UAV Image Motion Deblurring Methods in Precision Agriculture: A Bibliometric Analysis to a Short Survey

Image motion deblurring is an important issue in computer vision applications which encounter challenges like motion blur caused by camera shake, fast motion, or irregular deformation of agricultural living things during image acquisition. Images acquired by UAV-embedded cameras are often blurred and usually error-prone in precision agriculture. So, image deblurring in applications such as plant phenotyping recognition, crop pests and diseases detection or animal behavior analysis is a great challenge. The main purpose of this paper is to carry out both a bibliometric analysis to assess the current research trends on UAV image motion deblurring with a brief survey of the main image motion deblurring techniques in agriculture. So, we used the Scopus database and 2138 articles were retrieved. This dataset has then been analyzed using a bibliometric tool. According to results, the most impactful authors have 53 and 46 publications respectively. Remote Sensing is the most impactful journal with an h-index of 49 and 285 published articles, whereas China is the country with the most impactful production and the most cited document, indicating its considerable influence in this area of research. Results from the short survey indicate that further research is needed to develop more robust and efficient motion deblurring techniques tailored to the specific challenges of UAV imagery in precision agriculture.