Results 81 to 90 of about 17,346 (205)

Bangladeshi crops leaf disease detection using YOLOv8 [PDF]

The agricultural sector in Bangladesh is a cornerstone of the nation’s economy, with key crops such as rice, corn, wheat, potato, and tomato playing vital roles.
Hossen, Md. Jakir, Mazumder, Shazzad Hossain, Al Mamun, Abdullah, Jahan, Busrat, Hossain Mazumder, Shazzad, Al Mamun, Abdullah Sarwar, Abid, Md Shahriar Zaman, Zaman Abid, Md. Shahriar, Hossen, Md Jakir   +8 more
core   +1 more source

Foreign object detection of coal mine conveyor belt based on improved YOLOv8

Gong-kuang zidonghua
The existing deep learning based foreign object detection models for conveyor belts are relatively large and difficult to deploy on edge devices. There are errors and omissions in detecting foreign objects of different sizes and small objects.
HONG Yan, WANG Lei, SU Jingming, WANG Hantao, LI Mushi   +4 more
doaj   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract An agronomic trait such as stand count is important for cultivar development and crop management practices. Manually counting the number of plants is time consuming, labor‐intensive, and prone to error. The use of unoccupied aerial systems (UAS)‐collected red, green, blue (RGB) imagery in conjunction with advanced deep learning and image ...
Aliasghar Bazrafkan, Valerio Hoyos‐Villegas   +1 more
wiley   +1 more source

Attention-Enhanced YOLOv8 for Accurate Pedestrian Detection and Count Estimation [PDF]

Autonomous vehicles (AVs) are crucial for improving road safety by reducing human errors, which are a leading cause of accidents. They also offer significant potential for increased efficiency in transportation, lowering emissions, and enhancing mobility
Jubair, Mohammed Ahmed, Nafea, Mohammed Mansoor   +1 more
core  

Enhancing placental pathology detection with GAMatrix-YOLOv8 model

Purpose: Given the advancements in machine vision, deep learning, convolutional neural networks, and other technologies, various artificial intelligence techniques have been used in placental pathology examinations.
Zhifa Jiang, Zhen Zhang, Xiaoyan Wei, Weirui Wu, Jingwen Liu, Xiangyun Ye, Jiahui Ji   +6 more
core   +1 more source

Enhanced fire hazard detection in solar power plants: an integrated UAV, AI, and SCADA-based approach

Радіоелектронні і комп'ютерні системи
The Subject of this research is the development of an intelligent, integrated system for the early detection and causal analysis of fire hazards in large-scale solar power plants (SPPs).
Andrii Lysyi, Anatoliy Sachenko, Pavlo Radiuk, Mykola Lysyi, Oleksandr Melnychenko, Oleksii Ishchuk, Oleg Savenko   +6 more
doaj   +1 more source

Automatic measurement of rice tiller angle from unmanned aerial vehicle images

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Rice (Oryza sativa L.) tiller angle is an important trait that influences plant architecture, canopy light interception, and yield potential. In this study, we proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery.
Tan‐Hanh Pham, Yubin Yang, Jing Zhang, Sanai Li, Stanley Omar P. B. Samonte, Fugen Dou, Darlene Sanchez, Lloyd T. Wilson, Tanumoy Bera, Jing Wang, Kim‐Doang Nguyen   +10 more
wiley   +1 more source

YOLOv8-FDD: A Real-Time Vehicle Detection Method Based on Improved YOLOv8

Aiming at the serious problems of missed detection, false detection and difficult deployment of existing target detection algorithms when applied to traffic scenes, a vehicle detection model YOLOv8-FDD with lower parameter count and higher accuracy is ...
Yipeng Wang, Dexin Yu, Zimin Yuan, Xiaojia Liu   +3 more
core   +1 more source

Shrimp Diseases Detection Method Based on Improved YOLOv8 and Multiple Features

智慧农业
ObjectiveIn recent years, there has been a steady increase in the occurrence and fatality rates of shrimp diseases, causing substantial impacts in shrimp aquaculture.
XU Ruifeng, WANG Yaohua, DING Wenyong, YU Junqi, YAN Maocang, CHEN Chen   +5 more
doaj   +1 more source