In recent years, precision agriculture and precision weed control have been developed aiming at optimising yield and cost while minimising environmental impact. Such solutions include robots for precise hoeing or spraying. The commercial success of robots and other precision weed control techniques has, however, been limited, partly due to a combination of a high acquisition price and low capacity compared to conventional spray booms, limiting the usage of precision weeding to high-value crops. Nonetheless, conventional spray booms are rarely used optimally. A study by Jørgensen et al. (2007) has shown that selecting the right herbicides can lead to savings by more than 40 percent in cereal fields without decreasing the crop yield when using conventional sprayers. Therefore, in order to utilise conventional spray booms optimally, a preliminary analysis of the field is necessary. The major components of this system are composed of three processes: Image Segmentation, Feature Extraction, and Decision-Making. In the Image Segmentation process, the input images are processed into lower units where the relevant features are extracted.