270 related articles for article (PubMed ID: 34833515)
1. Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods.
Ullah S; Henke M; Narisetti N; Panzarová K; Trtílek M; Hejatko J; Gladilin E
Sensors (Basel); 2021 Nov; 21(22):. PubMed ID: 34833515
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot).
Narisetti N; Henke M; Neumann K; Stolzenburg F; Altmann T; Gladilin E
Front Plant Sci; 2022; 13():906410. PubMed ID: 35909752
[TBL] [Abstract][Full Text] [Related]
5. High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models.
Ullah S; Panzarová K; Trtílek M; Lexa M; Máčala V; Neumann K; Altmann T; Hejátko J; Pernisová M; Gladilin E
Plant Phenomics; 2024; 6():0155. PubMed ID: 38476818
[TBL] [Abstract][Full Text] [Related]
6. Automated Spike Detection in Diverse European Wheat Plants Using Textural Features and the Frangi Filter in 2D Greenhouse Images.
Narisetti N; Neumann K; Röder MS; Gladilin E
Front Plant Sci; 2020; 11():666. PubMed ID: 32655586
[TBL] [Abstract][Full Text] [Related]
7. A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping.
Henke M; Junker A; Neumann K; Altmann T; Gladilin E
Plant Methods; 2020; 16():95. PubMed ID: 32670387
[TBL] [Abstract][Full Text] [Related]
8. Detecting spikes of wheat plants using neural networks with Laws texture energy.
Qiongyan L; Cai J; Berger B; Okamoto M; Miklavcic SJ
Plant Methods; 2017; 13():83. PubMed ID: 29046709
[TBL] [Abstract][Full Text] [Related]
9.
Sadeghi-Tehran P; Virlet N; Ampe EM; Reyns P; Hawkesford MJ
Front Plant Sci; 2019; 10():1176. PubMed ID: 31616456
[TBL] [Abstract][Full Text] [Related]
10. Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.
Artemenko NV; Genaev MA; Epifanov RU; Komyshev EG; Kruchinina YV; Koval VS; Goncharov NP; Afonnikov DA
Front Plant Sci; 2023; 14():1336192. PubMed ID: 38283969
[TBL] [Abstract][Full Text] [Related]
11. Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping.
Zhan Y; Zhou Y; Bai G; Ge Y
Sensors (Basel); 2024 May; 24(11):. PubMed ID: 38894212
[TBL] [Abstract][Full Text] [Related]
12. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.
Tong N; Gou S; Yang S; Cao M; Sheng K
Med Phys; 2019 Jun; 46(6):2669-2682. PubMed ID: 31002188
[TBL] [Abstract][Full Text] [Related]
13. Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley.
Ling Y; Zhao Q; Liu W; Wei K; Bao R; Song W; Nie X
Plant Methods; 2023 Oct; 19(1):115. PubMed ID: 37891590
[TBL] [Abstract][Full Text] [Related]
14. Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.
Fernández-Campos M; Huang YT; Jahanshahi MR; Wang T; Jin J; Telenko DEP; Góngora-Canul C; Cruz CD
Front Plant Sci; 2021; 12():673505. PubMed ID: 34220894
[TBL] [Abstract][Full Text] [Related]
15. ThelR547v1-An Asymmetric Dilated Convolutional Neural Network for Real-time Semantic Segmentation of Horticultural Crops.
Islam MP; Hatou K; Aihara T; Kawahara M; Okamoto S; Senoo S; Sumire K
Sensors (Basel); 2022 Nov; 22(22):. PubMed ID: 36433404
[TBL] [Abstract][Full Text] [Related]
16. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Tong N; Gou S; Yang S; Ruan D; Sheng K
Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks.
Xiong H; Cao Z; Lu H; Madec S; Liu L; Shen C
Plant Methods; 2019; 15():150. PubMed ID: 31857821
[TBL] [Abstract][Full Text] [Related]
19. Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation.
Hussein BR; Malik OA; Ong WH; Slik JWF
Sensors (Basel); 2021 Jul; 21(13):. PubMed ID: 34283110
[TBL] [Abstract][Full Text] [Related]
20. Automated and accurate segmentation of leaf venation networks via deep learning.
Xu H; Blonder B; Jodra M; Malhi Y; Fricker M
New Phytol; 2021 Jan; 229(1):631-648. PubMed ID: 32964424
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
