Abstract: A method, a computer system, and a computer program product for a multi-dimension artificial intelligence (AI) agriculture advisor is provided. Embodiments of the present invention may include creating a user profile. Embodiments of the present invention may include preparing and transforming the external data. Embodiments of the present invention may include conducting a hypothesis on the transformed data. Embodiments of the present invention may include validating the transformed data. Embodiments of the present invention may include training an artificial intelligence (AI) model based on the transformed data. Embodiments of the present invention may include validating and retraining the artificial intelligence (AI) model. Embodiments of the present invention may include matching the user data with the artificial intelligence (AI) model. Embodiments of the present invention may include ranking results based on the matching the user data with the artificial intelligence (AI) model.

Abstract: Techniques for agriculture action recommendation are provided. Agricultural information from a plurality of data sources is used to generate a general agriculture data profile and user specific agricultural information is used to generate a user data profile. The general and user specific agricultural information is utilized by an agriculture optimization model to provide an action recommendation and the justification summary for an agriculture product. The action recommendation and the justification summary are based on a plurality of sell and storage options for the at least one agriculture product generated using the agriculture optimization model, where an optimal recommendation is selected by the agriculture optimization model for the agricultural product.

Abstract: A method, a computer system, and a computer program product for a multi-dimension artificial intelligence (AI) agriculture advisor is provided. Embodiments of the present invention may include creating a user profile. Embodiments of the present invention may include preparing and transforming the external data. Embodiments of the present invention may include conducting a hypothesis on the transformed data. Embodiments of the present invention may include validating the transformed data. Embodiments of the present invention may include training an artificial intelligence (AI) model based on the transformed data. Embodiments of the present invention may include validating and retraining the artificial intelligence (AI) model. Embodiments of the present invention may include matching the user data with the artificial intelligence (AI) model. Embodiments of the present invention may include ranking results based on the matching the user data with the artificial intelligence (AI) model.

Abstract: A method, a computer system, and a computer program product for an artificial intelligence (AI) based agribusiness logistics advisor is provided. Embodiments of the present invention may include receiving a first user data. Embodiments of the present invention may include collecting a second user data and external data. Embodiments of the present invention may include preparing and transforming the second user data and the external data. Embodiments of the present invention may include conducting a hypothesis on the transformed data. Embodiments of the present invention may include validating the transformed data. Embodiments of the present invention may include training an artificial intelligence (AI) model based on the transformed data. Embodiments of the present invention may include matching the first user data with the artificial intelligence (AI) model. Embodiments of the present invention may include ranking results based on the matching the first user data with the artificial intelligence (AI) model.

Abstract: A method, computer system, and a computer program product for verifying the consistency of a current medical product is provided. The present invention may include generating a quantity associated with each of one or more active principles in the current medical product based on a plurality of current medical product data and a plurality of information from the data domains. The present invention may then include comparing the generated quantity associated with each of the one or more active principles in the current medical product with one or more constraints associated with a feasible solution associated with the current medical product. The present invention may further include determining a level of counterfeit risk based on the compared quantity associated with each of the one or more active principles in the current medical product with the one or more constraints from the feasible solution for the current medical product.