# A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment
## 1 Introduction  
Manpower decrease and aging in the agricultural sector for food production cause the price rise of farm products. In order to keep up farmer's productivity and competitiveness, therefore, it is necessary to improve and automate agricultural technology [1]. More than 20,000 tons of sweet pepper (in other words, bell-pepper), one of South Korea's most cultivated greenhouse plants, are exported abroad annually, with the share of South Korean sweet pepper in the Japanese market exceeding 60% [2; 3]. However, with the recent increase of its unit production cost due to the continuous rise of labor costs, the productivity of sweet pepper is decreasing continuously in South Korea [4]. In addition, the commercial value of sweet pepper is reduced by the harvesting of day laborers lacking harvesting expertise [5]. In order to solve these problems of sweet pepper harvesting in South Korea, it is necessary to introduce a robot-based automated harvesting system. In order to carry out a robot-used automated harvest of sweet pepper, it is necessary to introduce a system which can consider the farming environment of the greenhouse and the location,size, shape and color of fruits. It is also necessary to reduce the problem of damaging the stem of sweet pepper in the course of the harvest. In the cases of South Korea, there has been active research on the automation of pest control and the development of automation technology for agricultural work in the interest of crops, but little research on fruit recognition and harvesting has been conducted recently [6]. In South Korea, [PERSON] and [PERSON] [7] developed a harvesting robot, which was designed to be suitable for harvesting oriental melon in a greenhouse, using a 4-axis manipulator structure combining shuttle-type and orthogonal coordinate-type. [PERSON] et al. [8] developed multi-functional robot which was designed to cultivate watermelon in a South Korean greenhouse. [PERSON] et al. [9,10] in South Korea developed an end-effector and a manipulator for a cucumber harvest robot. In contrast to South Korea, researchers around the world are actively studying harvest robots to automate harvesting operations. Fruit harvesting robots and fruit recognition methods have been actively studied for the automation of harvesting work [11]. [PERSON] et al. [12] studied detection methods of guava fruit using an RGB (red, green, blue) depth camera, and then used a state-of-the-art FCN (Fully Convolutional Network) model [13] to segment the guava fruits and branches from aligned RGB images obtained using the RGB depth camera. The precision and recall of guava fruit detection were 0.983 and 0.948, respectively. [PERSON] et al. [14] studied image processing methods to detect yellow peppers for a harvest robot and suggested a method to threshold yellow pepper fruits on a real greenhouse image. A decaying epsilon-greedy algorithm was proposed to threshold the fruit area of yellow peppers, and the performance of the algorithm to threshold the yellow pepper fruit area was 91.5%. [PERSON] et al. [15] suggested the stereo matching method to harvest lithchi fruits. Binocular CCD (Charge-Coupled Device) cameras were used to calculate the coordinates of lithchi fruits in real-world coordinates; the highest matching success rate was 97.37% and the lowest matching success rate was 91.96%. [PERSON] et al. [16] studied the detection method of sweet pepper peduncle for a harvest robot. RGB depth camera was used to detect peduncles of sweet pepper, and color and geometry information acquired from the RGB depth camera were used, and then a supervised-learning approach for the peduncle detection task was utilized; it achieved an AUC (the area under the curve) of 0.71 regarding detection precision of peduncles on field-grown sweet peppers. [PERSON] and [PERSON] [17] suggested a method to recognize sweet pepper fruits for a picking robot in a greenhouse.