| The leaves of plant are vital organs for photosynthesis and transpiration. To a certain extent, the growth index of the leaf playing an important role, the size of the leaf area hasn’t only an important role in the measurement of crop cultivation and breeding practices, but also reflected fertilization and watering or not in the various growth stages. Therefore, the correct and rapid measurement of leaf area is of great significance to agricultural production. However, the traditional leaf area measurements confined to the measurement of the monolithic leaves. When we need to measurement the non-destructive leaves, which may has overlapping, the traditional method has certain limitations. There is an urgent need to design the overlapping leaf area measurement algorithms, in order to meet the needs of non-destructive measurement of leaf area.In this thesis, I proposed a machine vision-based embedded measurement of overlapping leaf area, which based on the problems that exist in the traditional leaf area measurement and referred to the literature. The design uses the STM32as hardware platform, which acquires leaf image by OV7660. The processed image can be put into the STM32processor, displayed in the display module ILI9320.In this thesis, the work of its innovative results mainly reflected in the flowing three aspects:1. In the situation of complex background, using ultra-green gray can extract the target leaf, the after the lifting wavelet transform enhanced weak edge, and finally use the Canny edge detection and binaryzation treatment. Therefore, it can reach extracted from a complex background of the target leaves.2. We can calculate the area of single leaves and stored it. According to the same kind of plant leaves in the shape of the fractal law, we can estimate the each leaf area of the overlapping leaves.3. Through the calculated on the PC and the STM32hardware platform debugging, The experimental results demonstrate the feasibility and accuracy of the algorithm. |
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