Nowadays deep networks provide excellent results in the context of object segmentation. Available models have been trained on common objects and are not designed to segment specific objects such as fruits or vegetables. In order to help breeders to accelerate and to modernize the process of agriculture products phenotyping, it is necessary to fine tune general models on specific species. Nevertheless, a minimum amount of annotations are required for this retraining step. In this paper, we propose a solution to minimize the annotation workload for each specie. The main idea consists in leveraging the annotations of one specie A in order to fine tune a model on a specie B with few annotations. For this purpose, we propose an Instance-based CycleGAN (ICG) that creates synthetic images of specie B along with corresponding annotations. By fine tuning a segmentation network with these synthetic images and annotations, we show that this network can obtain very good performance on the new specie B, without requiring to manually annotate a large amount of images for this specific specie B.