  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.
