Conventional strain sensors based on metals and semiconductors are rigid and cannot measure soft and stretchable objects. Thus, new strain sensors based on polymer/nanomaterial composites are attracting more interest. Although much effort has been dedicated to achieve high values of both sensitivity and stretchability with linearity, this work endeavors to search and establish guidelines for the development of stretchable strain sensors, by critically reviewing conventional sensors and examining recent progress. It starts from introducing key parameters for conventional strain sensors; these parameters are further discussed for their potential impact on new polymer/nanomaterial strain sensors. The work concludes that there are no general benchmarks for conventional strain sensors utilized in industry. From the findings, the authors suggest that stretchable strain sensors should be custom designed and developed to meet particular measurement requirements, in comparison with a generic aim of yielding a sensor with high degrees of stretchability, sensitivity, and linearity. Challenges are discussed, including reliability, calibration to be used as proper gauges, and soft data acquisition systems.

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The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions.