Combined RGB and Depth Images to Detect Crop Rows for Sanshin Green Onion

• Main Authors: Kang, Wen-Yao, 康文耀
• Other Authors: Ou-Yang, Feng
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/dy9y8h

碩士 === 國立宜蘭大學 === 生物機電工程學系碩士班 === 106 === Ilan Shansin green onion is an important economic crop in Ilan. Because the weather of Ilan is wet and rainy, most crops are grown by ridge and furrow farming. Sanshin green onion’s cultivation and management is very labor-intensive and time-consuming. Therefore, detecting row crops lines of the Sanshin green onion on ridge by machine vision will be the critical technology for the unmanned field vehicle. The purpose of the study is to propose a method to combine the results of both color image and depth image acquired by the Kinect sensors to automatic detect the crop rows lines under varying conditions of light brightness and growth period. A Kinect sensor was used to acquire both color and depth images of the crop rows of the Sanshin green onion field. Green onion features were segmented from the background by the difference of the RGB components of the color image. Hough transform method was then used to find crop lines using feature points selected by the proposed grid squares. In the depth image the green onion features were segmented differently by inspecting the difference of the depth in the horizontal lines, and Hough transform method was also used. The slopes of the crop lines and the distances between the upper and lower endpoints of those lines were used to determine whether the results of color image or the depth image were correct. If both were correct, the final result was obtained by multiple specific weighting factors. Those factors were obtained by observing the successful rates and average errors from the experiment results of both the color and depth images. A crop row lines detecting method was successfully developed by combing the results of both color and depth images acquiring by Kinect. The experiment results showed that the average successful rate of color images to find the crop lines in sunny days was 90.4% and in cloudy day was 93.5%. Using depth images to find the crop lines in sunny day was 53.8%, and in cloudy day was 91.3%. The proposed combining method in this study can detect crop lines successful at a rate of 92.3% in sunny days and 100% in cloudy days. Therefore, the combining method in this study can effectively improve the total successful rate comparing with that used only color or depth image.