Graphene is a new type of carbon material with a flexible, two-dimensional structure. Due to the excellent stability of its lattice structure and its mechanical flexibility, graphene-based materials can be applied in flexible humidity sensors. At present, the application of graphene-based flexible humidity sensors in the fields of medical care and environmental monitoring is attracting widespread attention. In this review, the basic properties of graphene oxide (GO) and reduced graphene oxide (rGO) as moisture-sensitive materials and methods for their preparation were introduced. Moreover, three methods for improving the performance of moisture-sensitive materials were discussed. The working principle of different types of graphene-based humidity sensors were introduced. The progress in the research on graphene-based flexible humidity sensors in four respects: Human respiration, skin moisture, human sweat, and environmental humidity were discussed. Finally, the future research, following the development trends and challenges, to develop the potential of integrated, graphene-based flexible humidity sensors were discussed.

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Pine wilt disease (PWD) caused by pine wood nematode (PWN, Bursaphelenchus xylophilus) originated in North America and has since spread to Asia and Europe. PWN is currently a quarantine object in 52 countries. In recent years, pine wilt disease has caused considerable economic losses to the pine forest production industry in China, as it is difficult to control. Thus, one of the key strategies for controlling pine wilt disease is to identify epidemic points as early as possible. The use of hyperspectral cameras mounted on drones is expected to enable PWD monitoring over large areas of forest, and hyperspectral images can reflect different stages of PWD. The trend of applying hyperspectral techniques to the monitoring of pine wilt disease is analyzed, and the corresponding strategies to address the existing technical problems are proposed, such as data collection of early warning stages, needs of using unmanned aerial vehicles (UAVs), and establishment of models after preprocessing.