Automated citrus tree counting from oblique or ortho images and tree height estimation from oblique images.

摘要

The objectives of this research paper were to (1) compare tree counts between oblique and ortho images and (2) determine the accuracy of tree height measurements from oblique images. Accurate determination of tree count and canopy volume can aid growers in planning some orchard operations and estimating yield. The analytical capability of Feature Analyst (FA) to count trees enables the user to quantify the trees in a given area. Eight sample sets of ortho and oblique images were selected from Pictometry aerial imagery software (Pictometry International Corp, 100 Town Center Drive, Suite A, Rochester, NY, 14623) by entering the coordinates of the required area of interest. The images thus obtained were exported to Arc Map in ortho and oblique views. The same training samples randomly selected from the study area in the ortho view were also used for the oblique image in FA to validate/determine the number of trees. The image was represented by a shape file, and an attribute query denoted the quantity of trees. After processing with FA, the majority of the machine counts were below the actual value. Three of the eight ortho images represented over counts, while five represented under count with a total range of -16 to +23%, with a mean error of 8.67%. All the eight oblique images represented under counts with an error range of 0.26 to 27.82% with a mean error of 9.94%. Thus, Oblique images had an error range of 27.56%, while orthogonal images have an error range of 39%, but overall the average error was less for ortho images. An estimate was made of tree height using oblique images in Pictometry. The errors were partially due to the ambiguity in selecting each tree's base and apex. Assuming there may be 0.15 m error due to resolution in ground area covered and tree height centering contributed an 11% error, overall precision in tree canopy volume measurement was still 85%.