Agriculture

The use of AI is becoming prevalent in various industries, including agriculture. Context-driven data labelling plays a crucial role in helping farmers protect their crops while reducing human involvement. Image annotation in agriculture streamlines the following operations:

• Livestock Management – Drones equipped with image annotation enable efficient monitoring of crops and animals, simplifying livestock husbandry.
• Geosensing – Machines can identify soil conditions in the field using semantic segmentation. Deep learning trains large datasets to detect soil conditions accurately.
• Crop Health Monitoring – Computer vision detects infections caused by insects or fungus, promoting crop health through precise annotation.
• Plant Fructification Detection – Image annotation helps determine crop fructification and maturity levels, aiding farmers in harvesting at the right time.
• Unwanted Crop Detection – With proper ML training and image annotation, machines can identify and remove weeds and wildflowers, enhancing crop growth and yield.

Insurance

This is a sector that gains significant benefits from AI. AI must be precisely trained to replace manual damage evaluation, which requires annotated data highlighting vehicle defects. Advanced ML models can predict if a part needs replacement and calculate the replacement cost accurately.

Extensive pattern recognition leads to reduced response times, improved customer experience, and savings in financial and human resources for the insurance industry.

Autonomous driving

Image annotation is crucial for creating extensive datasets to train self-driving software for autonomous vehicles. To ensure vehicle safety, the algorithm must learn to recognize road signs, cycle lanes, traffic lights, potential hazards, optimal weather conditions, and more.

Other implementations of image annotation for autonomous vehicles include in-cabin behaviour monitoring, steering response navigation, on-road object and dimension detection, movement tracking, and LiDAR sensing.

Security and surveillance

The increasing need for security cameras is propelling the ML industry forward. Companies prioritize safeguarding their operations and sensitive data to prevent theft, and accidents. Automating inventory management and surveillance through image processing is valuable.

Image annotation plays a crucial role in security, enabling crowd detection, night vision, thermal vision, traffic motion, face identification for theft detection, pedestrian tracking, and more. ML engineers use annotated images to train high-tech video equipment for round-the-clock security surveillance and create a safer environment.

Medical imaging

Machine learning model training is revolutionizing the medical industry, contributing to the success of modern healthcare. AI plays a crucial role in detecting diseases like brain tumours, and neurological disorders through CT scans and MRI. Well-trained ML models with abundant medical image data enable these advancements.

Image annotation in the medical field extends to various use cases, including analysis for cancer cell detection, kidney stones, teeth segmentation, eye cell analysis, and even microscopic cell analysis at nano levels. Using these datasets, ML models perform deep learning to create automated diagnosis mechanisms for the healthcare industry.