Time and Cost Challenges in Image Annotation
- Matthew Kutugata
Developing a comprehensive image repository for agriculture is essential to advance precision agriculture technologies. The diversity and complexity of agricultural scenes pose significant challenges for data collection and annotation. This document outlines the need for such repositories, the time and cost implications of manual labeling, and the limited benefits of using third-party apps which require single manual image processing, even with deep learning-based annotation tools like the Segment Anything Model (SAM).
Diversity in Agricultural Scenes
Growth Stage Variability
Plant appearances change significantly as they grow.
Morphological Influences
Biotic (living organisms) and abiotic (environmental) factors affect plant morphology.
Weed Similarities and Differences
High intra- and inter-species variation can result in similar appearances, such as Palmer amaranth resembling waterhemp.
Complex and Dense Scenes
Factors such as climate, soil type, and existing plant populations contribute to scene complexity.
Challenges in Manual Labeling
Time-Consuming Process
Manual pixel-wise labeling is precise and labor-intensive, especially for semantic segmentation, which requires detailed attention to complex shapes.
Case Study: Texas A&M
Used Precise BPO Solution for labeling 1,000 images, costing $1,465.75 with 11,726 segments over 2.5 weeks.
Comparison of Annotation Services
Services like Google Cloud AI Platform and Amazon SageMaker offer scalability but can increase costs with additional workers.
Estimated Labeling Costs and Time
Manual Annotation
Base Cost per Segment: $0.125, based on industry averages for manual annotation tasks.
Time per Segment: Average of 2 minutes (0.0333 hours) per segment, accounting for the detailed attention required for dense plant images.
Challenges with Automatic Annotation Tools in Third-Party Services:
Using SAM and Other Tools: Even when integrated into third-party annotation services or apps, tools like SAM require each image to be reviewed and labeled manually. This process remains labor-intensive and costly, as the tools often provide assistance but do not fully automate the task.
SAM
Classification: Minimal impact as classification tasks often deal with labeling entire images.
Object Detection: Assumes a 30% reduction in time and cost due to the limited automation benefits in third-party apps.
Segmentation: Assumes a 50% reduction in time and cost because of the complexity and density of plant images.
Task Type Comparison:
Task Type | Segments/Detections | Manual Cost ($) | Manual Time (hours) | Cost with SAM in Services ($) | Time with SAM in Services (hours) |
|---|---|---|---|---|---|
Classification | 10,000 | 1,250 | 333 | 1,250 | 333 |
Classification | 50,000 | 6,250 | 1,665 | 6,250 | 1,665 |
Classification | 100,000 | 12,500 | 3,330 | 12,500 | 3,330 |
Object Detection | 10,000 | 1,250 | 333 | 875 | 233 |
Object Detection | 50,000 | 6,250 | 1,665 | 4,375 | 1,166 |
Object Detection | 100,000 | 12,500 | 3,330 | 8,750 | 2,331 |
Segmentation | 10,000 | 1,250 | 667 | 625 | 333 |
Segmentation | 50,000 | 6,250 | 3,335 | 3,125 | 1,668 |
Segmentation | 100,000 | 12,500 | 6,670 | 6,250 | 3,335 |
Time-Consuming Labeling Examples
Examples of Manual Labeling Tasks:
Source | Annotation Technique | Scene Type | Time per Image |
|---|---|---|---|
Cicco et al. (2017) | Manual segmentation of cutouts | Simple | 5-30 min |
Skovsen et al. (2019) | Manual segmentation of cutouts | Very complex (field) | Hour(s) |
Sa et al. (2017) | Manual segmentation of drone images | Complex (field) | 60 min |
Bosilj et al. (2020) | Manual segmentation (3 classes) | Complex (field) | 2-4 hrs |
Conclusion
The development of a large image repository is crucial for advancing precision agriculture technologies. While automatic annotation tools can provide some assistance, they do not fully alleviate the cost and time burdens associated with labeling complex agricultural images in third-party applications. Integrated automated pipelines offer more significant efficiencies and are essential for managing extensive agricultural datasets effectively.