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AgricultureBounding BoxImage Classification

Tobacco Damaged Leaf Count

Bounding boxes separating normal from damaged tobacco leaves, with every visible leaf captured for accurate counting.

SAMPLE PENDING

Sample frames for this project are available under NDA. Write to bilal@wortel.ai and we will share the relevant before and after pairs.

Challenges

  • Distinguishing normal leaves from damaged leaves based on subtle visual differences.
  • Minor or partially visible damage was easy to overlook.
  • Overlapping tobacco leaves complicated box placement.
  • Leaves appeared at different growth stages and sizes.
  • Varied types and levels of damage made classification harder.
  • Shadows and changing lighting conditions altered the appearance of damaged areas.
  • Complex backgrounds made individual leaves difficult to isolate.
  • Every visible leaf had to be annotated correctly for the counts to hold up.

Related work

SAMPLE PENDING

Cabbage Size Classification

Bounding boxes plus small, medium or large size labels on each cabbage, judged consistently despite shifting camera distance.

SAMPLE PENDING

Corn Detection & Growth Stage Classification

Bounding boxes separating large and small corn plants from weeds and ambiguous vegetation across complex field imagery.

SAMPLE PENDING

Cotton Detection & Size Classification

Cotton bolls boxed and split into regular and small classes among dense leaves, branches, and heavy occlusion.