Computer Vision in the Agriculture industry has made leaps and bounds but tends to be confined to processing plants. TagX takes it to the field by providing training data to Agriculture AI companies enabling growers to spot early signs of disease for yield maximization and pest prevention as well as automate manual tasks like weeding and picking.
AI is used for crop monitoring to solve problems before large-scale damage is done, ensuring consistent high-quality operations and high-yield production.Using drones or satellite imagery to monitor crops, identify diseases or pests, and predict crop yield. TagX can perform image annotation to label and tag images of crops, identifying the presence of specific pests or diseases, and the overall health of the crop.
Companies implement vision-based best practices to ensure the quality and safety of yield with accuracy and efficiency. Using sensor data and machine learning models they optimize crop management and increase yield. TagX can perform data annotation to label sensor data, identifying specific attributes such as soil moisture or temperature, to train models for precision farming.
This process begins with fruit detection on trees using computer vision with a sensor and robot arm motion to the position of the detected fruit and fruit harvesting by the end effector without damaging the target fruit and its tree. Our data annotation services can help train computer vision models to identify and differentiate between different types of crops and their maturity levels, which can enable automated vegetable and fruit picking.
AI is used to automate animal detection & monitor livestock counts across multiple locations. Cameras or sensor data are used to monitor the health and behavior of livestock. TagX can perform image and video annotation to label and tag images and videos of livestock, identifying specific attributes such as posture or movement, to train models for livestock monitoring.
Machine learning and AI models are used to create autonomous vehicles to control tasks such as planting, harvesting, and soil analysis. TagX can perform data annotation to label sensor data and images collected by autonomous vehicles, identifying specific attributes such as crop type or soil condition, to train models for autonomous vehicles.
Our data services can be used to label images and data related to crop quality, such as size, color, and shape, which can be used to train AI models to grade and sort crops automatically. Accurate grading and sorting of crops lead to better market value and customer satisfaction.
This involves labeling images of plants with different types of weeds or diseases to help the model learn to distinguish between healthy and infected plants. With accurate annotations, the model can be trained to identify early signs of diseases or weeds, allowing farmers to take timely action to prevent crop losses. Our data annotation services can help train AI models to identify and differentiate between crops and weeds, enabling automated weed detection and removal.
With Tagx, your AI applications will recognize and identify objects at unmatched speed, improving your models predictions and confidence levels. We understand that different AI use cases require different types of data annotation to achieve optimal performance. TagX offers a wide range of annotation services for image, text, audio, and video data, as well as 3D point cloud and LiDAR annotation. We pride ourselves on delivering high-quality annotations with precision and accuracy to ensure that your AI models can perform at their best. Whatever your use case may be, you can count on us to provide the right type of annotation to meet your specific needs.
Book a free consultation call today with one our Experts and explore endless possibilities.