MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields DOI Creative Commons
Yong Shi, Ruijie Xu, Zhiquan Qi

et al.

Applied Artificial Intelligence, Journal Year: 2025, Volume and Issue: 39(1)

Published: March 17, 2025

Language: Английский

Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review DOI
Chetan Badgujar,

Alwin Poulose,

Hao Gan

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 223, P. 109090 - 109090

Published: May 31, 2024

Language: Английский

Citations

56
UPFormer: U-sharped Perception lightweight Transformer for segmentation of field grape leaf diseases DOI
Xinxin Zhang, Fei Li, H. Zheng

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 249, P. 123546 - 123546

Published: Feb. 21, 2024

Language: Английский

Citations

15
Efficient Tobacco Pest Detection in Complex Environments Using an Enhanced YOLOv8 Model DOI Creative Commons
Daozong Sun, Kai Zhang,

Hongsheng Zhong

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(3), P. 353 - 353

Published: Feb. 22, 2024

Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco identification leveraging enhancements YOLOv8 technology. Using large (YOLOv8l) as base, neck layer original is replaced with an asymptotic feature pyramid (AFPN) reduce model parameters. A SimAM attention mechanism, which does not require additional parameters, incorporated improve model’s ability extract features. The backbone network’s C2f VoV-GSCSP module computational requirements. Experiments show improved achieves high overall performance. Compared model, parameters and GFLOPs are reduced by 52.66% 19.9%, respectively, while [email protected] 1%, recall 2.7%, precision 2.4%. Further comparison popular models YOLOv5 medium (YOLOv5m), YOLOv6 (YOLOv6m), (YOLOv8m) shows has highest accuracy lightest detecting four common pests, optimal proposed facilitates precise, instantaneous recognition other crops, securing high-accuracy, comprehensive identification.

Language: Английский

Citations

13
An efficient detection method for litchi fruits in a natural environment based on improved YOLOv7-Litchi DOI
Can Li,

Jiaquan Lin,

Zhao Li

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108605 - 108605

Published: Jan. 20, 2024

Language: Английский

Citations

12
Detection network for multi-size and multi-target tea bud leaves in the field of view via improved YOLOv7 DOI

Tianci Chen,

Haoxin Li, Jiazheng Chen

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 218, P. 108700 - 108700

Published: Feb. 9, 2024

Language: Английский

Citations

10
Lightweight CNN combined with knowledge distillation for the accurate determination of black tea fermentation degree DOI
Zezhong Ding, Chongshan Yang, Bin Hu

et al.

Food Research International, Journal Year: 2024, Volume and Issue: 194, P. 114929 - 114929

Published: Aug. 18, 2024

Language: Английский

Citations

9
Instance Segmentation and 3D Pose Estimation of Tea Bud Leaves for Autonomous Harvesting Robots DOI Creative Commons

Haoxin Li,

Tianci Chen,

Yingmei Chen

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(2), P. 198 - 198

Published: Jan. 17, 2025

In unstructured tea garden environments, accurate recognition and pose estimation of bud leaves are critical for autonomous harvesting robots. Due to variations in imaging distance, exhibit diverse scale characteristics camera views, which significantly complicates the process. This study proposes a method using an RGB-D precise leaves. The approach first constructs leaves, followed by dynamic weight strategy achieve adaptive estimation. Quantitative experiments demonstrate that instance segmentation model achieves mAP@50 92.0% box detection 91.9% mask detection, improving 3.2% 3.4%, respectively, compared YOLOv8s-seg model. results indicate maximum angular error 7.76°, mean 3.41°, median 3.69°, absolute deviation 1.42°. corresponding distance errors 8.60 mm, 2.83 2.57 0.81 further confirming accuracy robustness proposed method. These can be applied environments non-destructive with bud-leave

Language: Английский

Citations

1
A lightweight pineapple detection network based on YOLOv7-tiny for agricultural robot system DOI
Jiehao Li, Chenglin Li,

Shan Zeng

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 109944 - 109944

Published: Jan. 23, 2025

Language: Английский

Citations

1
YOLO-TBD: Tea Bud Detection with Triple-Branch Attention Mechanism and Self-Correction Group Convolution DOI Creative Commons
Zhongyuan Liu, Zhuo Li, Chunwang Dong

et al.

Industrial Crops and Products, Journal Year: 2025, Volume and Issue: 226, P. 120607 - 120607

Published: Feb. 1, 2025

Language: Английский

Citations

1
A lightweight palm fruit detection network for harvesting equipment integrates binocular depth matching DOI
Jiehao Li, Tao Zhang,

Qunfei Luo

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 233, P. 110061 - 110061

Published: Feb. 27, 2025

Language: Английский

Citations

1