Dataset Information

SeedQuant: a deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds.

ABSTRACT: Witchweeds (Striga spp.) and broomrapes (Orobanchaceae and Phelipanche spp.) are root parasitic plants that infest many crops in warm and temperate zones, causing enormous yield losses and endangering global food security. Seeds of these obligate parasites require rhizospheric, host-released stimulants to germinate, which opens up possibilities for controlling them by applying specific germination inhibitors or synthetic stimulants that induce lethal germination in the host's absence. To determine their effect on germination, root exudates or synthetic stimulants/inhibitors are usually applied to parasitic seeds in in vitro bioassays, followed by assessment of germination ratios. Although these protocols are very sensitive, the germination recording process is laborious, representing a challenge for researchers and impeding high-throughput screens. Here, we developed an automatic seed census tool to count and discriminate germinated seeds (GS) from non-GS. We combined deep learning, a powerful data-driven framework that can accelerate the procedure and increase its accuracy, for object detection with computer vision latest development based on the Faster Region-based Convolutional Neural Network algorithm. Our method showed an accuracy of 94% in counting seeds of Striga hermonthica and reduced the required time from approximately 5 min to 5 s per image. Our proposed software, SeedQuant, will be of great help for seed germination bioassays and enable high-throughput screening for germination stimulants/inhibitors. SeedQuant is an open-source software that can be further trained to count different types of seeds for research purposes.

SUBMITTER: Braguy J

PROVIDER: S-EPMC8260127 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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SeedQuant: a deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds.

Braguy Justine J Ramazanova Merey M Giancola Silvio S Jamil Muhammad M Kountche Boubacar A BA Zarban Randa R Felemban Abrar A Wang Jian You JY Lin Pei-Yu PY Haider Imran I Zurbriggen Matias M Ghanem Bernard B Al-Babili Salim S

Plant physiology 20210701 3

Witchweeds (Striga spp.) and broomrapes (Orobanchaceae and Phelipanche spp.) are root parasitic plants that infest many crops in warm and temperate zones, causing enormous yield losses and endangering global food security. Seeds of these obligate parasites require rhizospheric, host-released stimulants to germinate, which opens up possibilities for controlling them by applying specific germination inhibitors or synthetic stimulants that induce lethal germination in the host's absence. To determi ...[more]

PMID: 33856485

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Project description:IntroductionAccurate and rapid diagnosis is crucial for the effective treatment of parasitosis. Traditional etiological methods, especially microscopic examination, are time-consuming, labor-intensive, and prone to false or missed detections. In response to these challenges, this study explores the use of artificial intelligence (AI) for the detection and classification of human parasite eggs through the YOLOv4 deep learning object detection algorithm.MethodsEggs from species such as Ascaris lumbricoides (A. lumbricoides), Trichuris trichiura (T. trichiura), Enterobius vermicularis (E. vermicularis), Ancylostoma duodenale (A. duodenale), Schistosoma japonicum (S. japonicum), Paragonimus westermani (P. westermani), Fasciolopsis buski (F. buski), Clonorchis sinensis (C. sinensis), and Taenia spp. (T. spp.) were collected and prepared as both single species and mixed egg smears. These samples were photographed under a light microscope and analyzed using the YOLO (You Only Look Once) v4 model.ResultsThe model demonstrated high recognition accuracy, achieving 100% for Clonorchis sinensis and Schistosoma japonicum, with slightly lower accuracies for other species such as E. vermicularis (89.31%), F. buski (88.00%), and T. trichiura (84.85%). For mixed helminth eggs, the recognition accuracy rates arrived at Group 1 (98.10, 95.61%), Group 2 (94.86, 93.28 and 91.43%), and Group 3 (93.34 and 75.00%), indicating the platform's robustness but also highlighting areas for improvement in complex diagnostic scenarios.DiscussionThe results show that this AI-assisted platform significantly reduces reliance on professional expertise while maintaining real-time efficiency and high accuracy, offering a powerful tool for the diagnosis and treatment of parasitosis. With further optimization, such as expanding training datasets and refining recognition algorithms, this AI system could become a key resource in both clinical and public health efforts to combat parasitic infections.

Dataset Information

SeedQuant: a deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds.

Publications

SeedQuant: a deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds.

Similar Datasets

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