577 research outputs found
Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation
Multi-modal 3D scene understanding has gained considerable attention due to
its wide applications in many areas, such as autonomous driving and
human-computer interaction. Compared to conventional single-modal 3D
understanding, introducing an additional modality not only elevates the
richness and precision of scene interpretation but also ensures a more robust
and resilient understanding. This becomes especially crucial in varied and
challenging environments where solely relying on 3D data might be inadequate.
While there has been a surge in the development of multi-modal 3D methods over
past three years, especially those integrating multi-camera images (3D+2D) and
textual descriptions (3D+language), a comprehensive and in-depth review is
notably absent. In this article, we present a systematic survey of recent
progress to bridge this gap. We begin by briefly introducing a background that
formally defines various 3D multi-modal tasks and summarizes their inherent
challenges. After that, we present a novel taxonomy that delivers a thorough
categorization of existing methods according to modalities and tasks, exploring
their respective strengths and limitations. Furthermore, comparative results of
recent approaches on several benchmark datasets, together with insightful
analysis, are offered. Finally, we discuss the unresolved issues and provide
several potential avenues for future research
Impacts of coffee fragmented landscapes on biodiversity and microclimate with emerging monitoring technologies
Habitat fragmentation and loss are causing biodiversity declines across the globe. As biodiversity is unevenly distributed, with many hotspots located in the tropics, conserving and protecting these areas is important to preserve as many species as possible. Chapter 2 presents an overview of the Ecology of the Atlantic Forest, a highly fragmented biodiversity hotspot. A major driver of habitat fragmentation is agriculture, and in the tropics coffee is major cash crop. Developing methods to monitor biodiversity effectively without labour intensive surveys can help us understand how communities are using fragmented landscapes and better inform management practices that promote biodiversity. Acoustic monitoring offers a promising set of tools to remotely monitor biodiversity. Developments in machine learning offer automatic species detection and classification in certain taxa. Chapters 3 and 4 use acoustic monitoring surveys conducted on fragmented landscapes in the Atlantic Forest to quantify bird and bat communities in forest and coffee matrix, respectively. Chapter 3 shows that acoustic composition can reflect local avian communities. Chapter 4 applies a convolutional neural network (CNN) optimised on UK bat calls to a Brazilian bat dataset to estimate bat diversity and show how bats preferentially use coffee habitats. In addition to monitoring biodiversity, monitoring microclimate forms a key part of climate smart agriculture for climate change mitigation. Coffee agriculture is limited to the tropics, overlapping with biodiverse regions, but is threatened by climate change. This presents a challenge to countries strongly reliant on coffee exports such as Brazil and Nicaragua. Chapter 5 uses data from microclimate weather stations in Nicaragua to demonstrate that sun-coffee management is vulnerable to supraoptimal temperature exposure regardless of local forest cover or elevation.Open Acces
The Digital Agricultural Revolution: a Bibliometric Analysis Literature Review
The application of digital technologies in agriculture can improve
traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG)
emissions, and promote a sustainable intensification for food security. Some
authors argued that we are experiencing a Digital Agricultural Revolution (DAR)
that will boost sustainable farming. This study aims to find evidence of the
ongoing DAR process and clarify its roots, what it means, and where it is
heading. We investigated the scientific literature with bibliometric analysis
tools to produce an objective and reproducible literature review. We retrieved
4995 articles by querying the Web of Science database in the timespan
2012-2019, and we analyzed the obtained dataset to answer three specific
research questions: i) what is the spectrum of the DAR-related terminology?;
ii) what are the key articles and the most influential journals, institutions,
and countries?; iii) what are the main research streams and the emerging
topics? By grouping the authors' keywords reported on publications, we
identified five main research streams: Climate-Smart Agriculture (CSA),
Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT),
and Artificial Intelligence (AI). To provide a broad overview of each of these
topics, we analyzed relevant review articles, and we present here the main
achievements and the ongoing challenges. Finally, we showed the trending topics
of the last three years (2017, 2018, 2019)
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