Browsing by Author "Sonah, Humira"

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    Pinpointing genomic regions and candidate genes associated with seed oil and protein content in soybean through an integrative transcriptomic and QTL meta-analysis
    (Cells, 2023) Kumar, Virender; Goyal, Vinod; Mandlik, Rushil; Kumawat, Surbhi; Joy, Roy; Sudhakaran, Sreeja; Padalkar, Gunashri; Rana, Nikita; Deshmukh, Rupesh; Sharma, Tilak Raj; Sonah, Humira
    Soybean with enriched nutrients has emerged as a prominent source of edible oil and protein. In the present study, a meta-analysis was performed by integrating quantitative trait loci (QTLs) information, region-specific association and transcriptomic analysis. Analysis of about a thousand QTLs previously identified in soybean helped to pinpoint 14 meta-QTLs for oil and 16 meta-QTLs for protein content. Similarly, region-specific association analysis using whole genome re-sequenced data was performed for the most promising meta-QTL on chromosomes 6 and 20. Only 94 out of 468 genes related to fatty acid and protein metabolic pathways identified within the meta- QTL region were found to be expressed in seeds. Allele mining and haplotyping of these selected genes were performed using whole genome resequencing data. Interestingly, a significant haplotypic association of some genes with oil and protein content was observed, for instance, in the case of FAD2- 1B gene, an average seed oil content of 20.22% for haplotype 1 compared to 15.52% for haplotype 5 was observed. In addition, the mutation S86F in the FAD2-1B gene produces a destabilizing effect of (DDG Stability) 􀀀0.31 kcal/mol. Transcriptomic analysis revealed the tissue-specific expression of candidate genes. Based on their higher expression in seed developmental stages, genes such as sugar transporter, fatty acid desaturase (FAD), lipid transporter, major facilitator protein and amino acid transporter can be targeted for functional validation. The approach and information generated in the present study will be helpful in the map-based cloning of regulatory genes, as well as for marker-assisted breeding in soybean.
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    Precision diagnosis of tomato diseases for sustainable agriculture through deep learning approach with hybrid data augmentation
    (2025) Sonah, Humira
    Tomato is a key crop in global agriculture, yet it faces yield and quality challenges due to various diseases. Traditional disease identification methods are slow and require expertise, limiting their practicality in large-scale farming. Integrating automated disease detection with precision agriculture provides a timely, accurate diag nosis, promoting sustainable practices. However, the scarcity of real-world data hampers effectiveness. To address this issue, data augmentation techniques simulate variations in farm images, enriching datasets for improved detection of diseases. This investigation aims to identify seven different tomato diseases, such as bacterial spot, early blight, late blight, and others, while also detecting healthy plant leaves. Unlike previous studies that relied on the controlled PlantVillage dataset, this study utilizes the real-world PlantDoc dataset. The study addresses different challenges faced throughout the model development process, like data scarcity and imbalances. A hybrid data augmentation technique is introduced to increase the dataset size from 737 images to 6696 images, which improves the accuracy and robustness of the computer vision model. The study employs the YOLOv8n deep convolutional neural network, achieving 96.5 % mAP, 97 % precision, 93.8 % recall, and 95 % F1 score. The results demonstrate a significant improvement in disease detection, addressing challenges from inadequate datasets and advancing AI-driven precision agriculture. The proposed YOLOv8n model has the po tential to be applied beyond its current scope by training it on datasets of other crops. The model can learn and generalize the unique image features associated with various crop types, expanding its utility in agricultural applications. This flexibility allows the model to detect and classify plant characteristics, diseases, or pests across different crops, enabling its use in diverse agricultural environments. As a result, the YOLOv8n model could serve as a robust tool for precision farming, helping to optimize crop management and enhance productivity on a broader scale.