Colorimetric aptasensors using unmodified gold nanoparticles (AuNPs) have attracted much attention because of their low cost, simplicity, and practicality, and they have been developed for various targets in the past several years. However, previous research has focused on developing single-target assays. Here, we report the development of a homogeneous multiplex aptasensor by using more than one class of aptamers to stabilize AuNPs. Using sulfadimethoxine (SDM), kanamycin (KAN) and adenosine (ADE) as example targets, a KAN aptamer (750 nM), an SDM aptamer (250 nM) and an ADE aptamer (500 nM) were mixed at a 1∶1∶1 volume ratio and adsorbed directly onto the surface of unmodified AuNPs by electrostatic interaction. Upon the addition of any of the three targets, the conformation of the corresponding aptamer changed from a random coil structure to a rigid folded structure, which could not adsorb and stabilize AuNPs. The AuNPs aggregated in a specific reaction buffer (20 mM Tris-HCl containing 20 mM NaCl and 5 mM KCl), which led to a color change from red to purple/blue. These results demonstrate that the multiplex colorimetric aptasensor detected three targets simultaneously while maintaining the same sensitivity as a single-target aptasensor for each individual target. The multiplex aptasensor could be extended to other aptamers for various molecular detection events. Due to its simple design, easy operation, fast response, cost effectiveness and lack of need for sophisticated instrumentation, the proposed strategy provides a powerful tool to examine large numbers of samples to screen for a small number of potentially positive samples containing more than one analyte, which can be further validated using sophisticated instruments.

Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning techniques trigger revolutions for precision agriculture based on deep learning and big data. In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis. We compiled the current sensor tools and deep learning principles involved in plant stress phenotyping. In addition, we reviewed a variety of deep learning applications/functions with plant stress imaging, including classification, object detection, and segmentation, of which are closely intertwined. Furthermore, we summarized and discussed the current challenges and future development avenues in plant phenotyping.

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