ULLAH, Sajid, Klára PANZAROVÁ, Martin TRTÍLEK, Matej LEXA, Vojtěch MÁČALA, Thomas ALTMANN, Kerstin NEUMANN, Jan HEJÁTKO, Markéta PERNISOVÁ a Evgeny GLADILIN. High-throughput spike detection in greenhouse cultivated grain crops with attention mechanisms based deep learning models. Plant Phenomics. AAAS, 2024, roč. 2024, March, s. 1-11. ISSN 2643-6515. Dostupné z: https://dx.doi.org/10.34133/plantphenomics.0155.
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Základní údaje
Originální název High-throughput spike detection in greenhouse cultivated grain crops with attention mechanisms based deep learning models
Autoři ULLAH, Sajid (586 Pákistán, domácí), Klára PANZAROVÁ, Martin TRTÍLEK, Matej LEXA (703 Slovensko, domácí), Vojtěch MÁČALA (203 Česká republika, domácí), Thomas ALTMANN, Kerstin NEUMANN, Jan HEJÁTKO (203 Česká republika, domácí), Markéta PERNISOVÁ (203 Česká republika, domácí) a Evgeny GLADILIN.
Vydání Plant Phenomics, AAAS, 2024, 2643-6515.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 10201 Computer sciences, information science, bioinformatics
Stát vydavatele Spojené státy
Utajení není předmětem státního či obchodního tajemství
WWW URL
Impakt faktor Impact factor: 6.500 v roce 2022
Organizační jednotka Přírodovědecká fakulta
Doi http://dx.doi.org/10.34133/plantphenomics.0155
UT WoS 001231155500001
Klíčová slova anglicky spike detection; high-throughput image analysis; Attention networks; Deep Neural Networks
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Mgr. Markéta Pernisová, Ph.D., učo 11554. Změněno: 15. 6. 2024 13:13.
Anotace
Detection of spikes is the first important step towards image-based quantitative assessment of crop yield. However, spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of the mass of plant leaves that exhibit similar colors as spike regions. Consequently, accurate detection of grain spikes renders, in general, a non-trivial task even for advanced, state-of-the-art deep learning neural networks (DNNs). To improve pattern detection in spikes, we propose architectural changes to Faster-RCNN (FRCNN) by reducing feature extraction layers and introducing a global attention module. The performance of our extended FRCNN-A vs. conventional FRCNN was compared on images of different European wheat cultivars, including ’difficult’ bushy phenotypes from two different phenotyping facilities and optical setups. Our experimental results show that introduced architectural adaptations in FRCNN-A helped to improve spike detection accuracy in inner regions. The mAP of FRCNN and FRCNN-A on inner spikes is 76.0% and 81.0%, respectively, while on the state-of-the-art detection DNNs, Swin Transformer mAP is 83.0%. As a lightweight network, FRCNN-A is faster than FRCNN and Swin Transformer on both baseline and augmented training datasets. On the FastGAN augmented dataset, FRCNN achieved mAP of 84.24%, FRCNN-A 85.0%, and the Swin Transformer 89.45%. The increase in mAP of DNNs on the augmented datasets is proportional to the amount of the IPK original and augmented images. Overall, this study indicates a superior performance of attention mechanisms-based deep learning models in detecting small and subtle features of grain spikes.
Návaznosti
EF16_026/0008446, projekt VaVNázev: Integrace signálu a epigenetické reprogramování pro produktivitu rostlin
VytisknoutZobrazeno: 19. 7. 2024 12:20