Measurement Methods of Fruit Tree Canopy Volume Based on Machine Vision

Abstract:

There were some problems of artificial and sensor measurement for tree canopy volume, such as inefficiency, low precision, high cost, complex operation. In order to solve those problems, a new measurement method based on machine vision was proposed. The previous research indicated that there was significant correlation between tree canopy area and canopy. Based on this, the new method was proposed. Firstly, tree canopy image was obtained by machine vision according to the set standards. Secondly, tree canopy area was extracted by using a series of image processing operations. Meanwhile, the least square method and the 5-point calibration method were used to obtain the model of tree canopy volume. Finally, the corresponding volume was got. Experimental result showed that the average prediction error of the model of pear tree and Osmanthus fragrans were 13.73% and 10.18%, respectively. In view of the conditions of tree canopy, the structure estimation method was used to fit ellipsoid structure according to the contour of tree canopy that without a series of samples. Then, the volume of tree canopy was got by the compensation formula. Experimental result showed that the average prediction error of the model of peach trees and Osmanthus fragrans was about 10%. Consequently, characteristics extraction method of fruit tree canopy images was effective and feasible. The tree canopy volume characteristics can be perfectly expressed by tree canopy area and contour.