###### Abstract  
Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven operation. This study aims to curtail the effort needed to prepare very large images datasets by creating artificial images of maize (_Zea mays_) and four common weeds (i.e., Charlock, Fat Hen, Shepherd's Purse, and small-flowered Cranesbill) through conditional Generative Adversarial Networks (cGANs). The fidelity of these synthetic images was tested through t-distributed stochastic neighbor embedding (t-SNE) visualization plots of real and artificial images of each class. The reliability of this method as a data augmentation technique was validated through classification results based on the transfer learning of a pre-defined convolutional neural network (CNN) architecture--the _AlexNet_; the feature extraction method came from the deepest pooling layer of the same network. Machine learning models based on a support vector machine (SVM) and linear discriminant analysis (LDA) were trained using these feature vectors. The \(F1\) scores of the transfer learning model increased from 0.97 to 0.99, when additionally supported by an artificial dataset. Similarly, in the case of the feature extraction technique, the classification \(F1\)-scores increased from 0.93 to 0.96 for SVM and from 0.94 to 0.96 for the LDA model. The results show that image augmentation using generative adversarial networks (GANs) can improve the performance of crop/weed classification models with the added advantage of reduced time and manpower. Furthermore, it has demonstrated that generative networks could be a great tool for deep-learning applications in agriculture. eurm  
Although deep-learning networks have enhanced the authenticity of automated crop/weed classification algorithms, the technique suffers from mining large amounts of data that are collected from various geographic conditions. Furthermore, a majority of in-field weed identification tasks require pixel-level annotations [16; 17; 18]. Overall, acquiring huge amounts of data and the preparation of ground truth is a tedious task, especially for precision agriculture applications [19]. Though many open-source agriculture datasets have been available in recent years, the quality and amount of data do not meet the requirements of researchers [19; 20]. In addition, models trained with such data fail to generalize and are not robust enough to be used in diverse practical environments [21]. One way to overcome these difficulties is by adopting image geometric- and intensity-based data augmentation [22]. In addition, when CNNs are employed for machine vision tasks, transfer learning is preferred [23; 24], where a pre-trained deep-learning model is fine-tuned with an available dataset for a particular task [25]. This approach has seen a lot of utilization for in-field weed identification [26; 27; 28]. For instance, [PERSON] et al. developed a solution based on feature extraction from deep layers of various transfer-learned CNN models for automated crop and weed identification [26]. [PERSON] et al. performed a similar study based on transfer learning for identifying weeds in cotton production systems [27]. Both of the above studies recorded classification accuracies greater than 95%. However, such traditional image augmentation techniques and transfer learning provide highly correlated images and only little additional information to the deep-learning model. This not only reduces the ability of the model to generalize but leads to over-fitting problems. In recent years, another advancement in deep learning, in the form of generative adversarial networks (GANs), has proven to be very efficient for data augmentation and image enhancement [29]. GANs can generate artificial-realistic images using existing image data. The combination of these artificial and original images could enhance the development of subsequent models.