An Improved Rotating Box Detection Model for Litchi Detection in Natural Dense Orchards DOI Creative Commons
Bin Li, Huazhong Lu, Xinyu Wei

и другие.

Agronomy, Год журнала: 2023, Номер 14(1), С. 95 - 95

Опубликована: Дек. 30, 2023

Accurate litchi identification is of great significance for orchard yield estimations. Litchi in natural scenes have large differences scale and are occluded by leaves, reducing the accuracy detection models. Adopting traditional horizontal bounding boxes will introduce a amount background overlap with adjacent frames, resulting reduced accuracy. Therefore, this study innovatively introduces use rotation box model to explore its capabilities scenarios occlusion small targets. First, dataset on constructed. Secondly, three improvement modules based YOLOv8n proposed: transformer module introduced after C2f eighth layer backbone network, an ECA attention added neck network improve feature extraction 160 × head enhance target detection. The test results show that, compared model, proposed improves precision rate, recall mAP 11.7%, 5.4%, 7.3%, respectively. In addition, four state-of-the-art mainstream networks, namely, MobileNetv3-small, MobileNetv3-large, ShuffleNetv2, GhostNet, studied comparison performance model. article exhibits better dataset, precision, recall, reaching 84.6%, 68.6%, 79.4%, This research can provide reference estimations complex environments.

Язык: Английский

YOLO-TP: A lightweight model for individual counting of Lasioderma serricorne DOI Creative Commons

B.G. Li,

Liu Li,

Haijiang Jia

и другие.

Journal of Stored Products Research, Год журнала: 2024, Номер 109, С. 102456 - 102456

Опубликована: Ноя. 13, 2024

Язык: Английский

Процитировано

3

A density-point network for dense tiny stored grain pest counting DOI

Runsheng Qi,

Rui Li, Jie Zhang

и другие.

Journal of Stored Products Research, Год журнала: 2025, Номер 111, С. 102536 - 102536

Опубликована: Янв. 19, 2025

Язык: Английский

Процитировано

0

Dmm-Yolo: Accurate Detection of Soil Fauna Using an Improved Algorithm DOI
JieHui Ke, Renbo Luo, Guoliang Xu

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Disease detection on exterior surfaces of buildings using deep learning in China DOI Creative Commons
You Chen, Dayao Li

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 12, 2025

Urban infrastructure, particularly in ageing cities, faces significant challenges maintaining building aesthetics and structural integrity. Traditional methods for detecting diseases on exteriors, such as manual inspections, are often inefficient, costly, prone to errors, leading incomplete assessments delayed maintenance actions. This study explores the application of advanced deep learning techniques accurately detect exterior surfaces buildings urban environments, aiming enhance detection efficiency accuracy while providing a real-time monitoring solution that can be widely implemented infrastructure health management. The research model improves feature extraction by integrating DenseNet blocks Swin-Transformer prediction heads, trained validated using dataset 289 high-resolution images collected from diverse environments China. Data augmentation improved model's robustness against varying conditions. proposed achieved high rate 84.42%, recall 77.83%, an F1 score 0.81, with speed 55 frames per second. These metrics demonstrate effectiveness identifying complex damage patterns, minute cracks, even within noisy significantly outperforming traditional methods. highlights potential transform strategies offering practical ultimately enhancing contributing practices timely interventions.

Язык: Английский

Процитировано

0

PestDet: A unified detecti framework for accurate and efficient stored-grain pest detection DOI Creative Commons

Jida Tian,

Muyi Sun, Huiling Zhou

и другие.

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103145 - 103145

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Rethinking feature representation and attention mechanisms in intelligent recognition of leaf pests and diseases in wheat DOI Creative Commons
Yuhan Zhang, Dongsheng Liu

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 5, 2025

Язык: Английский

Процитировано

0

Bio-inspired hexagonal MoO nano-pencil rods for agrarian-pest control DOI

S. Sreevidya,

Sushma Yadav,

Sunita Sanwaria

и другие.

Journal of the Taiwan Institute of Chemical Engineers, Год журнала: 2025, Номер 174, С. 106195 - 106195

Опубликована: Май 27, 2025

Язык: Английский

Процитировано

0

Overview of Pest Detection and Recognition Algorithms DOI Open Access

Boyu Guo,

Jianji Wang,

Minghui Guo

и другие.

Electronics, Год журнала: 2024, Номер 13(15), С. 3008 - 3008

Опубликована: Июль 30, 2024

Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, enhancing food production. With advancement artificial intelligence technologies, traditional pest detection recognition algorithms based on manually selected features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce primary neural network architectures evaluation metrics in field recognition. Subsequently, summarize widely used public datasets Following this, present various proposed recent years, providing detailed descriptions each algorithm their respective performance metrics. Finally, outline challenges that current encounter propose future research directions related

Язык: Английский

Процитировано

2

Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests DOI Creative Commons

Hengzhao Li,

Bowen Tan,

Leiming Sun

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(17), С. 7695 - 7695

Опубликована: Авг. 31, 2024

Efficient diagnosis of apple diseases and pests is crucial to the healthy development industry. However, existing single-source image-based classification methods have limitations due constraints input image information, resulting in low accuracy poor stability. Therefore, a method for disease pest areas based on multi-source fusion proposed this paper. Firstly, RGB images multispectral are obtained using drones construct an canopy dataset. Secondly, vegetation index selection saliency attention proposed, which uses multi-label ReliefF feature algorithm obtain importance scores indices, enabling automatic indices. Finally, area model named AMMFNet constructed, effectively combines advantages images, performs data-level data, channel mechanisms exploit complementary aspects between data. The experimental results demonstrated that achieves significant subset 92.92%, sample 85.43%, F1 value 86.21% dataset, representing improvements 8.93% 10.9% compared prediction only or images. also proved can provide technical support coarse-grained positioning orchards has good application potential planting

Язык: Английский

Процитировано

2

Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset DOI Creative Commons

Guilherme Pires Silva de Almeida,

Leonardo Nazário Silva dos Santos,

Leandro Rodrigues da Silva Souza

и другие.

Agronomy, Год журнала: 2024, Номер 14(10), С. 2194 - 2194

Опубликована: Сен. 24, 2024

One of the most challenging aspects agricultural pest control is accurate detection insects in crops. Inadequate measures for insect pests can seriously impact production corn and soybean plantations. In recent years, artificial intelligence (AI) algorithms have been extensively used detecting field. this line research, paper introduces a method to detect four key species that are predominant Brazilian agriculture. Our model relies on computer vision techniques, including You Only Look Once (YOLO) Detectron2, adapts them lightweight formats—TensorFlow Lite (TFLite) Open Neural Network Exchange (ONNX)—for resource-constrained devices. leverages two datasets: comprehensive one smaller sample comparison purposes. With setup, authors aimed at using these datasets evaluate performance models subsequently convert best-performing into TFLite ONNX formats, facilitating their deployment edge The results promising. Even worst-case scenario, where with reduced dataset was compared YOLOv9-gelan full dataset, precision reached 87.3%, accuracy achieved 95.0%.

Язык: Английский

Процитировано

2