Tomato Target Detection Dataset
#target detection #image recognition #crop monitoring #pest detection #precision agriculture
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-10-14
AI Analysis & Value Prop
In the current agricultural field, tomato cultivation faces challenges such as frequent pests and low production efficiency. Existing solutions often rely on manual inspection, which is inefficient and prone to missed detections. Therefore, the tomato target detection dataset is created to help farmers promptly discover and address pests, improving crop yield and quality through efficient target detection technology. The dataset is constructed by capturing tomato images in various growth environments using high-resolution cameras, followed by multiple rounds of annotation and expert review to ensure accuracy and consistency. Data storage employs JPEG format, organized such that each image file corresponds to an annotation file, facilitating subsequent processing and model training. This dataset not only includes images of tomatoes in various growth states but also undergoes strict quality control to ensure data usability.
Dataset Insights
Sample Examples
b1f69235**.jpg|3024*4032|2.08 MB
74777023**.jpg|3456*5184|1.53 MB
e8f255c7**.jpg|3456*5184|1.32 MB
418be478**.jpg|4640*6960|5.18 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_count | int | The total number of tomato objects detected in the image. |
| object_type | string | The category name of the detected object, for example, tomato ripeness. |
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 purpose of the Tomato Object Detection Dataset?
- This dataset can be used to train machine learning models to identify and detect tomatoes, thereby improving agricultural production efficiency and crop quality.
- How can the Tomato Object Detection Dataset be used to enhance agricultural production efficiency?
- By training deep learning models for tomato detection, the identification and picking processes can be automated, thus enhancing agricultural production efficiency.
- How is the Tomato Object Detection Dataset constructed?
- The dataset is constructed by collecting and annotating a large number of tomato images so that machine learning models can perform recognition and detection.
- What are the applications of the Tomato Object Detection Dataset in agriculture?
- The dataset can be used in various agricultural applications such as automated tomato picking, crop management, and disease detection.
- Why is the Tomato Object Detection Dataset important for modern agriculture?
- Its role in providing accurate detection and classification can significantly reduce manual labor, optimize resource use, and enhance crop yield and quality, which is crucial for modern agriculture.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Tomato Target Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/190e47395c83c411bc314bdeab6b2356},
urldate={2025-09-15},
keywords={tomato target detection, agricultural dataset, pest detection, target detection dataset},
version={1.0}
}Using this in research? Please cite us.
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