Leaf area index (LAI) is an important parameter for studying forest ecosystem and vegetation canopy structure. It is an important work in forestry engineering to measure LAI efficiently and accurately. The traditional LAI measurement method requires manual hand-held instruments to make on-site measurement, which is time consuming and laborious. In recent years, with the development of Internet of Things, the technology of using wireless sensors to measure LAI has gradually become mature, but some problems still need to be solved. This paper proposed a LAI measurement method based on digital infrared hemispherical photography (DIHP), and designed an adaptive segmentation algorithm for the color space of infrared photography, which was deployed on the edge computing platform "Raspberry Pi" to solve the problem that traditional digital hemispherical photography (DHP) methods are prone to environmental interference. The measurement results of the sensor designed in this paper are significantly correlated with the hand-held vegetation canopy analyzer HM-G20, with an R value of 0.99691 and an average measurement accuracy of 93.57%, which is 13.85% higher than that of DHP. The DIHP sensor has low operating power consumption, which meets the requirements of long-term field deployment of forestry Internet of Things and has great application prospects.