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Object Detection and Tracking have provided a valuable tool for many tasks, mostly time-consuming and prone-to-error jobs, including fruit counting while in the field, among others. Fruit counting can be a challenging assignment for humans due to the large quantity of fruit available, which turns it into a mentally-taxing operation. Hence, it is relevant to use technology to ease the task of farmers by implementing Object Detection and Tracking algorithms to facilitate fruit counting. However, those algorithms suffer undercounting due to occlusion, which means that the fruit is hidden behind a leaf or a branch, complicating the detection task. Consequently, gathering the datasets from multiple viewing angles is essential to boost the likelihood of recording the images and videos from the most visible point of view. Furthermore, the most critical open-source datasets do not include labels for certain fruits, such as grape bunches. This study aims to unravel the scarcity of public datasets, including labels, to train algorithms for grape bunch Detection and Tracking by considering multiple angles acquired with a UAV to overcome fruit occlusion challenges.

The “EscaYard” dataset comprises multimodal data collected from vineyards to support agricultural research, specifically focusing on vine health and productivity. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing multispectral images and 3D point clouds, and (2) smartphones for detailed ground-level photography. The UAV used was DJI Matrice 210 V2 RTK, equipped with a Micasense Altum sensor, flying at 30 m above ground level to ensure detailed coverage. Ground-level data were collected using smartphones (iPhone X and Xiaomi Poco X3 Pro), which provided high-resolution images of individual plants. These images were geotagged, enabling location mapping, and included data on the phytosanitary status and number of grape clusters per plant. Additionally, the dataset contains RTK GNSS data, offering high-precision location information for each vine, enhancing the dataset's value for spatial analysis. Moreover, the dataset is structured to support various research applications, including agronomy, remote sensing, and machine learning. It is particularly suited for studying disease detection, yield estimation, and vineyard management strategies. The high-resolution and multispectral nature of the data allows for a detailed analysis of vineyard conditions. Potential reuse of the dataset spans multiple disciplines, enabling studies on environmental monitoring, geographic information systems (GIS), and precision agriculture. Its comprehensive nature makes it a valuable resource for developing and testing algorithms for disease classification, yield prediction, and plant phenotyping. For instance, the images of bunches and grape leaves can be used to train object detection algorithms for accurate disease detection and consequent precise spraying. Moreover, yield prediction algorithms can be trained by extracting the phenotypic traits of the grape bunches. The “EscaYard” dataset provides a foundation for advancing research in sustainab

Unmanned aerial vehicles (UAVs) have emerged as versatile tools with significant potential in various fields, including, but not limited to civil engineering, ecology, networking and precision agriculture. Systematic literature reviews (SLRs) play a crucial role in assessing the quality of research methods and approaches, aiding researchers and practitioners in selecting and optimizing their projects. However, the quality assessment of UAV-related SLRs and the aggregation of UAV technologies across research fields remain limited. This study aims to address these gaps by conducting a tertiary literature review (TLR) that assesses the quality of SLRs, aggregates data across research fields, and provides guidelines for researchers and practitioners in the UAV community. Based on a review of 73 SLRs, it is evident that the quality of UAV-related SLRs is generally low, with a lack of quality assessment and inadequate reporting of detailed information on primary studies. Consequently, this study presents reporting items and example quality assessment ratings sourced from the SLRs to enhance the transparency and comparability of future UAV-related research. Additionally, it highlights common limitations faced by UAV applications, such as regulatory, technical, social, and research-related challenges, which require attention for progress in the field. Overall, this study aims to enhance the quality and knowledge sharing in the UAV research community by providing insights into the current state of UAV-related SLRs and offering practical guidance for researchers and practitioners through the provision of data-extraction templates and quality control questions for future UAV-related reviews and primary research.

This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)-based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV-based SLAM. A scoping review was carried out to identify the key concepts and applications, and discover research gaps in the use of algorithm-oriented and task-oriented, open-source studies. A total of 97 studies met the criteria after conducting a two-step screening by a systematic method followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Among eligible studies, 97 were classified into two main categories: algorithm-oriented studies and task-oriented studies. The analysis of the literature revealed that the majority of the studies were focused on the development and implementation of new algorithms and algorithms. This review highlights the significance and diversity of sensors utilized in UAVs in different tasks and applications scenarios that employ different types of sensors. The evaluation method is able to show the real results and performance of the new algorithms in the target scenarios compared with the evaluation method by the public data set and simulation platform.