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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Fruit Tree Growth Stage Classification Dataset

The current agricultural industry faces the challenge of accurately monitoring the growth stages of fruit trees. Traditional methods of manual observation are inefficient and prone to errors. Existing solutions often lack specificity and fail to provide real-time feedback on the growth of fruit trees. This dataset aims to use image classification technology to address the technical challenges of identifying fruit tree growth stages, assisting farmers and agricultural managers in optimizing management decisions. The dataset is constructed mainly through a combination of drone photography and ground collection methods, ensuring coverage of various growth environments. For quality control, multiple rounds of annotation and expert review are used to ensure data accuracy and consistency. The data is stored in JPEG format and organized by image ID for easy retrieval and use.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
fruit_tree_species	string	Indicates the species of the fruit tree in the image.
growth_stage	string	Describes the growth stage of the fruit tree depicted in the image, such as budding, flowering, or fruiting.
leaf_color	string	Records the color of the leaves in the fruit tree image to assess health status.
fruit_presence	boolean	This field indicates whether fruits are present in the image.
canopy_density	string	Measures the density of the fruit tree canopy, categorized as sparse, medium, or dense.
disease_signs	string	Detects and records any signs of disease in the fruit tree image, such as leaf spots or wilting.

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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