0, 12.6 and 4.7% for the 2010 data; and using all four-sensor data by 21.8, 25.6 and 13.6% in 2009, and 14.9, 21.9, and 7.9% in 2010, respectively. For SSC prediction, using the two-sensor data (i.e., NIR and scattering) improved predictions for JG, GD and RD apples harvested in 2009, with their SEP values being reduced by 10.4, 6.6 and 6.8%, respectively. This research demonstrated that the fused systems provided more complete complementary information and, thus, were more powerful than individual sensors in prediction of apple quality.
机译：已经开发了几种非破坏性技术来评估苹果的硬度和可溶性固形物含量（SSC）。这些技术中的每一种在预测两个质量参数方面都有其优缺点。利用多传感器数据融合的概念，不同的传感器将协同工作并互补地工作，以改善苹果的质量预测。在这项研究中，对四个传感系统（即声学传感器，生物屈服强度测试仪，微型近红外（NIR）光谱仪和在线高光谱散射系统）进行了评估，并将其组合在一起，以对'Jonagold的硬度和SSC进行无损预测（JG），“ Golden Delicious”（GD）和“ Delicious”（RD）苹果。分析使用了2009年和2010年总共收获的6,535个苹果。四个传感器中的每一个都显示出不同程度的预测苹果品质的能力。通过潜伏变量的数量，相关系数和预测标准误差（SEP）来衡量，与使用单个传感器相比，使用传感器融合可以更好地预测硬度，并且在大多数情况下，可以使用SSC。使用多传感器融合方法从两个收获季节获得的结果非常一致，证实了所提出方法的有效性和鲁棒性。使用两种传感器的最佳组合对JG，GD和RD进行硬度测量的SEP分别比2009年数据降低了13.3％，19.7％和7.9％，而2010年数据降低了16.0％，12.6％和4.7％。在2009年使用所有四传感器数据的比例分别为21.8、25.6和13.6％，在2010年分别使用14.9、21.9和7.9％。对于SSC预测，使用2009年收获的JG，GD和RD苹果的双传感器数据（即NIR和散射）进行了改进，其SEP值分别降低了10.4、6.6和6.8％。这项研究表明，融合系统提供了更完整的补充信息，因此在预测苹果品质方面比单个传感器更强大。

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