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This paper presents datasets utilised for synthetic near-infrared (NIR) imagegeneration and bounding-box level fruit detection systems. It is undeniablethat high-calibre machine learning frameworks such as Tensorflow or Pytorch,and large-scale ImageNet or COCO datasets with the aid of accelerated GPUhardware have pushed the limit of machine learning techniques for more thandecades. Among these breakthroughs, a high-quality dataset is one of theessential building blocks that can lead to success in model generalisation andthe deployment of data-driven deep neural networks. In particular, syntheticdata generation tasks often require more training samples than other supervisedapproaches. Therefore, in this paper, we share the NIR+RGB datasets that arere-processed from two public datasets (i.e., nirscene and SEN12MS) and ournovel NIR+RGB sweet pepper(capsicum) dataset. We quantitatively andqualitatively demonstrate that these NIR+RGB datasets are sufficient to be usedfor synthetic NIR image generation. We achieved Frechet Inception Distance(FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepperdatasets respectively. In addition, we release manual annotations of 11 fruitbounding boxes that can be exported as various formats using cloud service.Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novelbounding box datasets on top of our previous work presented in the deepFruitsproject [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. Thetotal number of bounding box instances of the dataset is 162k and it is readyto use from cloud service. For the evaluation of the dataset, Yolov5 singlestage detector is exploited and reported impressivemean-average-precision,mAP[0.5:0.95] results of[min:0.49, max:0.812]. We hopethese datasets are useful and serve as a baseline for the future studies.

deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques