Browsing by Person "Kienbaum, Lydia"
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Publication Combining ability and hybrid breeding in Tunisian melon (Cucumis melo L.) for fruit traits(2024) Chikh-Rouhou, Hela; Kienbaum, Lydia; Gharib, Amani H. A. M.; Fayos, Oreto; Garcés-Claver, Ana; Chikh-Rouhou, Hela; Regional Research Centre on Horticulture and Organic Agriculture (CRRHAB), LR21AGR03, University of Sousse, Sousse 4042, Tunisia; Kienbaum, Lydia; Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599 Stuttgart, Germany; Gharib, Amani H. A. M.; Department of Vegetable, Medicinal and Aromatic Plant Breeding, Horticulture Research Institute (HRI), Agricultural Research Center (ARC), Giza 12613, Egypt; Fayos, Oreto; Department of Plant Science, Agrifood Research and Technology Centre of Aragon (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain; Garcés-Claver, Ana; Department of Plant Science, Agrifood Research and Technology Centre of Aragon (CITA), Avda. Montañana 930, 50059 Zaragoza, Spain; Wang, Huasen; Miao, LiA half-diallel cross study of seven melon inbred lines was carried out. The seven parents and their 21 F1 hybrids were evaluated for precocity of maturity, average weight per fruit, and fruit quality (fruit size, rind thickness, and soluble solids). The Diallel analysis was investigated for breeding values of these melon genotypes via general and specific combining ability, relationships between general and specific combining ability, and heterosis for the evaluated traits. The analysis of variance of the traits evaluated indicated highly significant differences among genotypes, suggesting the presence of adequate genetic variation for breeding. Additive genetic effects were most important with respect to fruit weight, while genetic dominance and epistasis effects mainly controlled fruit quality traits (fruit size, rind thickness, and TSS). Parent 1 (P1) and parent 3 (P3) had significant positive general combining ability effects for fruit weight. Also, P3 had positive general combining ability effects for fruit length and diameter, and cavity diameter. P3 was found to show maximum significant GCA in the desirable direction for all the traits except for TSS. Evaluation of heterosis (%) revealed that hybrid P1 × P3 can be considered as the best-performing hybrid for average fruit weight, TSS, and precocity, which also exhibited the highest positive and significant SCA effect for these traits. These results suggested that, among the melon genotypes studied, there is the potential to generate superior new varieties in hybrid production.Publication DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics(2021) Kienbaum, Lydia; Correa Abondano, Miguel; Blas, Raul; Schmid, KarlBackground: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy (r = 0.99). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.