The repository is for the versioning of the actioning learning projects on the crop diseases detection using few shots techniques.
Topic: "Evaluating Few-Shot Learning Approaches in Computer Vision for Enhanced Detection of Agricultural Crop Diseases"
This research initiative aims to address the challenge of detecting crop diseases using advanced machine learning techniques, particularly few-shot learning in computer vision. Given the critical role of early disease detection in agriculture for mitigating crop losses, this study is positioned at the intersection of data science and agricultural technological innovation.
To develop a data-efficient machine learning model, utilizing few-shot learning algorithms, that can accurately identify a variety of crop diseases from limited image data.
Investigate the efficacy of various few-shot learning techniques and algorithms in the domain of crop disease detection.
Optimize a machine learning model for high accuracy and efficiency with constrained training datasets.
Implement a scalable and user-friendly model deployment solution, using Streamlit, for practical agricultural applications.
Knowledge Skills Required:
Machine Learning Expertise: Proficiency in few-shot learning, computer vision techniques, and image processing.
Python and ML Libraries: Strong skills in Python, with experience in TensorFlow or PyTorch, and data manipulation libraries like Pandas and NumPy.
Model Deployment: Knowledge of deploying ML models using tools like Streamlit, with a focus on user-friendly interface design.
Software, Tools, System Architecture and Components Anticipated:
Development Tools: Python, TensorFlow or PyTorch for model development, and Jupyter Notebooks for iterative coding and testing.
Data Processing and Visualization: Pandas, NumPy, and OpenCV for data handling, and Matplotlib or Seaborn for visualization.
Deployment and Collaboration: Streamlit for application development and GitHub for version control and collaboration.
Advancement in Few-Shot Learning: Expecting significant improvements in crop disease detection accuracy using few-shot learning techniques compared to traditional methods.
Efficient Data Utilization: Hypothesis that the model will achieve high accuracy with substantially less data, demonstrating the effectiveness of few-shot learning in resource-constrained environments.
Practical Application Feasibility: Anticipating that the developed model can be easily integrated into real-world in agricultural settings, offering a user-friendly and accessible tool for farmers and agronomists.
Literature Review and Algorithm Selection: Conduct a review of current few-shot learning methods.
Data Preparation and Model Development: Prepare the 200k image dataset and develop the machine learning model using any of the few-shot learning techniques.
Model Testing and Refinement: Test the model extensively and refine it based on performance metrics, with a focus on improving performance, accuracy and efficiency.
Deployment and User Testing: Develop a Streamlit-based application for model deployment and conduct user testing to ensure practical usability and effectiveness in real agricultural settings.
https://www.kaggle.com/datasets/sadmansakibmahi/plant-disease-expert
200k images (A subset will be used)
https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00770-1
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4611614
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4627525
https://www.sciencedirect.com/science/article/pii/S2772375523001363
https://acsess.onlinelibrary.wiley.com/doi/full/10.1002/agj2.21285
ML Model:
Few-Shot Learning Framework: Implement a machine learning model based on few-shot learning principles, tailored for high performance with limited training data. This model will be fine-tuned to maximize disease detection accuracy with minimal training data, aligning with the latest trends in data science and machine learning.