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README.md

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+---
+tags:
+- object detection
+- image recognition
+- agricultural product transportation
+- logistics management
+- smart agriculture
+license: cc-by-nc-sa-4.0
+task_categories:
+- object-detection
+language:
+- en
+pretty_name: Agricultural Product Transportation Scene Dataset
+size_categories:
+- 1B<n<10B
+---
+
+# Agricultural Product Transportation Scene Dataset
+
+The current agricultural industry faces challenges such as low transportation efficiency and high loss rates. Especially during the transportation of agricultural products, the damage rate significantly affects farmers&#039; income. Existing solutions primarily rely on manual inspection, which is inefficient and error-prone, hence the urgent need for an efficient automated detection method. This dataset aims to provide fundamental data for object detection in agricultural product transportation scenarios to help develop automated monitoring systems. Data collection is performed using high-resolution cameras capturing agricultural product transportation scenes under various lighting and weather conditions, ensuring data diversity and representativeness. In terms of quality control, the data undergoes multiple rounds of annotation, consistency checks, and expert reviews to ensure annotation accuracy and consistency. Data storage is in JPG format organized into a folder structure for quick access and processing.
+
+## Technical Specifications
+
+| Field | Type | Description |
+| :---  | :---  | :--- |
+| file_name | string | File name |
+| quality | string | Resolution |
+| transport_vehicle_type | string | The type of vehicle used during transportation, such as a truck or tractor. |
+| product_type | string | The type of agricultural product being transported, such as fruit, vegetables, or wheat. |
+| product_quantity | integer | The quantity of agricultural products being transported, which helps to understand the scale of transportation. |
+| weather_condition | string | The weather condition when the image was captured during transportation. |
+| time_of_day | string | The time period when the image was taken, such as morning, afternoon, or evening. |
+| obstacles_present | boolean | Indicates whether there are obstacles present in the transportation process as depicted in the image. |
+| road_condition | string | The condition of the road during transportation, such as smooth, rough, or slippery due to rain. |
+
+## Compliance Statement
+
+<table>
+  <tr>
+    <td>Authorization Type</td>
+    <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td>
+  </tr>
+  <tr>
+    <td>Commercial Use</td>
+    <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td>
+  </tr>
+  <tr>
+    <td>Privacy and Anonymization</td>
+    <td>No PII, no real company names, simulated scenarios follow industry standards</td>
+  </tr>
+  <tr>
+    <td>Compliance System</td>
+    <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td>
+  </tr>
+</table>
+
+## Source & Contact
+
+If you need more dataset details, please visit [Mobiusi](https://www.mobiusi.com/datasets/66be44825d2614a9862fff75d9eb9a55?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com