ABSTRACT
Strawberry (Fragaria X ananassa) is one of the most popular fruits cultivated around the world. That is owing to its unique flavor and nutritious properties in addition to the wide usage utility in fresh or processed condition. Strawberry fruits also have a significant economic importance around the world with strong potential as an export commodity. As a matter of fact, investigation and assessment of various strawberry fruit characteristics at different developmental stages is crucial for candidate cultivars selection in fruit plantation as well as fruit yield prediction. Strawberry fruits developmental stage is conventionally applied visually based on expert knowledge, which is a time and labor exhaustive process. Thus, this paper presents a dataset, namely Strawberry-DS (Strawberry-Developmental Stages) dataset, consisting of strawberry fruits (Festival CV type) expert-annotated images at various developmental stages. Data collection was performed on site during the period between January and March from a greenhouse located in the Central Laboratory for Agricultural Climate (CLAC) at the Agricultural Research Center of the Ministry of Agriculture and Land Reclamation in Giza, Egypt. The dataset comprises 247 high-resolution RGB (.jpg) images annotated manually, using Roboflow Annotate annotation tool with reference to ground truth Region of Interest (RoI), and presented in YOLO (.txt files) format. The presented Strawberry-DS dataset can be generally used for developing various automated models of strawberry fruits detection, fruits maturity stage classification, as well as visual counting, through taking into account the visual features such as shape, color, size, and texture of strawberry fruits. Strawberry-DS is freely available at: https://data.mendeley.com/datasets/z6dtfdpzz8/1.
ABSTRACT
This article presents the details of Historical-crack18-19 dataset containing around 3886 annotated concrete surface images from historical buildings. The dataset comprises about 40 raw images collected from an ancient mosque (Masjid) in Historic Cairo, Egypt, with about 757 cracked and 3139 non-cracked surface instances. The images of Historical-crack18-19 dataset were captured using Canon EOS REBEL T3i digital camera with 5184 × 3456 resolution over two years (2018 and 2019). The images of Historical-crack18-19 dataset are annotated with the help of an expert and are intended for training and validation of automated non-invasive crack detection and crack severity recognition as well as crack segmentation approaches based on Machine learning (ML) and Deep Learning (DL) models. According to the environmental circumstances, where the dataset was collected, several challenges are encountered by crack detection/segmentation systems in surface images of historical buildings (illumination, crack-like patterns, separators, dust, blurring, deep texture, etc.). Further, researchers can use the dataset for benchmarking the performance of state-of-the-art methods designed for solving related (image classification and object detection problems. Historical-crack18-19 dataset is freely available at [https://data.mendeley.com/datasets/xfk99kpmj9/1].
