Developing computer vision applications

We help you to design and develop tailored AI solutions

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Visual crop yield estimation

Yield estimation is an essential preharvest practice, supporting decision-making for allocating essential logistics such as transportation means, labor force, supplies, and more. An overestimation leads to further costs that impact profitability; understimation entails potential crop waste and additional costs.

Manual yield estimation with the counting of products such as fruits or vegetables is very time-consuming and expensive. Modern deep learning methods provide good accuracy for automatic counting, even with occlusions caused by leaves or branches, illumination, and object size. So, computer vision approaches can be used for the automatic counting of fruits, vegetables, or even flowers.

object detection tomato
image classification diseases

Pests, weeds, and plant diseases

Traditional disease identification relies on manual scouting, which can be time-consuming and prone to human error. Image recognition and classification enables rapid, large-scale disease detection, helping to prevent outbreaks before they spread.

Moreover, drones equipped with multispectral and hyperspectral cameras can scan entire fields, enabling identifying areas of plant stress days before symptoms become visible to the human eye. Detecting variations in plant health, soil moisture levels, and nutrient deficiencies across vast farmland. For instance, NDVI (Normalized Difference Vegetation Index) imaging helps farmers assess plant vigor by analyzing how crops reflect light.

Smart farming animal monitoring

Animal monitoring is a crucial aspect of modern smart farming. By employing vision systems with advanced object detection and pose estimation models, farmers can monitor the health, behavior, and well-being of their livestock in real time.

Moreover, modern methods are capable of assessing the resources provided to the animals (space, lying substrate, drinker access) and measuring the animals themselves to detect lameness, indicators of injury or disease, and abnormal behaviors. The advantages of computer vision systems are rooted in their automatic, non-invasive, and low-cost animal monitoring capabilities.

object detection chicken

Developing your edge computer vision applications

From Abraia Vision we develop custom computer vision solutions based on state of the art deep learning models and image processing, ready to run on local devices (Edge AI). This approach allows for real-time analysis and decision-making without relying on cloud services.

Edge AI involves deploying lightweight models on devices like smartphones, cameras, drones, or industrial machines, rather than sending data to cloud servers for processing. It helps to reduce latency by processing data locally, reduce costs by minimizing data transfer to the cloud, and enhance data security and privacy by keeping sensitive information on the device rather than transmitting it to the cloud.

We solve and build custom computer vision applications ready to run in the edge on real time, for multiple problems and sectors, based on state-of-the-art foundation models for image classification, object detection, image segmentation, or even person recognition. For instance, computer vision in agriculture enables intelligent agriculture -like detecting plant diseases- and food production for smart farming applications.

Request assessment for your problem and get a solution ready to be tested on the field, like count and track animals, detect weed seed, recognize people, detect defects, etc.


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