Wheat Detection - Wortel AI annotation portfolio

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

Counting visible leaves accurately on small wheat plants.
Distinguishing individual plants where they grew close together or overlapped.
Partially visible leaves and plants complicated the count.
Differentiating wheat leaves from surrounding vegetation.
Separating plants correctly into the Z11, Z12 and Z13 classes.
Keeping classification consistent across different field and lighting conditions.

Project overview

The Wheat Detection project involved annotating wheat plants according to the number of visible leaves. Different classes were assigned depending on whether an individual wheat plant carried one leaf, two leaves, or three or more leaves. The dataset was prepared for computer vision model training focused on wheat detection and leaf-count-based classification.

The three classes were Z11 for a wheat plant with one leaf, Z12 for a plant with two leaves, and Z13 for a plant with three or more leaves.

Dataset

Images: 1,000+
Classes: 3
Annotation: Bounding box detection
Source: Client-provided
Tool: CVAT
Client: Private client

Industry

Agriculture

Annotation type

Bounding Box Image Classification

Labels

Z11 Z12 Z13

Related work

Cabbage Size Classification

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

Corn Detection & Growth Stage Classification

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

Cotton Detection & Size Classification

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