Automated Author Profilekaur, upinder
kaur, upinder
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Plant Stress Phenotyping Dataset Dataset Description Overview This dataset is an extensive compilation designed for advancing the research in crop stress identification. It comprises a meticulously curated collection of 9,900 high-quality images and an additional 11,923 images that have been augmented to enrich the diversity of the dataset. The images encompass a range of biotic and abiotic stress factors affecting crop health.Biotic Stress Categories The biotic stress factors are categorized into the following types, with a total of 71 distinct features:Bacteria: 14 features Viral: 17 features Fungal: 16 features Weeds: 12 features Pests: 12 featuresAbiotic Stress Categories The dataset also includes 7 abiotic stress categories that capture environmental and climatic challenges crops face:Salt Stress Nutrition Stress Drought Stress Flooding Stress Cold Stress Heat Stress Climate StressQuality Control This ensures that the dataset serves as a robust resource for accurate and effective model training.This dataset is tailored for those who are delving into the realm of precision agriculture, specifically focusing on identifying various stress factors in crops through image analysis. It offers a rich source of data for training machine learning models to recognize and classify a spectrum of biotic and abiotic stresses in agriculture.
Authors
- kaur, upinder
Plant Stress Phenotyping Dataset Dataset Description Overview This dataset is an extensive compilation designed for advancing the research in crop stress identification. It comprises a meticulously curated collection of 9,900 high-quality images and an additional 11,923 images that have been augmented to enrich the diversity of the dataset. The images encompass a range of biotic and abiotic stress factors affecting crop health.Biotic Stress Categories The biotic stress factors are categorized into the following types, with a total of 71 distinct features:Bacteria: 14 features Viral: 17 features Fungal: 16 features Weeds: 12 features Pests: 12 featuresAbiotic Stress Categories The dataset also includes 7 abiotic stress categories that capture environmental and climatic challenges crops face:Salt Stress Nutrition Stress Drought Stress Flooding Stress Cold Stress Heat Stress Climate StressQuality Control This ensures that the dataset serves as a robust resource for accurate and effective model training.This dataset is tailored for those who are delving into the realm of precision agriculture, specifically focusing on identifying various stress factors in crops through image analysis. It offers a rich source of data for training machine learning models to recognize and classify a spectrum of biotic and abiotic stresses in agriculture.
Authors
- kaur, upinder