“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. 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

• $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)