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README.md

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+---
+tags:
+- Image classification
+- deep learning training
+- pattern recognition
+- Agricultural automation
+- spraying operations
+- crop monitoring
+license: cc-by-nc-sa-4.0
+task_categories:
+- image-classification
+language:
+- en
+pretty_name: Pesticide Spraying Scene Classification Dataset
+size_categories:
+- 10M<n<100M
+---
+
+# Pesticide Spraying Scene Classification Dataset
+
+The current agricultural industry faces challenges such as low spraying efficiency and environmental pollution, especially during large-scale farmland spraying. Traditional methods rely on manual operations, which can lead to pesticide waste and uneven spraying. Existing solutions often lack efficient image recognition technology and cannot monitor spraying effectiveness and crop conditions in real-time. This dataset aims to support the intelligent development of agriculture by providing high-quality images of spraying scenes, thereby improving the efficiency and accuracy of spraying operations. Data collection uses high-resolution cameras in actual spraying environments to ensure images accurately reflect operational conditions. Quality control includes multiple rounds of annotation and expert review to ensure data consistency and accuracy. Image data is stored in JPG format, organized by category for easy subsequent processing and analysis.
+
+## Technical Specifications
+
+| Field | Type | Description |
+| :---  | :---  | :--- |
+| file_name | string | File name |
+| quality | string | Resolution |
+| crop_type | string | Identify the type of crop being sprayed in the image. |
+| application_method | string | Identify how pesticides are being applied in the image, such as manual spraying or mechanical spraying. |
+| daytime | string | Determine the time period when the image was taken, such as daytime or nighttime. |
+| weather_condition | string | Identify the weather condition when the image was taken, such as sunny, cloudy, or rainy. |
+| operator_presence | boolean | Determine if there is an operator present in the image. |
+| equipment_type | string | Identify the type of spraying equipment used in the image. |
+| target_area | string | Identify the area being sprayed in the image, such as leaves, stems, or blossoms. |
+
+## 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/0c8f54017df916a472c40d4cdc65b60a?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com