Data Annotation for Crop and Weed Classification in Precision Agriculture

This solution uses built-for-AgTech processes to identify and classify crops, weeds, and grasses through a hybrid method that combines automated pre-labelling with human review. It supports applications such as intelligent spraying.

Model Overview

• Detection and classification of crops, weeds and grass, with strong capabilities for crops like corn and cotton, thanks to built-in pre-labelling algorithms.
• Workflow customization lets you tailor the pipeline to your needs.
• “Human in the loop” (HITL) teams to review, catch edge cases and improve quality beyond what pure automation achieves.
• Integrations with cloud storage and database systems so your data flows smoothly into training pipelines.

Key Advantages

• Improved farm productivity by delivering insights into crop types and the distribution of weeds.
• Better resource usage — less waste of labour, chemicals and time through more focused operations.
• A cloud-ready data framework: secure data handling, scalable operations, and flexible dataset integration.

Features in More Detail

Flexible & Secure Data Handling

• Incorporate both public and private cloud datasets into your pipeline.
• The platform architecture incorporates data security and regulatory compliance.
• Scalable management practices to support the increasing volume and complexity of data.

Custom Workflow & Quality Control

• The workflow allows you to customize the data annotation path and optimize for efficiency.
• Logic-gate-based sampling supports quality audits, filtering and review workflows.
• Error code integration allows flagging of unique or tricky cases, helping refine model training.
• Dashboards and analytics let you monitor annotator performance, assess data-label accuracy, and track project progress.

Application Focus

Smart Spraying & Precision Agriculture

This annotation solution uses synthetic data generation and augmentation for crops such as cotton and corn, followed by expert human annotation of the curated dataset. The resulting data serves as ground truth for models used in intelligent spraying and other precision agriculture workflows.

The workflow is designed specifically for tasks including smart spraying and intelligent irrigation: label data, build it into training systems, validate, and deploy. The annotation platform supports this with flexibility, scalability and end-to-end coverage.

Getting Started

Time-to-data is a critical factor. Our platform combines automated and expert human annotation to help you move from raw imagery to model-ready datasets faster.