Binocular-Vision-Based Structure From Motion for 3-D Reconstruction of Plants

Monitoring plant growth is essential in modern agriculture to guarantee productivity. Since manual measurement of plant characteristics is laborious and expensive, automatic measures are desirable. This can be accomplished by methods such as vision-based structure from motion (SFM) to obtain the 3-D information of a plant. An SFM method based on binocular vision is here developed to acquire the physical parameters of plants. In this method, image sequences are captured by a binocular camera from multiple views of the target plant to improve the effectiveness and simplify the implementation. The spatial relationships between adjacent images are estimated through image feature extraction and matching. A disparity map is then built and the 3-D coordinate of each image pixel is obtained by applying stereo-vision. The connected coordinates then constitute the 3-D model of the plant. By doing so, plant structure parameters, such as height, canopy size, and trunk diameter, can be derived from the 3-D model. Experimental results show that the measured plant height, the canopy width, and the trunk diameter of the target plant are within an acceptable accuracy at the millimeter level, and the mean errors of the measured sizes are all less than 2%. This demonstrates the potential value of the proposed method for online growth monitoring of agricultural plants.

Automated summary

The SFM method based on binocular vision can reconstruct plant morphology in 3D for plant growth monitoring. The method shows accurate measurement results for plant height, canopy size, and trunk diameter. This approach demonstrates promising potential for online growth monitoring of agricultural plants.