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During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. This has underlined the need for advanced Deep Learning and Machine Learning approaches to effectively spot and manage the virus's spread. Indeed, researchers worldwide have dynamically participated in the field by publishing an important number of papers across various databases. In this context, we present a bibliometric analysis focused on the detection and classification of COVID-19 using Deep Learning and Machine Learning techniques, based on X-Ray and CT images. We analyzed published documents of the six prominent databases (IEEE Xplore, ACM, MDPI, PubMed, Springer, and ScienceDirect) during the period between 2019 and November 2023. Our results showed that rising forces in economy and technology, especially India, China, Turkey, and Pakistan, began to compete with the great powers in the field of scientific research, which could be seen from their number of publications. Moreover, researchers contributed to Deep Learning techniques more than the use of Machine Learning techniques or the use of both together and preferred to submit their works to Springer Database. An important result was that more than 57% documents were published as Journal Articles, which was an important portion compared to other publication types (conference papers and book chapters). Moreover, the PubMed journal Multimedia Tools and Applications' tops the list of journals with a total of 29 published articles. [ABSTRACT FROM AUTHOR]

Date fruit is one of the most beneficial and delicious fruits in the world through its nutritional value, it's a rich source of sugar, Protein, Manganese, Fibers, and many other vitamins, on the other hand, date production in the world reached up to 9.45 million tons, one palm tree can produce dates for 40-50 years which makes date production one of the main pillars of the economy. Generally, the methods used for pre and post-harvesting are done manually, making the operation heavy in terms of time and economy. Research in the area of harvesting automation is still limited due to the absence of data. This work presents a full dataset for detection, classification, analysis, and harvesting decisions. The dataset contains a collection of images and videos for different varieties of dates at different stages of maturity, taking into consideration all the natural conditions including light, contrast, dates in bags, and multi-scaled images. The big advantage of this dataset is it can help to put agriculture research at an advanced level by robotizing all the pre and post-harvesting tasks. The dataset is published for free in ZENODO [1] https://zenodo.org/doi/10.5281/zenodo.8315234., (© 2023 The Author(s).)