A deep learning approach based on graphs to detect plantation lines.

Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns.

Multicomponent or Resistance Training for Nursing Home Residents: A Systematic Review With Meta-Analysis.

Objectives: To perform a systematic review with meta-analysis to verify the effects of multicomponent and resistance training on the physical performance in older adult residents in long-term care, as well as to compare these modalities.

Design: Systematic review with meta-analysis of randomized controlled trials.

Setting And Participants: Older adults age over 60 years who are nursing home residents in long-term care.

Guided Endodontics in Root Canals with Complex Access: Two Case Reports.

This article reported two clinical cases in which the guided endodontics was used to perform the access to the root canals. The first case presents a 40-year-old female with a history of pain related to the left maxillary canine. After radiographic examination, the presence of severe calcification up to the apical third of the root canal, associated with a periapical radiolucency, was noted.

Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network.

Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods.

Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing.

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits.

Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method.

Mapping utility poles using side-view images acquired with car-mounted cameras is a time-consuming task, mainly in larger areas due to the need for street-by-street surveying. Aerial images cover larger areas and can be feasible alternatives although the detection and mapping of the utility poles in urban environments using top-view images is challenging. Thus, we propose the use of Adaptive Training Sample Selection (ATSS) for detecting utility poles in urban areas since it is a novel method and has not yet investigated in remote sensing applications.

Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery.

Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Jacq.

Storm-Drain and Manhole Detection Using the RetinaNet Method.

As key-components of the urban-drainage system, storm-drains and manholes are essential to the hydrological modeling of urban basins. Accurately mapping of these objects can help to improve the storm-drain systems for the prevention and mitigation of urban floods. Novel Deep Learning (DL) methods have been proposed to aid the mapping of these urban features.

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery.

This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps.

Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs.

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection.

Importance of Vertices in Complex Networks Applied to Texture Analysis.

Texture analysis has attracted increasing attention in computer vision due to its power in describing images and the physical properties of objects. Among the methods for texture analysis, complex network (CN)-based ones have emerged to model images because of their flexibility. In image modeling, each pixel is mapped to a vertex of the CN and two vertices are connected if they are spatially close in the image.

Complex network classification using partially self-avoiding deterministic walks.

Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification relies on the use of representative measurements that describe topological structures.