Fruit Tree Quantity Detection Dataset

#object detection #quantity estimation #agricultural monitoring #fruit tree management #precision agriculture

20000 records
4.5G
JPG/PNG/JSON
CATL
MOBIUSI INC

Updated:2026-04-14

AI Analysis & Value Prop

The current agricultural industry faces challenges in fruit tree management and yield monitoring. Traditional manual counting methods are inefficient and prone to errors, failing to meet the needs of modern agriculture. Existing image recognition technologies, although making progress in some fields, still face issues of insufficient accuracy and poor adaptability in fruit tree quantity detection. This dataset aims to solve these technical challenges by providing high-quality image samples, improving the accuracy and efficiency of detection. The dataset contains diversified images from different orchards, captured with high-resolution cameras under various lighting conditions to ensure diversity and representativeness. We have implemented multiple labeling rounds and expert reviews as quality control measures to ensure the accuracy and consistency of data annotation. The data is stored in JPEG format, with a clear structure, making it convenient for subsequent analysis and model training.

Dataset Insights

Sample Examples

54937497**.jpg|6960*4640|9.92 MB

b30f3c4e**.jpg|6960*4640|9.52 MB

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
tree_type	string	The type of fruit tree in the image, such as apple tree, orange tree, etc.
tree_count	integer	The number of fruit trees that can be detected in the image.
tree_health	string	The health status of the fruit tree, including healthy, pest infestation, wilting, etc.
fruit_presence	boolean	Indicates whether there are visible fruits on the fruit trees in the image.
leaf_density	string	The density of leaves on the fruit tree, represented as sparse, medium, or dense.
background_type	string	The type of background in the fruit tree image, such as farmland, forest land, or urban environment.
lighting_condition	string	The lighting condition during image capture, such as sunny, cloudy, or dusk.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What is the Fruit Tree Quantity Detection Dataset?: The Fruit Tree Quantity Detection Dataset is an image dataset for object detection, focusing on fruit tree management. It helps in more efficiently and accurately managing the number of fruit trees in the agricultural field.

Why is the Fruit Tree Quantity Detection Dataset important for agriculture?: The Fruit Tree Quantity Detection Dataset is important for agriculture because it improves the automation of fruit tree management, reduces labor input, and increases the accuracy of yield monitoring.

How does the Fruit Tree Quantity Detection Dataset improve fruit tree management?: The Fruit Tree Quantity Detection Dataset improves fruit tree management by providing accurate data and localization of fruit trees, helping farmers and agricultural experts optimize planting and maintenance strategies, thereby enhancing management efficiency.

What is the potential of applying this dataset?: The Fruit Tree Quantity Detection Dataset has the potential to be widely applied in smart agriculture systems, aiding in the development of automated fruit tree monitoring tools and enhancing the intelligence level of orchard management.

What are the prospects of technologies using the Fruit Tree Quantity Detection Dataset?: The prospects of technologies using the Fruit Tree Quantity Detection Dataset include the development of automated drones and robotics for real-time monitoring of fruit tree growth and assisting in precision agriculture practices.

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Cite this Work

@dataset{Mobiusi2025,
  title={Fruit Tree Quantity Detection Dataset},
  author={MOBIUSI INC},
  year={2025},
  url={https://www.mobiusi.com/datasets/2f1970741fe2ef85ebe14b66e7f290dc?dataset_scene_id=5},
  urldate={2025-09-15},
  keywords={fruit tree quantity detection, agricultural dataset, object detection dataset, fruit tree monitoring, precision agriculture},
  version={1.0}
}

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