Agriculture

Powering companies in the agriculture industry with the highest quality training data to accelerate ML pipeline.

computer vision in agriculture

Use cases

computer vision in agriculture

Monitoring soil health

Conduct chemical soil analyses and provide accurate estimates of missing nutrients.
computer vision in agriculture

Protecting crops

Monitor the state of plants to spot and even predict diseases and recommend effective treatment of pests.
computer vision in agriculture

Feeding crops

Identify optimal irrigation patterns and nutrient application times and predict the optimal mix of agronomic products.
computer vision in agriculture

Harvesting, grading and sorting

Automate harvesting and predict the best time for it. Assess features such as ripeness, color, size, and defects.
computer vision in agriculture

Livestock Counting

Monitor animal detection and eliminate human error for livestock count across different locations.
computer vision in agriculture

Detecting weeds

Automate weed detection in farm crops and reduce the need for chemical herbicides.

How IntelinAir built the highest quality datasets with SuperAnnotate

IntelinAir uses the power of aerial imagery analytics, computer vision, deep learning, and mobile technology to deliver real-time, data-driven decision support to farmers, which helps them manage their operations more effectively.

When trying to accelerate their annotation pipeline, IntelinAir found SuperAnnotate and was able to dramatically boost annotation speed and quality.

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superannotate for agriculture industry

SuperAnnotate for agriculture

Machine learning empowers farmers of all sizes to achieve industry-critical goals quickly and accurately. Training this kind of large, complex model, however, requires a robust data annotation workflow with quality management measures and smooth iteration cycles.

SuperAnnotate is designed to feed quality data into AI models and get them into production up to 5x faster.

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