Results 91 to 100 of about 19,033 (238)

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

AI‐Driven Circular Construction Waste Management for Advancing Sustainable Development

Sustainable Development, EarlyView.
ABSTRACT Construction and demolition waste (C&DW) represents up to 40% of global solid waste, posing a significant barrier to achieving circular economy (CE) objectives and the Sustainable Development Goals (SDGs), particularly SDG 11 and SDG 12. However, construction waste management (CWM) systems remain constrained by fragmented data environments ...
Mohamed T. Elnabwy, Pablo Martinez
wiley   +1 more source

Artificial Intelligence for Blue Transformation: A Review of Predictive Modeling and Decision Support Systems in Sustainable Aquaculture

Sustainable Development, EarlyView.
ABSTRACT The FAO's “blue transformation” roadmap necessitates a fundamental shift towards precision aquaculture to meet global food security targets while minimizing environmental footprints. This review provides a comprehensive overview of how artificial intelligence (AI) and decision support systems (DSS) serve as pivotal enablers for the “better ...
Mustafa Öz, Enes Üstüner
wiley   +1 more source

Computer vision for Pokémon Battles: A YOLO and Tesseract-Based System for Automated Recognition and Gameplay Analysis

Interfases
Pokémon Double Battles present a complex decision-making environment that has traditionally relied on manual data analysis. This paper introduces an automated system leveraging computer vision and deep learning to extract structured gameplay data from ...
Miguel R. Lladó, Terence Morley
doaj   +1 more source

AI‐assisted automated endpoint interpretation for turbidity‐based digital LAMP using bright‐field microscopy

VIEW, EarlyView.
Turbidity‐based digital LAMP offers a low‐cost alternative for nucleic acid quantification, but interpreting bright‐field images is difficult due to weak contrast and noise. We developed NanoFuse‐YOLO11, an AI‐based detection model that automatically identifies positive reaction units in turbidity microscopy images.
Zhu Chen, Yiliang Bao, Yue Wang, Kaixuan Guo, Meimei Zeng, Yan Deng, Song Li, Yuan Guo   +7 more
wiley   +1 more source