The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It is therefore crucial for those involved in UAV research to recognize its interdisciplinary nature and its connections with other disciplines. This paper presents a comprehensive overview of the UAV field, highlighting recent trends and advancements. Drawing on recent literature reviews and surveys, the review begins by classifying UAVs based on their flight characteristics. It then provides an overview of current research trends in UAVs, utilizing data from the Scopus database to quantify the number of scientific documents associated with each research direction and their interconnections. This paper also explores potential areas for further development in UAVs, including communication, artificial intelligence, remote sensing, miniaturization, swarming and cooperative control, and transformability. Additionally, it discusses the development of aircraft control, commonly used control techniques, and appropriate control algorithms in UAV research. Furthermore, this paper addresses the general hardware and software architecture of UAVs, their applications, and the key issues associated with them. It also provides an overview of current open source software and hardware projects in the UAV field. By presenting a comprehensive view of the UAV field, this paper aims to enhance our understanding of this rapidly evolving and highly interdisciplinary area of research.

A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use ‘low-throughput’ methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ ‘high-throughput phenotyping’ to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for ‘digital whole-community phenotyping’ (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.

https://www.frontiersin.org/articles/10.3389/fpls.2023.1141554/pdf

Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field

Abstract Biodiversity is declining across the globe. Main drivers causing the vanishing of species are habitat destruction and the transformation of former heterogeneous landscapes into homogeneous and intensively used landscapes. Modern land management focuses on the creation of landscape configuration with sharp boundaries between ecosystems. In consequence, transition zones between two ecosystems such as between forest and open grassland are rare, as it counteracts the efficient and commercial use of space. However, there are many species relying on such transition zones between habitats, as the Clouded Apollo butterfly Parnassius mnemosyne . This highly endangered butterfly species occurs in light deciduous forests, interspersed with extensively used grasslands. In our study, we analysed habitat requirements of this butterfly species. We recorded larvae and feeding marks at its primary larval food plant, Corydalis cava , and assessed microhabitat characteristics, including microclimatic conditions. We captured high-resolution multispectral aerial imagery with an unmanned aerial vehicle. We subsequently combined ground-truthing data with information from high resolution aerial pictures to calculate habitat suitability models. We found that P. mnemosyne larvae mainly occur in the transition zone between deciduous light forest and extensively used grasslands with C. cava . Caterpillars of P. mnemosyne are particularly found around trees, basking on foliage and grass to rapidly reach high operation temperatures. Results from Species Distribution Models underline the relevance of transition zones between habitats for P. mnemosyne , and for biodiversity in general. The Clouded Apollo may serve as excellent indicator species for light deciduous forests, and as flagship to promote the conservation and restoration of transition zones between habitats in nature conservation. 

/pdf/the-relevance-of-transition-habitats-for-butterfly-1mjgjcnu.pdf

The relevance of transition habitats for butterfly conservation

Wildland conservation efforts require accurate maps of plant species distribution across large spatial scales. High-resolution species mapping is difficult in diverse, dense plant communities, where extensive ground-based surveys are labor-intensive and risk damaging sensitive flora. High-resolution satellite imagery is available at scales needed for plant community conservation across large areas, but can be cost prohibitive and lack resolution to identify species. Deep learning analysis of drone-based imagery can aid in accurate classification of plant species in these communities across large regions. This study assessed whether drone-based imagery and deep learning modeling approaches could be used to map species in complex chaparral, coastal sage scrub, and oak woodland communities. We tested the effectiveness of random forest, support vector machine, and convolutional neural network (CNN) coupled with object-based image analysis (OBIA) for mapping in diverse shrublands. Our CNN + OBIA approach outperformed random forest and support vector machine methods to accurately identify tree and shrub species, vegetation gaps, and communities, even distinguishing two congeneric shrub species with similar morphological characteristics. Similar accuracies were attained when applied to neighboring sites. This work is key to the accurate species identification and large scale mapping needed for conservation research and monitoring in chaparral and other wildland plant communities. Uncertainty in model application is associated with less common species and intermixed canopies.

https://www.mdpi.com/2504-446X/7/7/421/pdf?version=1687686784

A Drone-Powered Deep Learning Methodology for High Precision Remote Sensing in California’s Coastal Shrubs

Thinning is an important routine for apple growers to manage crop load and improve fruit quality, which can be accomplished through manual, chemical, or mechanical manipulation of flowers and fruitlets. Traditionally, blossom thinning relies on human experts’ visual evaluation of the flower load, a leading indicator of crop load, which can be imprecise and prone to errors. This study aimed to develop an apple blossom density mapping algorithm utilizing point clouds reconstructed through unmanned aerial vehicle (UAV)-based red-green-blue (RGB) imagery and photogrammetry. The algorithm was based on grid average downsampling and white color thresholding, and it was able to generate top-view blossom density maps of user-defined tree height regions. A preliminary field experiment was carried out to evaluate the algorithm’s accuracy using manual blossom counts of apple tree row sections as ground truths, and a coefficient of determination (R2) of 0.85, a root mean square error (RMSE) of 1307, and a normalized RMSE (NRMSE) of 9.02% were achieved. The algorithm was utilized to monitor the blooming of the apple tree rows and was demonstrated to effectively show blossom density variations between different tree rows and dates. The study results suggested the potential of UAVs as a convenient tool to assist precise blossom thinning in apple orchards, while future research should further investigate the reliability of photogrammetry techniques under different image qualities and flight settings as well as the influence of blossom distribution on algorithm accuracy.

/pdf/uav-photogrammetry-based-apple-orchard-blossom-density-9fyjqv7e.pdf

UAV Photogrammetry-Based Apple Orchard Blossom Density Estimation and Mapping

\n \n \n Unmanned aerial vehicles (UAVs), i.e., drones, have recently emerged as cost-effective and flexible tools for acquiring remote sensing data with fine spatial and temporal resolution. It provides a new method and opportunity for plant ecologists to study issues from individual to regional scales. However, as a new method, UAVs remote sensing applications in plant ecology are still challenged. The needs of plant ecology research and the application development of UAVs remote sensing should be better integrated. This report provides a comprehensive review of UAV-based remote sensing applications in plant ecology to synthesize prospects of applying drones to advance plant ecology research.\n \n \n \n Of the 400 references, 59% were published in remote sensing journals rather than in plant ecology journals, reflecting a substantial gap between the interests of remote sensing experts and plant ecologists. Most of the studies focused on UAV remote sensing's technical aspects, such as data processing and remote sensing inversion, with little attention on answering ecological questions. There were 61% of studies involved community-scale research. RGB and multispectral cameras were the most used sensors (75%). More ecologically meaningful parameters can be extracted from UAV data to better understand the canopy surface irregularity and community heterogeneity, identify geometrical characteristics of canopy gaps, and construct canopy chemical assemblies from living vegetation volumes. More cooperation between plant ecologists and remote sensing experts is needed to promote UAV remote sensing in advancing plant ecology research.\n

/pdf/uavs-as-remote-sensing-platforms-in-plant-ecology-review-of-1zo58myu3z.pdf

UAVs as remote sensing platforms in plant ecology: review of applications and challenges

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UAVs as remote sensing platforms in plant ecology: review of applications and challenges