International Business Machines Corporation

Lucile Ter-Minassian

Komminist S Weldemariam

Ismail Yunus Akhalwaya

Naweed Aghmad Khan

Francois Pierre Luus

Nelson Kibichii Bore

Charles Muchiri Wachira

Juliet K Mutahi

Methods and systems for operating a machine learning system are described. In an example, a device can receive an image and assign a set of pixels in the image as a digital representation of a livestock. The device can further train a machine learning model using the digital representation. The device can further run the machine learning model to generate prediction data relating to the livestock. The device can further generate output data relating to at least one activity among a livestock value chain. The at least one activity can correspond to a process to generate a commodity based on the livestock.

Machine learning models of livestock value chain

In an aspect, a decision platform that optimizes honey value chain can be provided. The decision platform may receive images of a geographic region including catchment areas, run a first machine learning model with the images as input to identify resources in the catchment areas, run a second machine learning model with the identified resources to predict pollen and nectar concentration in the catchment areas, run a third machine learning model with at least the predicted pollen and nectar concentration to predict honey yield in each of the catchment areas, and determine placement of a swarm to at least one of the catchment areas. The decision platform may also control an unmanned aerial vehicle to guide the swarm to at least one of the catchment areas.

Cognitive decision platform for honey value chain

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London, United Kingdom

Lucile Ter-Minassian