Abstract: Abstract: In order to perform online and nondestructive measurements of the parameters of flora, the use of machine vision technology was investigated. This technology was used to capture the image of a flora canopy, and then three segmentation algorithms: Excess Green (ExG) minus Excess Red (ExR), ExG, and normalized difference indices (NDI) were used to extract the canopy area of the flora. The ExG and NDI used an Otsu threshold value to obtain a binary image, and the ExG-ExR used a fixed threshold value to obtain a binary image. Flora canopy characteristic parameters (covering ratio, canopy length, and canopy width) were extracted based on the projection profile of the canopy leaves extracted by the flora canopy segmentation methods. These were combined with the parameters of the flora obtained by artificial measurement: stem height, stem diameter, leaf number, fruit number, and LAI (fitting value), to form five types of inversion models for the five growth parameters of the flora. The inversion models were based on the covering ratio, canopy width, and canopy length, and a regression equation established by three parameters of the flora and an average inversion model were established. The results showed that the contact ratio and recognition rate of extraction of the flora canopy region, using the segmentation method ExG-ExR, were more than 99.5% and 98.2%, respectively. Furthermore, identification of the flora canopy was accurate, and there were very few mistakenly identified areas. No matter when the image was captured, the recognition performance of the flora canopy image was stable, and the performance was superior to the methods ExG+Otsu and NDI+Otsu. The contact ratio of the ExG+Otsu segmentation method ranged from 72.7% to 93.5% and recognition rate was 71.1%-90.2%, and showed a small amount of leakage and error used to partition the flora canopy figures. The contact ratio of the NDI+Otsu segmentation method ranged from 99.9% to 100%, however, the scope of the recognition rate was 13.1%-89.2%, and showed a high incidence of false recognition and unstable performance. Inversion models were validated using 120 new images. The inversion results showed that the regression coefficient between the inversion value and the measured value was greater than 0.958 when using the inversion model of the flora canopy covering ratio. The performance of the flora canopy covering ratio was superior to the inversion models of canopy width and canopy length. The inversion model using the regression equation and the average model were the only two parameters that were better than the inversion model of the covering ratio. Between the inversion values of stem height, stem diameter, leaf number, fruit number, LAI, and the measured values of each, the regression coefficient were 0.979, 0.976, 0.979, 0.965, and 0.973, respectively, and the SE were 10.55 cm, 1.37, 0.213 mm, 0.672, and 0.055, respectively. The inversion method, based on machine vision technology, can achieve online and nondestructive measurements of the parameters of flora, which can provide significant advances in controlling the greenhouse environment and precise fertigation.