Smart detection of leaf wilting by 3D image processing and 2D Fourier transform

Wilting is a common symptom in plants responding to drought stress. Early wilting detection is of high importance for crop precision management. However, it is challenging to develop a reliable measurement technology. This study presents a sensing method based on three-dimensional (3D) images that are generated by a laser scanner. A leaf wilting index (LWI2DFT) is defined from the spectrum of a two-dimensional (2D) Fourier transform (FT), in which a leaf is equivalent to a mathematical surface in 3D space, and thus a wilting process refers to a series of the curved surfaces. To verify the effectiveness of the index LWI2DFT, we applied our method to zucchini plants (Cucurbita pepo L.) 17 days after they emerged from the soil. The laboratory experiments included two periods of 10 days each. From six leaves of three plants tested in the first period, it was observed that this index was capable of sensing the stress response of the zucchini from slight wilting to severe stress levels. The regression results between LWI2DFT and ambient temperature (Tair) fitted a linear equation with 0.814 6 R 6 0.908, and those between LWI2DFT and the photosynthetically active radiation (LPAR) with 0.696 6 R 6 0.856. In the second period we repeated the measurements with new samples to validate our results. A good correlation (0.609 6 R 6 0.899 for Tair, 0.748 6 R 6 0.892 for LPAR) confirmed the reliability of our proposed method. 2012 Elsevier B.V. All rights reserved.