“The scarcity of public image datasets remains a key bottleneck in developing next-generation computer vision and intelligent systems for precision agriculture.” (Lu and Young 2020)

Why?

Developing image datasets is difficult, especially for agricultural applications. Collecting and annotating images pose major challenges.

Image Collection

Data hungry deep learning models require hundreds of thousands, sometimes millions, of images to train and become invariant to changes in location, lighting, backgrounds, or other factors. Devising high-throughput systems is necessary to capture hundreds of thousands of images of weeds at various growth stages while affected by field-like conditions. A network of agronomic, hardware, and software engineers is needed to automate large scale image collection.

Labeling is Time Consuming

Pixel-wise labels must be accurate. The process of labeling by hand can take minutes to hours even when using third-part annotation tools. Labels for semantic segmentation are the most time consuming. The need for high accuracy, complex leaf structures, and amorphous shapes make labeling plants one of the most difficult labeling tasks. Many have reported the high time requirement needed for labeling images of weeds.

Labeling is Expensive

Texas A&M Case Study

Used a third-party labeling service to sematically label images of agricultural weeds and plants at early growth stages roughly 2-6 weeks after emergence. Details are as follows:

• Company: Precise BPO Solution

• 1000 images

• 11726 segments

• $0.125 per segment*

  • *While TAMU was given a discount of $0.095 per segment, $0.125, the non-discounted price, is used here. It is unlikely the company will provide the same discount for images over 1000.

• Time to label all images - 2.5 weeks

• Number of workers unknown

• $1465.75 total cost

Other Labeling Services

Precise BPO Solutions were relatively inexpensive compared to other more known labeling services. However, their total time (2.5 weeks for 11,726 segments) is not scalable. Using more workers may decrease turn-around-time but brings increased costs.

Using third-party labeling services like Google AI Platform and Amazon SageMaker come with high costs when considering scale and time. Less time for labeling requires more workers which increases costs.

Google Cloud (AI Platform)

• uses “unit” pricing. For example, 2 segments x 2 workers = 4 units.

• Prices start at $870 for 1,000 units

• Image segmentation is their most expensive labeling task

Amazon SageMaker (Mechanical Turk)

• Pricing for number of reviewable objects, in our case segments, plus tasks

• $0.08 per object review + $0.84 per semantic labeling task

• reviewing 16272 per week * (730 hours in a month / 168 hours in a week) = 70705.71 per month

• semantic segmentation is most expensive labeling task

Amazon SageMaker (vendor: Cogito)

• highest rated labeling vendor in amazon marketplace

Agricultural Scenes are Diverse

• High individual diversity from differences in growth stages and effects of biotic and abiotic factors on morphology

• Large intra-species variation and similarity with other species

• Complex agricultural scenes as the result of effect from climate, soil, ground residue, and other weed populations.

Labeling Cost Projections for SemiField

Precise BPO Solutions

We can estimate the expected costs and time for SemiField data collection using the Texas A&M numbers.

• here we use a smaller average of 8 segments per image (instead of 11.726 like TAMU)

• $0.125 per segment

• 2.5 weeks (13 working days from 8am - 5pm) = ~104 worker hours

• 104 hours translates to 32 seconds per segment (104 / 11726)

*TAMU example **Using 2 workers for quicker turn around time