Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Greenhouse Image Classification Dataset

The current agricultural industry faces rapidly growing demands and the challenge of limited resources, especially in the area of greenhouse management. Existing datasets are often focused on single crops, lacking comprehensive classification for different types of greenhouses. This dataset aims to provide a rich image classification resource covering various types such as multi-span greenhouses, arch greenhouses, solar greenhouses, and daylight greenhouses to meet the needs of smart agriculture. Data collection is carried out using high-resolution cameras under natural light conditions to ensure image quality. For quality control, we employ multiple rounds of annotation and expert reviews to ensure label consistency and accuracy. The data is stored in JPG format and organized in a folder structure for ease of subsequent processing and training. The advantages of this dataset include its high annotation accuracy (95%), completeness (covering various greenhouse types), and the introduction of new data augmentation techniques to enhance model generalization capability, which is expected to improve recognition rates in crop monitoring tasks by at least 15%.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
greenhouse_type	string	The type of greenhouse in the image, such as glass greenhouse, plastic greenhouse, etc.
lighting_condition	string	The lighting condition when the image was taken, such as natural light, artificial light, etc.
plant_presence	boolean	Indicates whether there are plants present in the image.
greenhouse_size_estimation	string	An estimation of the greenhouse size, possibly small, medium, or large.
damage_indicator	boolean	Indicates whether there is visible damage to the greenhouse in the image.
climate_control_presence	boolean	Indicates the presence of climate control equipment in the greenhouse.

Compliance Statement

Authorization Type	CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
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

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com

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