Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping

Tamvakis, Petros N.; Kiourt, Chairi; Solomou, Alexandra D.; Ioannakis, George; Tsirliganis, Nestoras C.

doi:10.1016/j.ifacol.2022.11.119

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…However, the cost of expensive equipment makes it difficult to deploy this method on a large scale in the laboratory. Deep learning techniques, which have gradually become a hot research direction in recent years, can solve most of the problems that are difficult to overcome in traditional image processing methods ( Baar et al., 2022 ; Sapoukhina et al., 2022 ; Tamvakis et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning

Liao

et al. 2023

Front. Plant Sci.

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IntroductionLeaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production.MethodIn this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods).ResultsThe average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed.DiscussionThe growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed.

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Section: Discussionmentioning

confidence: 99%

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning

Liao

et al. 2023

Front. Plant Sci.

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“…In biological microscopy, deep learning has demonstrated promising performance in a range of segmentation applications including semantic segmentation of human oocyte (Targosz et al, 2021), semantic and instance segmentation for cell nuclei (Caicedo et al, 2019) and semantic segmentation potato tuber (Biswas and Barma, 2020). Examples of plant phenotyping applications include semantic and instance segmentation for plant leaf detection and counting (Aich and Stavness, 2017;Giuffrida et al, 2018;Itzhaky et al, 2018;Jiang et al, 2019;Fan et al, 2022), semantic and instance segmentation for crop phenotyping (Jiang and Li, 2020), grapevine leaf semantic segmentation (Tamvakis et al, 2022), barley seed detection from instance segmentation (Toda et al, 2020) and many other applications (Kolhar and Jagtap, 2023).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in mature Pinus radiata D. Don. somatic embryo images

Davidson,

Saggese,

Krajňáková

2024

Front. Plant Sci.

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In commercial forestry and large-scale plant propagation, the utilization of artificial intelligence techniques for automated somatic embryo analysis has emerged as a highly valuable tool. Notably, image segmentation plays a key role in the automated assessment of mature somatic embryos. However, to date, the application of Convolutional Neural Networks (CNNs) for segmentation of mature somatic embryos remains unexplored. In this study, we present a novel application of CNNs for delineating mature somatic conifer embryos from background and residual proliferating embryogenic tissue and differentiating various morphological regions within the embryos. A semantic segmentation CNN was trained to assign pixels to cotyledon, hypocotyl, and background regions, while an instance segmentation network was trained to detect individual cotyledons for automated counting. The main dataset comprised 275 high-resolution microscopic images of mature Pinus radiata somatic embryos, with 42 images reserved for testing and validation sets. The evaluation of different segmentation methods revealed that semantic segmentation achieved the highest performance averaged across classes, achieving F1 scores of 0.929 and 0.932, with IoU scores of 0.867 and 0.872 for the cotyledon and hypocotyl regions respectively. The instance segmentation approach demonstrated proficiency in accurate detection and counting of the number of cotyledons, as indicated by a mean squared error (MSE) of 0.79 and mean absolute error (MAE) of 0.60. The findings highlight the efficacy of neural network-based methods in accurately segmenting somatic embryos and delineating individual morphological parts, providing additional information compared to previous segmentation techniques. This opens avenues for further analysis, including quantification of morphological characteristics in each region, enabling the identification of features of desirable embryos in large-scale production systems. These advancements contribute to the improvement of automated somatic embryogenesis systems, facilitating efficient and reliable plant propagation for commercial forestry applications.

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“…In biological microscopy, deep learning has demonstrated promising performance in a range of segmentation applications including semantic segmentation of human oocyte (Targosz et al, 2021), semantic and instance segmentation for cell nuclei (Caicedo et al, 2019) and semantic segmentation potato tuber (Biswas and Barma, 2020). Examples of plant phenotyping applications include semantic and instance segmentation for plant leaf detection and counting (Aich and Stavness, 2017;Giuffrida et al, 2018;Itzhaky et al, 2018;Jiang et al, 2019;Fan et al, 2022), semantic and instance segmentation for crop phenotyping , grapevine leaf semantic segmentation (Tamvakis et al, 2022), barley seed detection from instance segmentation (Toda et al, 2020) and many other applications (Kolhar and Jagtap, 2023).…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Orlov

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping

Cited by 11 publications

References 22 publications

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning

Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in mature Pinus radiata D. Don. somatic embryo images

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

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