Omkar2118/datasets huggingface dataset:
CCPT
Full name: Conceptual Combination with Property Type

CCPT is a dataset containing 12.3K triplets of noun phrases, properties, and property types for conceptual combination.

Introducing paper: Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual Combination with Property Type
Synthetic Plant Dataset

About Dataset
The File contains 3D point cloud data of a Fabricate plant with 10 sequences. Each sequence contains 0-19 days data at every growth stage of the specific sequence.

The Importance of Synthetic Plant Datasets
Synthetic Plant Datasets: Synthetic plant FIle are carefully curated collections of computer- Beginning images that mimic the diverse appearance and growth stages of real plants. 

Training and Evaluation: By  fabricated plant file, researchers can train and evaluate machine learning models in a controlled environment, free from the limitations of real-world data collection. This controlled setting enables more efficient code development and ensures consistent performance across various environmental conditions.

Applications in Agricultural Technology
Plant Phenotyping: Synthetic plant file enable researchers to analyze plant traits and characteristics on a large scale, facilitating plant phenotyping studies aimed at understanding genetic traits, environmental influences, and crop performance.

Crop Monitoring: With the rise of precision agriculture, fabricated plant file play a crucial role in developing remote sensing techniques for monitoring crop health, detecting pest infestations, and optimizing irrigation strategies.

Advancements in Computer Vision and Machine Learning
Object Detection: It serve as benchmarking tools for training and evaluating object detection algorithms tailored to identifying plants, fruits, and diseases in agricultural settings.

Future Directions and Challenges
Dataset Diversity: As the demand for more diverse and realistic  grows, researchers face the challenge of generating data that accurately reflects the variability observed in real-world agricultural environments.

Researchers continue to explore techniques for bridging the gap between synthetic and real data to enhance model robustness and applicability.

Conclusion
Synthetic plant datasets represent a cornerstone in the development of cutting-edge technologies for agricultural monitoring, plant phenotyping, and disease diagnosis. By harnessing the power of synthetic data generation and machine learning, researchers can unlock new insights into plant biology and revolutionize the future of agriculture.

This dataset is sourced from Kaggle.

Introducing paper: Synthetic Dataset Generation for Adversarial Machine Learning Research