# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 1 Introduction  
Rice is one of the major cereal food crops in the world. The transplanting of rice seedlings is a preliminary stage for rice growing and production operations. Traditional transplanting methods include conventional approaches such as manual transplanting (which has been successfully used for many centuries) and mechanical transplanting, but neither is practical for large-scale commercial farms because they are extremely labor-intensive, costly and time-consuming. Hence, it is necessary to use an automated rice transplanter to reduce manual tasks and the cost of crop production, and to contribute to the productivity and competitiveness of farmers in providing agricultural supplies [1]. The key for an automatic rice transplanter operating in-a complex paddy field environment is to have autonomous navigation. In recent years, along with advances in robot technology, navigation systems have been applied to intelligent agricultural machinery such as grafting robots [2], transplanting robots [3] and harvesting robots [4]. Among a number of guidance-sensing technologies investigated in the last decades, two main approaches have achieved the greatest commercial success: global positioning system(GPS) and visual navigation system. GPS is a space-based satellite navigation system with an unobstructed line of sight to four or more GPS satellites, and it has been used in agricultural robots or automated agricultural machinery to provide location and time information in all weather conditions [5]. For example, [PERSON] et al. [6] designed an automated rice transplanter with a GPS. The study indicated that the system can provide accurate absolute positioning for navigation. However, the cost of GPS is too high to be widely used in agricultural machinery and it has many practical problems, such as drops in the GPS signal because of natural obstacles, errors in satellite communication, and frequent manual intervention [7]. With the improvements in computer calculation speed and the development of visual algorithms, the visual navigation system for agricultural robots has become one of the development trends and has strong real-time and high accuracy characteristics. In the visual navigation system, how to extract the guidance line, especially in a complicated environment, is a difficult task. For the visual navigation of farm machinery, the most widely used method to extract the navigation line consists of two steps [8]: image segmentation of crop rows, and guidance line extraction in an image of the crop row. Image segmentation of crop rows is a fundamental step of extracting information on crop rows in which the essential problem to be tackled is to segment plants, by classifying image pixels into plant or background elements such as soil, stones and water. Guidance line extraction creates a reference, allowing the machinery to determine its position relative to the guidance line and to follow the crop row without damaging crops. Thus, good performance in segmenting crops is crucial for guidance line extraction. Over the last decade, numerous crop segmentation approaches have been reported in the literature [9]. Among them, color-index-based methods are the most commonly used methods for crop segmentation, in which the RGB (Red Green Blue) space is first converted into an alternative color space to obtain a gray image with high contrast, thus highlighting the plant, i.e., with pixels brighter for plant pixels and darker for non-plant pixels, and then segmenting plants using a threshold [10]. These techniques, including ExG (excess green index) [11], VEG (vegetative index) [12], ExGR (excess green minus excess red index) [13], CIVE (color index of vegetation extraction) [14] and MExG (modified excess green index) [15], generally rely on the principle that the green channel in a plant image contains more useful information than the other two channels, and needs a threshold to classify an image pixel into one class (plant or background).