Tomato Plant 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

Detecting tomato plants in dense agricultural environments.
Tomato plants had to be differentiated from weeds and surrounding vegetation.
Overlapping plants and foliage complicated boundary placement.
Partially visible plants needed accurate annotation.
Different growth stages produced variations in plant size and appearance.
Complex field backgrounds made individual plants difficult to identify.
Varying lighting conditions and shadows affected plant visibility.
Tight and consistent bounding boxes around irregular plant structures required careful attention to detail.

Project overview

Agricultural field images were annotated for a computer vision dataset focused on detecting tomato plants in real-world agricultural environments. Visible tomato plants were identified and accurate bounding box annotations created to generate high-quality training data for an AI-based object detection model.

The main objective was to prepare a reliable dataset for automatically detecting tomato plants across different field conditions and growth stages. As specified by the project, annotations used the Tree class label.

Dataset

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

Industry

Agriculture

Annotation type

Bounding Box

Labels

Tree

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Apple Detection

Bounding boxes around individual apples on tree branches, where dense leaves and occlusion hid much of each fruit.