A Study on Image Processing on Self-Propelled and Movement for a Leafy Vegetable Harvester

• Main Authors: Fu-Kai Wang, 王富凱
• Other Authors: Chung-Kee Yeh
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/4gp985

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 106 === According to Council of Agriculture, R.O.C. (Taiwan), those aged between 50 and 64 years account for 44.5% of Taiwan’s total agricultural labor force. This figure highlights Taiwan’s problematic agricultural labor force structure. Because of the aging agricultural labor force and increasing urbanization, finding workers to hire during the harvest season has become challenging. Accordingly, crops cannot be harvested in time, so crop quality has deteriorated. Additionally, crops have become increasingly likely to fail. Because current policy does not allow foreign agricultural workers to work in Taiwan, agricultural automation is required to resolve this problem. In this study, we developed a leafy vegetable harvester that can move within a screened greenhouse and harvest leafy vegetables through image processing. The experiment comprised three parts, including the best image processing method I should use, development of behavioral control for the crawler-track cutter and development of movement control for the crawler-track motor. To develop appropriate motor control for the cutter, 40 images of crops photographed in National Taiwan University underwent image processing. Each image was divided into crop sections and soil sections. The number of white dots in the crop sections was calculated to estimate the number of white dots that crops should have. Finally, the critical value method was adopted for a large sample size and unknown population variance to ascertain a representative crop’s critical value and to determine whether the cutter should take actions. The results revealed that when the camera resolution was 4068 × 3456 and the area of the crop section exceeded 5,825,540 pixels, with a confidence level of 95%, the area reflected crops to be harvested. For movement control of the crawler-track motor, a crawler-track car produced by this study was utilized. A Raspberry Pi, a web camera, and a motor driver board were installed to the car to control and monitor its movement. In the experiment, images obtained through the web camera were processed through hue-saturation-value color-space conversion. Subsequently, image banalization was conducted, and software was used to calculate the differences in area between the two crop sections at the right-hand and left-hands side of the image. The motor’s rotational speed was controlled by adjusting the proportional-integral-derivative parameters to achieve minimal steady-state errors and optimal maximal overshoot. The results revealed that the optimal tracking effect was produced when kp = 0.2.