Results 81 to 90 of about 19,743 (267)

Time‐series digital camera photos combined with machine learning algorithms can realize accurate observation of flowering phenology

Remote Sensing in Ecology and Conservation, EarlyView.
Intelligent approaches are required to extract valuable phenological information from time‐series digital camera photos. In this research, we employed YOLO‐based object detection and semantic segmentation models to identify flowers and flower pixels, acquire flower count and flower cover data, and extract phenophases such as first, peak, and end ...
Chuangye Song, Yuan Jia, Lin Zhang, Dongxiu Wu   +3 more
wiley   +1 more source

Real‑Time Detection and Segmentation of Tomato Pests with YOLOv8

Journal of Agricultural Sciences
Tomato (Solanum lycopersicum L.) is vital for global nutrition and economic stability, yet it is threatened by pests such as Tuta absoluta, Helicoverpa armigera, and Bemisia tabaci. Effective pest management is crucial to prevent significant crop losses.
Yavuz Selim Şahin, Hasan Şahin, Nimet Sema Gençer   +2 more
doaj   +1 more source

Ground‐based robotic remote sensing for standardized biodiversity monitoring in coastal habitats

Remote Sensing in Ecology and Conservation, EarlyView.
Illustrated workflow of the proposed citizen‐to‐robot monitoring pipeline: (i) expert‐validated citizen observations are translated into AI models, (ii) deployed on a ground‐based robotic platform for proximal sensing of coastal dune habitats, (iii) enabling standardized detection of ecological targets (e.g., Pancratium maritimum & Brithys crini), and (
Giovanni Di Lorenzo, Simonetta Bagella, Micaela del Valle Rasino, Maria Laura Carranza, Manolo Garabini, Franco Angelini   +5 more
wiley   +1 more source

Performance Analysis of YOLO, Faster R-CNN, and DETR for Automated Personal Protective Equipment Detection

Journal of Applied Informatics and Computing
Automated monitoring of Personal Protective Equipment (PPE) is crucial for enhancing safety in high-risk environments like construction sites, yet selecting the optimal detection model requires careful evaluation of accuracy versus efficiency trade-offs.
Rihan Naufaldihanif, Dedy Kurniawan, Ken Ditha Tania   +2 more
doaj   +1 more source

Detecting Plateau Zokor (Eospalax baileyi) Mounds in UAV Imagery of Alpine Meadows Using Deep Learning Algorithms

Remote Sensing in Ecology and Conservation, EarlyView.
We developed PZM‐YOLO to automatically detect plateau zokor mounds in UAV imagery of alpine meadows. The model achieved reliable detection of small and densely distributed mounds under complex backgrounds, outperforming the baseline YOLOv5s. This framework supports mound counting, mound position, rodent impact assessment, and grassland restoration ...
Yang Yang, Limin Hua, Lianguo Wang, Jiazhen Li, Rui Dong, Siwei Yang   +5 more
wiley   +1 more source

Inferring Brown Bear Hair Snare Interactions by Automatically Detecting Bipedalism on Camera Trap Images Using Pose Estimation and a Multilayer Perceptron

Remote Sensing in Ecology and Conservation, EarlyView.
This study proposes an automated method to infer brown bear hair snare interactions by detecting bipedal behavior in camera‐trap images using a pose estimation model and a multilayer perceptron (MLP). A YOLO‐based model, fine‐tuned from humans and dogs to a custom dataset, achieved high performance (≈93% keypoint precision and ≈96% classification ...
Arnau Campanera‐Moliné, Víctor Sazatornil, Lluís Brotons, Felipe Lumbreras Ruiz, Robert Benavente, Maguiña Ramilo‐Henry, Cristina Ramos, Santiago Palazón, Daniel Ponsa   +8 more
wiley   +1 more source

Fusion or Confusion? Assessing the Impact of Visible‐Thermal Image Fusion for Automated Wildlife Detection

Remote Sensing in Ecology and Conservation, EarlyView.
This study evaluates the performances of synchronous aerial visible (VIS) and thermal infrared (TIR) imagery for detecting great blue heron (Ardea herodias) nests and individuals using a YOLO11n model. VIS and TIR images were automatically aligned using deep learning, and both early and late fusion approaches were tested.
Camille Dionne‐Pierre, Samuel Foucher, Jérôme Théau, Jérôme Lemaître, Patrick Charbonneau, Maxime Brousseau, Mathieu Varin   +6 more
wiley   +1 more source

Development and Evaluation of a Low‐Cost, Semi‐Automated Camera Trap for Surveying Bumble Bee Communities

Remote Sensing in Ecology and Conservation, EarlyView.
Camera traps are emerging as a useful tool for noninvasive insect monitoring; however, it remains unclear how to lure diurnal insects to traps, how to detect them upon arrival, and how camera‐based methods compare to conventional sampling. We developed a low‐cost, open‐source camera trap to monitor wild bumble bees in agricultural fields. We found that
Michael P. Getz, Lincoln R. Best, Oksana Ostroverkhova, Andony P. Melathopoulos, Timothy L. Warren   +4 more
wiley   +1 more source

EA-YOLO: Efficient Extraction and Aggregation Mechanismof YOLO for Fire Detection

Multimedia Systems
Abstract For fire detection, there are characteristics such as variable sample feature morphology, complex background and dense target, small sample size of dataset and imbalance ofcategories, which lead to the problems of low accuracy and poor real-time performanceof the existing fire detection models.
Dongmei Wang, Ying Qian, Jingyi Lu, Peng Wang, Dandi Yang, Tianhong yan   +5 more
openaire   +1 more source

A Tracking‐By‐Detection Framework to Automatically Count Rare and Elusive Megafauna in Aerial Video Surveys

Remote Sensing in Ecology and Conservation, EarlyView.
Monitoring programs of vulnerable megafauna increasingly rely on aerial surveys from drones or planes fitted with cameras. However, automated methods are still lacking to accurately count individuals across entire surveys. Here, we leverage a tracking‐by‐detection framework to not only detect individuals but also link the resulting detections across ...
Laura Mannocci, Auguste Verdier, Louis Bézard, Valentine Fleuré, Gerard Subsol, Marc Chaumont, David Mouillot   +6 more
wiley   +1 more source