Drone-Based Shrub and Grass Monitoring Dataset

This dataset contains drone-captured aerial images of shrubs, grass, and surrounding vegetation collected from the campus area of Daffodil International University, located in Savar, Dhaka, Bangladesh, Asia. The data was acquired using low-altitude drone footage and later converted into high-resolution image frames to support computer vision and deep learning research.

The primary objective of this dataset is to enable analysis of environmental impacts on shrub and grass life cycles using deep learning techniques. The images capture natural variations in vegetation structure, density, color, and growth patterns influenced by environmental factors such as sunlight exposure, soil conditions, seasonal changes, and human activity.

The dataset is suitable for a wide range of tasks, including vegetation classification, object detection, segmentation, lifecycle analysis, and environmental monitoring. It can be used to train and evaluate deep learning models such as convolutional neural networks for understanding plant health, growth stages, and stress indicators from aerial imagery.

All images were extracted from drone videos to ensure diversity in viewing angles, scales, and spatial context. Minimal preprocessing was applied to preserve real-world conditions and visual complexity. This makes the dataset particularly useful for developing robust models that can generalize to real environmental scenarios.

This dataset is intended for academic research, environmental studies, and machine learning experimentation. Researchers can use it to explore sustainable environmental monitoring approaches, assess vegetation dynamics, and develop data-driven solutions for mitigating environmental impacts on shrub and grass ecosystems.