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- Experiment_Arabidopsis_No-Metadata-Old.zip 220 MB
- Experiment_Arabidopsis_spt_12C.zip 99.5 MB
- Experiment_Arabidopsis_Testing-Series.zip 59.9 MB
- Experiment_Paragon_Wheat.zip 8.13 MB
- Mac-Version_Leaf-GP.zip 180 MB
- Processed_Arabidopsis_No-Metadata-Old.zip 73.6 MB
- Processed_Arabidopsis_spt_12C.zip 32 MB
- Processed_Arabidopsis_Testing-Series.zip 43.1 MB
- Processed_Paragon_Wheat.zip 3.98 MB
- Source_Code.zip 27 MB
- Windows-Version_Leaf-GP.zip 224 MB
- Source code (zip)
- Source code (tar.gz)
Ji Zhou1,2,3,*, Christopher Applegate1, Daniel Reynolds1, and Nick Pullen2
1Earlham Institute, Norwich Research Park, Norwich UK
2John Innes Centre, Norwich Research Park, Norwich UK
3University of East Anglia, Norwich Research Park, Norwich UK
*Correspondence: email@example.com or firstname.lastname@example.org
Leaf-GP V1.18 : All Platforms
Leaf-GP V1.18 : Wheat
Please note that:
- A Jupyter notebook is ONLY used for demonstrating the algorithm, NOT for batch processing big crop image series.
- The performance of the algorithm provided for the Leaf-GP project could be varied due to an end-user's computing resources such as physical memory and CPU speed.
- Users might encounter issues with specific libraries if they have not installed properly or not been optimised for Linux-based operating systems.
- We recommend users to preinstall the latest open Anaconda Python distribution before using functions and modules in the Leaf-GP analysis pipeline.