The existing datasets lack the diversity required to train the model so that it performs equally well in real fields  under varying environmental conditions. To address this limitation, we propose to collect a small number of in-field data and use the GAN to generate synthetic data for training the deep learning network. To demonstrate the proposed method, a maize dataset 'IIITDMJ_Maize'  was collected using a drone camera under different weather conditions, including both sunny and cloudy days. The recorded video was processed to sample image frames that were later resized to 224 x 224.