AI Training Data for Agritech
Helping AI enterprises with training data to allow agriculture to better manage and monitor land, livestock, and farms by incorporating technologies like the Internet of Things (IoT), sensors, location systems, robots, and artificial intelligence (AI).
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A Smarter Approach to Data Annotation for Incorporating AI in Agriculture
Annotating and labeling agriculture AI data to develop ML models for crop health and soil monitoring, livestock management, fructification detection, unwanted weeds detection, and other applications.
Annotation for Empowering AI in Agritech
2D Bounding Box Annotation for Crop Detection
With perception models, crop detection can be simplified. By using 2D bounding boxes, high-quality training data can be generated for deploying AI to detect crops from weeds and eliminate the grass.
Keypoint Annotation for Detecting Fruits & Vegetables Shape & Size
Keypoints can help determine the shape and size of an object. Keypoint annotation allows AI to identify fruit and vegetable characteristics, like their shapes and sizes, i.e., indicatives of fructification and ripening.
Polyline Annotation for Classifying Crops Lanes in Big Farms
Splines are used in polyline annotations to classify and characterize objects. The use of polyline annotation techniques could lead to building AI for detecting diseases, analyzing irrigation patterns, and predicting harvest times on large farms.
GIS & Geospatial Data Annotation
The use of GIS & Geospatial annotation enables AI-integrated farm drones to monitor plant health & farm condition, track farming equipment, and increase the efficiency of agricultural production.
AI Use Cases in Agriculture
Artificial intelligence in agriculture proves to be beneficial in yielding healthier produce, controlling pests, and monitoring soil and crops. Explore the popular use cases of AI in agriculture to learn how it can contribute to better farming:
Precision Farming with Robotics
With precision farming, diseased plants are detected early, pests are controlled, and poor nutrient levels are identified on farms using artificial intelligence.
Monitoring Fructify/Ripeness Levels
Image annotation can help develop AI for analyzing fruit and vegetables’ health conditions and fructification/ripeness levels through intelligent grading and sorting.
3D Field Mapping
Data from drones and satellites can be used to provide agronomists with accurate estimates of crop yields and prices, using 3D field maps.
Detect Unwanted Plants
By using agricultural robotics to detect unwanted plants and weeds, custom agricultural training data can improve agricultural yields.
Crop Health & Soil Monitoring
Artificial intelligence can simplify farm operations while calculating nutritional values and analyzing soil chemicals to assess crop health accurately.
Livestock Management
Using training data to build machine learning models to track livestock movements accurately, improve livestock management, and promote livestock welfare.
Feeding & Harvesting Crops
Implement artificial intelligence learnings based on our training data to automate and predict harvesting based on irrigation patterns and nutrient application schedules.
Intelligent Spraying
Take advantage of our training data expertise in computer vision to develop machines and models for spotting and preventing disorders through intelligent spraying.
Crop Yield Prediction
AI and machine learning can use our training data to empower drones and real-time sensors to predict and improve crop yield prediction.
Count on Us for Agritech AI Data
Cogito can offer high-quality training data for AI to be integrated into farming practices for crop health monitoring, livestock management, plant fructification detection, and many other applications. As part of our AI training data practice, we adhere to a broad ethical agenda, which includes GDPR, CCPA, Fair Pay pledge, diversity, and inclusion.