Field Data Processing

Owned by Matthew Kutugata
Last updated: Dec 08, 2023
6 min read

Below is a detailed guide to the Field Data Processing section of the larger AgImageRepository development project. At the heart of Field Data processing is the role of the "Agricultural Data Processing Specialist," a key position responsible for harnessing advanced data processing techniques. This specialist is pivotal in managing, analyzing, and utilizing a significant collection of agricultural image data collected under field condition. Our project's structured approach is divided into three critical phases: addressing the backlog of existing data, developing an efficient image processing pipeline, and establishing ongoing monitoring and quality control. The Agricultural Data Processing Specialist plays a crucial role in each phase, ensuring the comprehensive processing and application of a vast array of agricultural image data.

 

Overview of Phases

Phase 1: Addressing Initial Data Backlog

  • Performed by: Data specialist

  • Duration: 2-6 weeks (dependent on extent of backlog and the state of data)

  • Description: This phase focuses on managing and organizing the existing backlog of images and metadata. Key activities include assessing the volume and storage of the data, reviewing and improving data organization, inspecting the metadata for accuracy and completeness, and performing a quality check of a sample of images. A key part of this phase is the creation of an "Image Quality Control Dataset," where a subset of images is labeled based on quality attributes like exposure, brightness, and focus. This dataset is pivotal in developing a classifier to aid future image quality inspections. This phase lays the groundwork for efficient data processing by identifying and resolving any immediate issues with the existing dataset.

Phase 2: Pipeline Development

  • Performed by: Dev team and data specialist

  • Duration: 3-4 weeks

  • Description: In this phase, the primary objective is to develop a robust image processing pipeline. This involves clearly defining the desired outputs (such as cutouts, segmentation masks, and bounding box labels), planning the development of the segmentation pipeline and the automatic annotation system, and establishing an organized system for managing cutouts or sub-images. The focus is also on automation to enhance efficiency and accuracy, as well as setting a realistic timeline for the pipeline’s development, testing, and deployment.

Phase 3: Processing Images and Continuous Monitoring

  • Performed by: Data specialist

  • Duration: On-going

  • Description: The final phase is centered around the actual processing of images using the developed pipeline, which includes implementing the processing, debugging and troubleshooting issues, validating the results, and reporting any processing-related issues. It also involves continuous monitoring of data uploads, ensuring effective data storage and backup, regularly updating processing protocols, coordinating with data-providing teams, and providing regular reports on the status and quality of the processed data. This phase ensures the ongoing integrity and utility of the data repository, adapting to evolving needs and technological advancements.

Detailed Description

Phase 1: Addressing Initial Data Backlog

  • Assessing Volume and Storage:

    • Volume Assessment: Assess the total volume of the dataset in terms of the number of images and the size of the metadata.

    • Storage and Accessibility Evaluation: Review current data storage solutions, assess accessibility for processing and analysis, and identify any data retrieval or integrity issues.

  • Data Organization and Inspection:

    • Data Organization Review: Analyze current data organization methods, such as naming conventions, and identify areas for improvement.

    • Metadata Table Inspection: Examine the metadata table’s structure and content, focusing on key columns and the type of information they contain, like species and location, identifying gaps or potential enhancements.

  • Quality Assessment and Anomaly Detection:

    • Visual Inspection of Images: Conduct a preliminary visual inspection of a representative sample of images covering various data sources and plant types to evaluate quality factors like resolution and clarity. Identify any missing image types.

    • Metadata-Image Correlation: Ensure accurate linking between images and their metadata, checking for discrepancies or missing connections.

    • Validate Plant Species: Confirm that the plant species in the image matches metadata information.

    • Anomaly Detection: Spot anomalies or outliers in image sizes, formats, or metadata entries.

    • Reporting Findings: Document findings, highlight issues, and make recommendations for data cleaning and processing improvements.

  • Image Quality Control Dataset Development

    • Creation of the Dataset: During the visual inspection of images, create a preliminary "Image Quality Dataset." This dataset will involve labeling a subsample of images based on their quality.

    • Labeling Criteria: Labels should be assigned on a per-image level, focusing on various aspects of image quality such as exposure, brightness, focus, saturation, and other relevant factors.

    • Purpose of the Dataset: The primary goal is to develop a dataset that can be used to train an "image quality" classifier. This classifier will serve as an automated tool to assist in future image quality inspections and streamlining the process.

    • Data Documentation and Preparation: Document the criteria used for labeling and prepare the dataset for integration into the on-going data inspection pipeline.

  • Data Cleaning and Preprocessing

    • Begin by correcting metadata errors, standardizing formats, and filtering out unusable images.

Phase 2: Pipeline Development

  • Defining Data Product Goals:

    • Clearly articulate desired outputs from the image processing pipeline, such as cutouts, segmentation masks, and bounding box labels. Specify the details of each data product.

    • Establish a system for managing cutouts or sub-images, focusing on naming conventions, storage structure, and indexing.

  • Pipeline Development Planning:

    • Plan the development of a segmentation pipeline, incorporating insights from previous approaches like semifield, SAM, or in-house models.

    • Identify technical requirements and necessary tools for the pipeline.

    • Plan for an iterative development process, allowing for continuous improvements and adjustments based on testing and feedback.

    • Develop a realistic timeline with key milestones for stages like initial development, testing, and deployment.

    • Automation and Efficiency:

    • Automate tasks such as image/species validation, duplication checking, and metadata validation using scripts or other data management tools.

    • Create scripts and protocols that Data Liaison can use to perform day-to-day inspections.

Phase 3: Processing Images and Continuous Monitoring

  • Image Processing Using Segmentation Pipeline

    • Processing Implementation: Utilize the developed segmentation pipeline to process all accumulated and incoming images.

    • Debugging and Troubleshooting: Actively debug and troubleshoot any issues that arise during the image processing phase. This involves coordinating with the dev team to identify and resolve technical glitches or inaccuracies in the segmentation process.

    • Validation of Results: Conduct thorough validation of the processed images to ensure that the results meet the established quality and accuracy standards. This may involve manually inspecting a random subsample of images, labels, and other results.

  • Monitoring Data Uploads:

    • Monitor data uploads to ensure proper submission of all data products and quickly identify any issues.

  • Data Storage and Backup:

    • Implement and maintain robust data storage and backup protocols.

  • Process Review and Updates:

    • Regularly review and update processing protocols to accommodate growing data volumes and evolving repository requirements.

  • Coordination and Communication:

    • Coordinate with liaisons to understand data submission schedules and unique characteristics of data from the three teams.

    • Communicate feedback regarding any inconsistencies or issues that need to be addressed.

  • Reporting on Data Status:

    • Regularly report on the status, volume, and quality of incoming data, ensuring transparency and informed decision-making.

 

Essential Skill set for Agricultural Field Data Processing Specialist

Azure Cloud Computing:

  • Proficiency in Azure services, particularly Azure Storage for managing large datasets.

  • Familiarity with Azure's tools like their Python SDK and command line tools.

Data Management and Analysis:

  • Experience in handling large datasets, including data cleaning, preprocessing, and organization.

  • Proficiency in data analysis techniques to assess data quality and extract insights.

Image Processing and Computer Vision:

  • Proficient in basic digital image processing techniques.

Programming and Software Development:

  • Proficiency in Python, which is commonly used in data science and image processing.

  • Familiarity with software development practices and tools for version control, debugging, and automation.

Machine Learning and AI:

  • Basic understanding of machine learning principles, particularly those applicable to image analysis and computer vision.

  • [Can add here more if we’re trying to attract someone with more sophisticated skills.]

Project Management:

  • Skills in planning, organizing, and managing projects, including setting timelines, allocating resources, and tracking progress.

  • Ability to handle multiple tasks simultaneously and adapt to changing priorities.

Problem-Solving and Critical Thinking:

  • Strong problem-solving skills to address technical challenges and data issues.

Communication and Collaboration:

  • Excellent communication skills to effectively convey technical information to non-technical stakeholders.

  • Ability to collaborate with diverse teams, including data scientists and agricultural experts.

Attention to Detail:

  • Keen attention to detail, especially important in data quality assessment and ensuring the accuracy of image processing.

Adaptability and Continuous Learning:

  • Adaptability to rapidly evolving technologies in data science and agricultural imaging.

  • Commitment to continuous learning and staying updated with the latest advancements.

Quality Assurance and Control:

  • Knowledge of quality assurance practices to ensure data integrity and accuracy.

  • Experience in developing and implementing quality control processes.

Technical Documentation:

  • Ability to create clear and comprehensive technical documentation for systems, processes, and guidelines.