Quantification of Pinus pinea L. Pinecone Productivity Using Machine Learning of UAV and Field Images

Kefauver, Shawn C.; Buchaillot, Ma. Luisa; Segarra, Joel; Fernandez-Gallego, José A.; Araus, J. L.; Llosa, Xavier; Beltrán, Mario; Piqué, Míriam

doi:10.3390/iecf2021-10789

The 2nd International Electronic Conference on Forests—Sustainable Forests: Ecology, Management, Products and Trade 2022

DOI: 10.3390/iecf2021-10789

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Quantification of Pinus pinea L. Pinecone Productivity Using Machine Learning of UAV and Field Images

Shawn C. Kefauver

Ma. Luisa Buchaillot

Joel Segarra

et al.

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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Cited by 2 publications

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Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning

Xiao

2023

Nonlinear Engineering

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In this study, we show a new way for a small unmanned aerial vehicle (UAV) to move around on its own in the plantations of the tree using a single camera only. To avoid running into trees, a control plan was put into place. The detection model looks at the image heights of the trees it finds to figure out how far away they are from the UAV. It then looks at the widths of the image between the trees without any obstacles to finding the largest space. The purpose of this research is to investigate how virtual reality (VR) may improve student engagement and outcomes in the classroom. The emotional consequences of virtual reality on learning, such as motivation and enjoyment, are also explored, making this fascinating research. To investigate virtual reality’s potential as a creative and immersive tool for boosting educational experiences, the study adopts a controlled experimental method. This study’s most significant contributions are the empirical evidence it provides for the efficacy of virtual reality in education, the illumination of the impact VR has on various aspects of learning, and the recommendations it offers to educators on how to make the most of VR in the classroom.

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Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning

Xiao

2023

Nonlinear Engineering

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Latest Trends on Tree Classification and Segmentation Using UAV Data—A Review of Agroforestry Applications

2023

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When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this paper presents a literature review on tree classification and segmentation using data acquired by unmanned aerial vehicles, with special focus on the last decade (2013–2023). The latest research trends in this field are presented and analyzed in two main vectors, namely: (1) data, where used sensors and data structures are resumed; and (2) methods, where remote sensing and data analysis methods are described, with particular focus on machine learning approaches. The study and review methodology filtered 979 papers, which were then screened, resulting in the 144 works included in this paper. These are systematically analyzed and organized by year, keywords, purpose, sensors, and methods used, easily allowing the readers to have a wide, but at the same time detailed, view of the latest trends in automatic tree classification and segmentation using unmanned aerial vehicles. This review shows that image processing and machine learning techniques applied to forestry and segmentation and classification tasks are focused on improving the accuracy and interpretability of the results by using multi-modal data, 3D information, and AI methods. Most works use RGB or multispectral cameras, or LiDAR scanners, individually. Classification is mostly carried out using supervised methods, while segmentation mostly uses unsupervised machine learning techniques.

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Quantification of Pinus pinea L. Pinecone Productivity Using Machine Learning of UAV and Field Images

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 2 publications

References 11 publications

Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning

Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning

Latest Trends on Tree Classification and Segmentation Using UAV Data—A Review of Agroforestry Applications

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