Light distribution within fruit tree canopy is one of the important factors to optimize fruit tree architecture and to improve the potential production and fruit quality. Take the canopy structure, radiation environment and light transmission into consideration, plant model simulation analysis is an important way to measure light distribution within tree canopy in the field. There were 700 to 900 multi-view visible images for one Chinese bayberry tree obtained by moving a camera around the tree canopy. Using machine vision method, three-dimensional point cloud was reconstructed from images based on the multiple-view stereo and Structure From Motion (MVS-SFM) algorithm. The encapsulating, smoothing and filling algorithm were used to transform point clouds into smooth surfaces consisting of triangular facets in computers automatically. Canopy height, canopy width, branch length and circumference for the first three grades were measured in the orchard to evaluate the performance of the 3D structural model. Ray-tracing method in a 3D radiation model was used to calculate light distribution within the 3D tree canopy. The light distribution result within the 3D structural model was visualized using computer technology. The results showed that the multi-view image method well reconstructed the tree canopy. There was satisfactory correspondence between acquired and measured tree trunk height, circumference and canopy width, with the root mean squared error (RMSE) smaller than 1.2 cm and the consistency coefficient (d) larger than 0.90. The light was mainly distributed in the periphery of the canopy and in the upper-middle layer of tree canopy. Due to the sever occlusion of foliage and branches, the light was less in the inner and lower layer of the canopy. The fraction of light interception was 0.10, 0.17 and 0.05 for the bottom, middle and upper 1/3 canopy layer respectively. This study provide possibility for high-throughput 3D canopy structure measurement in the orchard and provide a reference for tree pruning, ideal tree structure design, and fruit yield and quality improvement.
With the continuous improvement of intelligent management level in red bayberry orchards, the demand for automatic picking and automatic sorting is becoming increasingly apparent. The prerequisite for achieving these automated processes is to quickly identify the maturity of red bayberries by object detection. In this study, we classified red bayberry into 8 levels of maturity and achieved an object detection precision of 88.9%. We used a fast object detection model, combined with small object optimization methods and small feature extraction layers to get higher precision.
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