Tracking Plant Growth Using Image Sequence Analysis- Dataset

This dataset consists of five subsets with annotated images in COCO format, designed for object detection and tracking plant growth:
1. Cucumber_Train Dataset (for Faster R-CNN)
- Includes training, validation, and test images of cucumbers from different angles.
- Annotations: Bounding boxes in COCO format for object detection tasks.

2. Tomato Dataset
- Contains images of tomato plants for 24 hours at hourly intervals from a fixed angle.
- Annotations: Bounding boxes in COCO format.

3. Pepper Dataset
- Contains images of pepper plants for 24 hours at hourly intervals from a fixed angle.
- Annotations: Bounding boxes in COCO format.

4. Cannabis Dataset
- Contains images of cannabis plants for 24 hours at hourly intervals from a fixed angle.
- Annotations: Bounding boxes in COCO format.

5. Cucumber Dataset
- Contains images of cucumber plants for 24 hours at hourly intervals from a fixed angle.
- Annotations: Bounding boxes in COCO format.

This dataset supports training and evaluation of object detection models across diverse crops.

本数据集包含五个子集，所有子集均采用COCO格式（Common Objects in Context）标注图像，专为植物生长目标检测与追踪任务打造。
1. 黄瓜训练数据集（适配Faster R-CNN）
- 涵盖来自不同拍摄角度的黄瓜训练、验证与测试图像。
- 标注信息：采用COCO格式的边界框，用于目标检测任务。
2. 番茄数据集
- 包含固定拍摄角度下以每小时间隔采集的24小时番茄植株图像。
- 标注信息：COCO格式边界框。
3. 辣椒数据集
- 包含固定拍摄角度下以每小时间隔采集的24小时辣椒植株图像。
- 标注信息：COCO格式边界框。
4. 大麻植株数据集
- 包含固定拍摄角度下以每小时间隔采集的24小时大麻植株图像。
- 标注信息：COCO格式边界框。
5. 黄瓜数据集
- 包含固定拍摄角度下以每小时间隔采集的24小时黄瓜植株图像。
- 标注信息：COCO格式边界框。
本数据集可支持针对多种不同作物的目标检测模型的训练与评估。