学术文献库

The tree pruning process is the key to promoting fruits' growth and improving their productions due to effects on the photosynthesis efficiency of fruits and nutrition transportation in branches. Currently, pruning is still highly dependent on human labor. The workers' experience will strongly affect the robustness of the performance of the tree pruning. Thus, it is a challenge for workers and farmers to evaluate the pruning performance. Intended for a better solution to the problem, this paper presents a novel pruning classification strategy model called "OTSU-SVM" to evaluate the pruning performance based on the shadows of branches and leaves. This model considers not only the available illuminated area of the tree but also the uniformity of the illuminated area of the tree. More importantly, our group implements OTSU algorithm into the model, which highly reinforces robustness of the evaluation of this model. In addition, the data from the pear trees in the Yuhang District, Hangzhou is also used in the experiment. In this experiment, we prove that the OTSU-SVM has good accuracy with 80% and high performance in the evaluation of the pruning for the pear trees. It can provide more successful pruning if applied into the orchard. A successful pruning can broaden the illuminated area of individual fruit, and increase nutrition transportation from the target branch, dramatically elevating the weights and production of the fruits.