## 1 Introduction  
Smart farming provides labor conservation, automation, and improved efficiency of farming operations, thereby leading to advancement of value-added agricultural activities [1]. Some representative case studies of successful smart farming are automatic operations of farm machinery in terms of tractors and combine harvesters using multiple sensors and global navigation satellite systems (GNSS), automatic water control and management systems based on internet of things (IoT) sensors and devices [2], crop monitoring,and pesticide and fertilizer spreaders using drones [3]. Although smart farming has already progressed to the implementation, demonstration, and dissemination phases, the major tasks are limited to automated repetition of simplified operations and locomotion based on operational procedures and routes that must be set in advance. Practical applications of deep learning (DL) [4] technologies for smart farming are progressing rapidly [5]. Compared with general DL applications in terms of automated driving [6], biometric identification [7], and medical image processing [8], technical challenges remain, especially for classification, recognition, and decisions for agricultural applications [9; 10; 11]. Moreover, the collection of high-quality and large agricultural datasets [12] entails great burdens because of the wide variations in crop growing periods, weather patterns, air temperature, seasonal changes, and various external factors [13]. Although extensive amounts of elaborately annotated data improve DL performance and avoid overfitting [14], the burdens and time of annotation [15], especially for pixel-wise segmentation labels, make acquiring and using them labor-intensive and expensive. Therefore, numerous research tasks remain for DL applications in smart farming [16]. This study specifically examines semantic segmentation, which is a major task in computer vision studies, to explore DL-based solutions as challenges for smart farming [17]. Particularly, this study examines semantic segmentation of crop images [18] related to disease detection, weeding, and cultivation management. Image segmentation can be of three types: semantic segmentation, instance segmentation, and panoptic segmentation [19]. Segmentation targets comprise things as countable objects and stuff as matter, material, and textures. Semantic segmentation can handle both targets. Things within the same object category are assigned the same label. Instance segmentation handles only things. Things in each object are assigned with unique labels for the respective instances. Panoptic segmentation includes both semantic and instance segmentation mechanisms. [PERSON] et al. [20] proposed an instant segmentation method for crop and weed plants using a region-based convolutional neural network (R-CNN) [21]. Instance segmentation provides flexible detection of each crop and weed for sparse vegetation. Their research project specifically examined sparse vegetation images because they were intended to be mounted on a precision agricultural robot. By contrast, instance segmentation will be challenging if the vegetation distribution is dense. This paper presents a semantic segmentation framework to exploit agricultural images with dense vegetation. Numerous benchmark image datasets are available for semantic segmentation in agriculture [22]. These datasets were annotated using ground truth (GT) labels through an enormous effort. Annotations for agricultural datasets are conducted for the respective purposes and tasks. Special training is also necessary for annotators in advance, depending on objects such as crop damage and leaf types. To reduce burdens for annotating and producing synthesized data [23], this study is intended to generate crop datasets based on image style transfers [24] using generative adversarial networks (GANs) [25].