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Plant leaf wilting is a frequent symptom responding to drought stress. To realize nondestructive testing of leaf wilting, wilting index LDBM was customized based on double blanket method from fractal theory. A laser scanner was used to acquire threedimensional point cloud data of plant leaf. In the experiment, zucchini, okra, gourd and pumpkin were chosen as research objects to test the index. Meanwhile, the vapor pressure deficit and photo synthetically active radiation were collected as the microenvironment parameters. The seeds were individually sown in the same pots. After the young plant grew, they were placed in a plasticcovered tunnel. The soil water content for these plants was maintained at about 30%. The point cloud data was gathered at 1 h interval between 08:00 and 18:00. The results showed that the index LDBM was capable of sensing various leaf wilting levels. Regression results showed that LDBM had a linear relationship with vapor pressure deficit and photo synthetically active radiation. And the correlation coefficients of four plants were [0.810, 0.879] and [0.712, 0.825], respectively. Furthermore, LDBM had a negative linear relationship with L2DFT, which was tested to be a reasonable wilting index in previous studies. The correlation coefficients of four plants were [0.826, 0.944]. This paper suggested a nondestructive, realtime and in situ method for monitoring plant water deficit stress.