106 related articles for article (PubMed ID: 38962247)
1. Improved YOLO-FastestV2 wheat spike detection model based on a multi-stage attention mechanism with a LightFPN detection head.
Qing S; Qiu Z; Wang W; Wang F; Jin X; Ji J; Zhao L; Shi Y
Front Plant Sci; 2024; 15():1411510. PubMed ID: 38962247
[TBL] [Abstract][Full Text] [Related]
2. Detection method of wheat spike improved YOLOv5s based on the attention mechanism.
Zang H; Wang Y; Ru L; Zhou M; Chen D; Zhao Q; Zhang J; Li G; Zheng G
Front Plant Sci; 2022; 13():993244. PubMed ID: 36247573
[TBL] [Abstract][Full Text] [Related]
3. High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model.
Zhang Y; Li M; Ma X; Wu X; Wang Y
Front Plant Sci; 2022; 13():787852. PubMed ID: 35720576
[TBL] [Abstract][Full Text] [Related]
4. SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging.
Misra T; Arora A; Marwaha S; Chinnusamy V; Rao AR; Jain R; Sahoo RN; Ray M; Kumar S; Raju D; Jha RR; Nigam A; Goel S
Plant Methods; 2020; 16():40. PubMed ID: 32206080
[TBL] [Abstract][Full Text] [Related]
5. Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis.
Yan J; Zhao J; Cai Y; Wang S; Qiu X; Yao X; Tian Y; Zhu Y; Cao W; Zhang X
Plant Methods; 2023 May; 19(1):46. PubMed ID: 37179312
[TBL] [Abstract][Full Text] [Related]
6. Oriented feature pyramid network for small and dense wheat heads detection and counting.
Yu J; Chen W; Liu N; Fan C
Sci Rep; 2024 Apr; 14(1):8106. PubMed ID: 38582913
[TBL] [Abstract][Full Text] [Related]
7. YOLO-SCL: a lightweight detection model for citrus psyllid based on spatial channel interaction.
Lyu S; Zhou X; Li Z; Liu X; Chen Y; Zeng W
Front Plant Sci; 2023; 14():1276833. PubMed ID: 38023942
[TBL] [Abstract][Full Text] [Related]
8. A lightweight network for improving wheat ears detection and counting based on YOLOv5s.
Shen X; Zhang C; Liu K; Mao W; Zhou C; Yao L
Front Plant Sci; 2023; 14():1289726. PubMed ID: 38164250
[TBL] [Abstract][Full Text] [Related]
9. Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model.
Zhang J; Min A; Steffenson BJ; Su WH; Hirsch CD; Anderson J; Wei J; Ma Q; Yang C
Front Plant Sci; 2022; 13():834938. PubMed ID: 35222491
[TBL] [Abstract][Full Text] [Related]
10. OD-YOLO: Robust Small Object Detection Model in Remote Sensing Image with a Novel Multi-Scale Feature Fusion.
Bu Y; Ye H; Tie Z; Chen Y; Zhang D
Sensors (Basel); 2024 Jun; 24(11):. PubMed ID: 38894387
[TBL] [Abstract][Full Text] [Related]
11. Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort.
Wu T; Zhong S; Chen H; Geng X
Sensors (Basel); 2023 May; 23(10):. PubMed ID: 37430792
[TBL] [Abstract][Full Text] [Related]
12. T-YOLO: a lightweight and efficient detection model for nutrient buds in complex tea-plantation environments.
Bai B; Wang J; Li J; Yu L; Wen J; Han Y
J Sci Food Agric; 2024 Aug; 104(10):5698-5711. PubMed ID: 38372581
[TBL] [Abstract][Full Text] [Related]
13. MGA-YOLO: A lightweight one-stage network for apple leaf disease detection.
Wang Y; Wang Y; Zhao J
Front Plant Sci; 2022; 13():927424. PubMed ID: 36072327
[TBL] [Abstract][Full Text] [Related]
14. Segmentation and counting of wheat spike grains based on deep learning and textural feature.
Xu X; Geng Q; Gao F; Xiong D; Qiao H; Ma X
Plant Methods; 2023 Aug; 19(1):77. PubMed ID: 37528413
[TBL] [Abstract][Full Text] [Related]
15. EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5.
Zhu S; Ma W; Wang J; Yang M; Wang Y; Wang C
Front Plant Sci; 2023; 14():1120724. PubMed ID: 36909428
[TBL] [Abstract][Full Text] [Related]
16. A wheat spike detection method based on Transformer.
Zhou Q; Huang Z; Zheng S; Jiao L; Wang L; Wang R
Front Plant Sci; 2022; 13():1023924. PubMed ID: 36340370
[TBL] [Abstract][Full Text] [Related]
17. YOLO-LWNet: A Lightweight Road Damage Object Detection Network for Mobile Terminal Devices.
Wu C; Ye M; Zhang J; Ma Y
Sensors (Basel); 2023 Mar; 23(6):. PubMed ID: 36991979
[TBL] [Abstract][Full Text] [Related]
18. Weed detection and recognition in complex wheat fields based on an improved YOLOv7.
Wang K; Hu X; Zheng H; Lan M; Liu C; Liu Y; Zhong L; Li H; Tan S
Front Plant Sci; 2024; 15():1372237. PubMed ID: 38978522
[TBL] [Abstract][Full Text] [Related]
19. Lightweight and efficient neural network with SPSA attention for wheat ear detection.
Dong Y; Liu Y; Kang H; Li C; Liu P; Liu Z
PeerJ Comput Sci; 2022; 8():e931. PubMed ID: 35494849
[TBL] [Abstract][Full Text] [Related]
20. Detection and analysis of wheat spikes using Convolutional Neural Networks.
Hasan MM; Chopin JP; Laga H; Miklavcic SJ
Plant Methods; 2018; 14():100. PubMed ID: 30459822
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
