Lightweight tea bud detection method based on improved YOLOv5 DOI Creative Commons
Kun Zhang,

Bohan Yuan,

Jingying Cui

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 28, 2024

Abstract Tea bud detection technology is of great significance in realizing automated and intelligent plucking tea buds. This study proposes a lightweight identification model based on modified Yolov5 to increase the picking accuracy labor efficiency while lowering deployment pressure mobile terminals. The following methods are used make improvements: backbone network CSPDarknet-53 YOLOv5 replaced with EfficientNetV2 feature extraction reduce number parameters floating-point operations model; neck YOLOv5, Ghost module introduced construct ghost convolution C3ghost further replacing upsampling CARAFE can aggregate contextual information within larger sensory field improve mean average precision detecting results show that improved has 85.79%, only 4.14 M parameters, 5.02G operations. reduced by 40.94% 68.15%, respectively, when compared original model, but raised 1.67% points. advantages this paper’s algorithm shot be noticed comparing it other YOLO series algorithms. paper effectively detect buds lightweight, provide corresponding theoretical research for tea-picking robots.

Язык: Английский

Performance Evaluation of YOLO Models in Plant Disease Detection DOI Creative Commons
Usman Ali, Maizatul Akmar Ismail, Riyaz Ahamed Ariyaluran Habeeb

и другие.

Journal of Informatics and Web Engineering, Год журнала: 2024, Номер 3(2), С. 199 - 211

Опубликована: Июнь 13, 2024

Plant diseases significantly impact global agriculture, leading to substantial production losses and economic consequences. Timely disease detection can enhance crop yield, optimize resource utilization, reduce costs, mitigate environmental effects, ultimately ensuring high-quality food production. Deep learning, specifically computer vision-based techniques, have proven invaluable in tasks like image classification, segmentation, object detection. Learning techniques such as You Only Look Once (YOLO) models are state of the art neural network algorithms used for accurate In this study, YOLOv5, YOLOv7 YOLOv8 were trained on CCL’20 dataset citrus Data augmentation translation, scaling, flip, mosaic augmentations implemented improve models’ performance during training phase. The model was evaluated using metric Mean Average Precision at 50% 95% Intersection over Union score i.e. mAP@50-95. results show that performs better than other variants offers significant improvements benchmark from previous studies. final hyper-parameter tuned achieved 96.1% mAP@50-95 testing data 95.3%, 96.0% 97.0% Anthracnose, Melanose Bacterial Brown Spot diseases, respectively. able detect single multiple instances same or different an showing potential recent YOLO models. is deployed Roboflow platform.

Язык: Английский

Процитировано

5
Grape Guard: A YOLO-based mobile application for detecting grape leaf diseases1 DOI Creative Commons

Sajib Bin Mamun,

Israt Jahan Payel,

Md Taimur Ahad

и другие.

Journal of Electronic Science and Technology, Год журнала: 2025, Номер unknown, С. 100300 - 100300

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
A Detection Method for Sweet Potato Leaf Spot Disease and Leaf-Eating Pests DOI Creative Commons
Kang Xu,

Hou Yan,

Wenbin Sun

и другие.

Agriculture, Год журнала: 2025, Номер 15(5), С. 503 - 503

Опубликована: Фев. 26, 2025

Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy manual dependence, while deep learning-based target can achieve an efficient accurate detection. This paper proposed leaf method SPLDPvB, as well a low-complexity version SPLDPvT, to identification spots pests, such hawk moth wheat moth. First, residual module containing three depthwise separable convolutional layers skip connection was effectively retain key feature information. Then, extraction integrating attention mechanism designed significantly improve capability. Finally, in model architecture, only structure backbone network decoupling head combination retained, traditional replaced by module, which greatly reduced complexity. The experimental results showed that mAP0.5 mAP0.5:0.95 SPLDPvB were 88.7% 74.6%, respectively, number parameters amount calculation 1.1 M 7.7 G, respectively. Compared with YOLOv11S, increased 2.3% 2.8%, 88.2% 63.8%, achieves higher complexity, demonstrating excellent performance detecting pests diseases. realizes automatic diseases provides technical guidance for spraying

Язык: Английский

Процитировано

0
WHEAT GRAIN APPEARANCE QUALITY DETECTION BASED ON IMPROVED YOLOv8n DOI

Qingzhong KONG,

Na Ma

INMATEH Agricultural Engineering, Год журнала: 2025, Номер unknown, С. 356 - 365

Опубликована: Апрель 10, 2025

Wheat grains are a common type of cereal variety, and due to their large quantity high demand, traditional manual quality inspection requires significant amount labor with potentially inadequate results. To address this issue, study focuses on intact, damaged, moldy, shriveled wheat grains, establishes YOLO-wheat automatic grain appearance detection model. First, number sample images were collected, preprocessed, annotated. Next, YOLOv5n, YOLOv8n, YOLOv10n object models established, the optimal model YOLOv8n was selected as base for detection. further improve performance, Dilation-wise Residual (DWR) module integrated into network structure enhance feature extraction from expandable receptive field in higher layers network. Additionally, TripletAttention attention mechanism introduced, improved named YOLO-wheat. Experimental results showed that achieved an mAP value 91.3% detection, representing 4.3% improvement compared previous version. This provides technical support

Язык: Английский

Процитировано

0
LT-DeepLab: an improved DeepLabV3+ cross-scale segmentation algorithm for Zanthoxylum bungeanum Maxim leaf-trunk diseases in real-world environments DOI Creative Commons
Tao Yang, Jingjing Wei,

Yongjun Xiao

и другие.

Frontiers in Plant Science, Год журнала: 2024, Номер 15

Опубликована: Окт. 22, 2024

Introduction Zanthoxylum bungeanum Maxim is an economically significant crop in Asia, but large-scale cultivation often threatened by frequent diseases, leading to yield declines. Deep learning-based methods for disease recognition have emerged as a vital research area agriculture. Methods This paper presents novel model, LT-DeepLab, the semantic segmentation of leaf spot (folium macula), rust, frost damage (gelu damnum), and diseased leaves trunks complex field environments. The proposed model enhances DeepLabV3+ with innovative Fission Depth Separable CRCC Atrous Spatial Pyramid Pooling module, which reduces structural parameters module improves cross-scale extraction capability. Incorporating Criss-Cross Attention Convolutional Block Module provides complementary boost channel feature extraction. Additionally, deformable convolution low-dimensional features, Fully Network auxiliary header integrated optimize network enhance accuracy without increasing parameter count. Results LT-DeepLab mean Intersection over Union (mIoU) 3.59%, Pixel Accuracy (mPA) 2.16%, Overall (OA) 0.94% compared baseline DeepLabV3+. It also computational demands 11.11% decreases count 16.82%. Discussion These results indicate that demonstrates excellent capabilities environments while maintaining high efficiency, offering promising solution improving management efficiency.

Язык: Английский

Процитировано

1
Lightweight tea bud detection method based on improved YOLOv5 DOI Creative Commons
Kun Zhang,

Bohan Yuan,

Jingying Cui

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Дек. 28, 2024

Abstract Tea bud detection technology is of great significance in realizing automated and intelligent plucking tea buds. This study proposes a lightweight identification model based on modified Yolov5 to increase the picking accuracy labor efficiency while lowering deployment pressure mobile terminals. The following methods are used make improvements: backbone network CSPDarknet-53 YOLOv5 replaced with EfficientNetV2 feature extraction reduce number parameters floating-point operations model; neck YOLOv5, Ghost module introduced construct ghost convolution C3ghost further replacing upsampling CARAFE can aggregate contextual information within larger sensory field improve mean average precision detecting results show that improved has 85.79%, only 4.14 M parameters, 5.02G operations. reduced by 40.94% 68.15%, respectively, when compared original model, but raised 1.67% points. advantages this paper’s algorithm shot be noticed comparing it other YOLO series algorithms. paper effectively detect buds lightweight, provide corresponding theoretical research for tea-picking robots.

Язык: Английский

Процитировано

0