“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.

Labeling is Time Consuming

Time consuming

• Need tens - hundreds of thousands, sometimes millions, of images to train data hungry deep learning models

• other have noted that manually labeling images can take minutes to hours (Table 1)

Labeling Time from the literature

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

• $1465.75 total cost

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

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)