The above-ground part of a plant has an important contribution to plant development and yield production. Physiological activities of a plant canopy highly correlate to morphology of plant vegetation. Obviously, leaf area index is a good indicator for leaf area, but does not provide any information about the spatial architecture of plant canopy. With the development of fractal theory, a quantitative toot is now available for the investigation of complex objects and shapes such as plant structure. Vegetation structure of corn ( Zea mays L.) and soybean (Glycine max. (L.) Merr.] plants might be affected by the plant population density (low, normal, high) of each crop and corn-soybean intercropping. Skeletonized leaf-off images provided acceptable information to estimate the fractal dimension of the soybean plant 2-dimensionally, using the box-counting method. Fractal dimension varied among soybean treatments, with rankings: low > normal > intercrop > high, in the overall mean and normal ≈ intercrop ≈ low > high, in the slope of time plots. An adjustment of field corn plants to treatments, by changing the orientation of the plane of developed leaves with respect to the row, was observed. Thus, the fractal dimension of corn plant skeletal images from each of two sides, side I (parallel to row) and side 2 (perpendicular to row), was analyzed. On the basis of overall means of fractal dimension, treatments were ranked as: high > normal ≈ intercrop ≈ low for side 1 and intercrop > low ≈ normal > high for side 2. In both cases of soybean and corn plants, leaf area index, plant height and number of leaves (only in case of soybean plant) increased over the experiment for all the treatments, indicating a positive correlation with fractal dimension. In contrast, light penetration decreased during crop development, indicating a negative correlation with fractal dimension. Furthermore, a modified version of the Beer-Lambert equation, in which fractal dimension mu

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